Physics-Informed LLM-Agent for Automated Modulation Design in Power Electronics Systems

Abstract: LLM-based autonomous agents have demonstrated outstanding performance in solving complex industrial tasks. However, in the pursuit of carbon neutrality and high-performance renewable energy systems, existing AI-assisted design automation faces significant limitations in explainability, scalability, and usability. To address these challenges, we propose LP-COMDA, an LLM-based, physics-informed autonomous agent that automates the modulation design of power converters in Power Electronics Systems with minimal human supervision. Unlike traditional AI-assisted approaches, LP-COMDA contains an LLM-based planner that gathers and validates design specifications through a user-friendly chat interface. The planner then coordinates with physics-informed design and optimization tools to iteratively generate and refine modulation designs autonomously. Through the chat interface, LP-COMDA provides an explainable design process, presenting explanations and charts. Experiments show that LP-COMDA outperforms all baseline methods, achieving a 63.2% reduction in error compared to the second-best benchmark method in terms of standard mean absolute error. Furthermore, empirical studies with 20 experts conclude that design time with LP-COMDA is over 33 times faster than conventional methods, showing its significant improvement on design efficiency over the current processes.

• The paper presents LP-COMDA, integrating two physics-informed neural networks to capture both switch-level and circuit-level dynamics.
• It achieves a 63.2% reduction in mean absolute error under low-data conditions and 23.7% improvement with abundant data.
• The system accelerates modulation design by over 33× compared to traditional methods, enhancing renewable energy integration.

Physics-Informed LLM-Agent for Automated Modulation Design in Power Electronics Systems

In the context of increasing global temperatures and the critical imperative to achieve carbon neutrality, enhancing the efficiency and efficacy of power electronics systems (PES) becomes paramount. The integration of renewable energy resources (RES) into modern power grids heavily relies on sophisticated modulation design for power converters within PES. The paper "Physics-Informed LLM-Agent for Automated Modulation Design in Power Electronics Systems" tackles this exigency by introducing LP-COMDA, a LLM-based, physics-informed autonomous agent aimed at optimizing modulation design with minimal human intervention.

Overview and Methodology

The LP-COMDA system diverges from traditional AI-assisted design methods, which often suffer from data-intensive requirements, computational inefficiencies, and limited scalability. The proposed system employs a LLM-based planner to gather user requirements through an interactive chat interface. This facilitator coordinates with tailored design tools rooted in physics-informed neural networks (PINNs) to autonomously refine modulation designs. The integration of explainability in this process addresses a significant limitation in existing AI models, offering users complete transparency through visual explanations and interactive feedback.

The core contribution of LP-COMDA lies in its hierarchical approach to surrogate modeling via two physics-informed neural networks: ModNet and CirNet. ModNet operates at the switch level, capturing the behavior of semiconductor switches, while CirNet functions at the system level, encapsulating detailed circuit physics. This framework allows the system to achieve precise modeling of complex power converter dynamics with reduced data requirements.

Experimental Validation

The paper provides robust empirical results, demonstrating that LP-COMDA significantly outperforms traditional machine learning and modern deep learning benchmarks. With a remarkable 63.2% reduction in mean absolute error compared to the next best-performing method in low-data scenarios, LP-COMDA's efficacy in modeling accuracy is established. The system also exhibits consistent performance improvements, offering 23.7% lower errors in higher data availability settings. Such findings underscore the advantage of incorporating physics-informed components in LLM-based systems for complex engineering tasks.

Practical and Theoretical Implications

The practical implications of this research are profound. By drastically reducing the design time of modulation strategies—over 33 times faster than conventional methods—LP-COMDA directly addresses the industrial need for rapid and accurate power electronics design automation. This efficiency can drastically reduce resource consumption by streamlining the design process, making it highly relevant for deployment in renewable energy integration.

Theoretically, LP-COMDA exemplifies the successful integration of physics-based modeling techniques within LLM frameworks. This advancement opens various avenues for future research, particularly in enhancing other domains of engineering and science where fine-grained modeling can benefit greatly from LLM-based interactions and decision-support systems.

Conclusion and Future Directions

The development of LP-COMDA marks a substantial step forward in automating complex PES modulation design. Its approach presents a vital synergy between machine learning, physics, and human interface systems. Future research could explore extending the LP-COMDA system's applicability to other types of converters and more complex energy systems. Additionally, expanding the toolkit of optimization and modeling techniques could further augment LLM-based agent systems' capacity to address multifaceted industrial challenges across various sectors. Overall, LP-COMDA sets a precedent for the integration of LLMs and PINNs in high-stakes, real-world applications, paving the way for more expansive and nuanced approaches within the field of intelligent design automation.