Mode Clustering Based Dynamic Equivalent Modeling of Wind Farm for Small-Signal Stability Analysis

Abstract: Dynamic equivalent models (DEMs) are necessities for the small-signal stability (SSS) analysis of the power system with large-scale wind farms (WFs). This paper proposes a mode clustering based dynamic equivalent modeling method of WFs for the SSS analysis. It is deemed that a DEM can be used to represent the whole WF to evaluate its impact on the SSS of power systems, as long as the frequency response of the DEM adequately matches that of the detailed WF model around the frequency of oscillation modes of concern. Focusing on the concerned oscillation modes in the small-signal model of the whole WF, closely distributed modes of them on the complex plane are classified into a cluster and then represented by a single mode. By the linear superposition principle, the modal participation factor (MPF) regarding each of modal clusters are superimposed, generating a feature vector for each wind turbine (WT). Based on the feature vectors, the oscillation feature similarity of the WTs as well as their aggregation can be evaluated and performed easily. Taking the DC-link voltage control (DVC) mode of full-power WT converters as an example, the aggregated DEM is verified by frequency-domain analyses and time-domain simulations.