Two‐Step Clustering‐Based Equivalent Modeling of Large‐Scale Wind Farms for Enhanced Primary Frequency Regulation

ABSTRACT The focus on wind power frequency regulation has largely been on individual wind farms, which presents challenges in meeting the coordinated operation needs of multiple stations within large‐scale installations. To address issues of model simplification and accuracy, we propose a large‐scale wind farm equivalent modeling method based on dynamic grouping and parameter aggregation. Initially, principal component analysis was utilized to identify key variables that characterize the dynamic attributes of turbines, completing an initial grouping. This strategy was further refined using the wind energy utilization index and optimized through system clustering methods, resulting in a multimachine equivalent model. This two‐step grouping mechanism overcomes the limitations of traditional single‐variable approaches and reduces the computational complexity associated with high‐dimensional models. RTLAB simulations demonstrate that the proposed aggregated equivalent method significantly enhances performance in frequency regulation scenarios. Under steady‐state conditions, the improved model's accuracy surpasses that of stand‐alone and traditional models by 96.94% and 81.79%, respectively. Even under random wind disturbances, accuracy improvements reach 94.51% and 68.78%. These advancements in model response accuracy provide refined support for coordinated frequency regulation of wind power clusters, maximizing the frequency regulation potential of large‐scale wind farms. The improved model reduces drop rates by 10.34% and 0.61% in the initial drop stage and by 16.36% and 3.28% at the lowest point stage, while steady‐state power backup control lowers frequency deviation by 11.52% and 6.01%, significantly enhancing overall system performance.