Equivalent Modeling of Photovoltaic Power Station Based on Canopy-FCM Clustering Algorithm

Abstract

Grid-connected photovoltaic power generation has become an effective way to utilize solar energy. In order to accurately analyze the dynamic characteristics of a grid-connected photovoltaic power station, an equivalent modeling method based on the Canopy-FCM clustering algorithm is proposed. Considering the lack of theoretical basis for the selection of the traditional clustering index, this paper deduces the transfer function of the grid-connected inverter control system based on the detailed model and proposes a new clustering index, which takes the zero-pole expression of the closed-loop transfer function as the prototype. In order to improve the fuzzy C-means clustering method, which is sensitive to the initial clustering centers and the outliers, a Canopy algorithm with lower computation cost is introduced. Before fuzzy C-means clustering analysis, the Canopy algorithm is used to determine the initial clustering centers and the number of clusters to improve the accuracy and efficiency of the clustering algorithm. The effectiveness of the proposed method is verified by simulation examples.