The penetration rate of photovoltaic power generation keeps increasing.To address the issues of poor clus-ter partitioning and lengthy processing times for photovoltaic power station clusters,the paper proposes a method for rapid cluster partitioning method for photovoltaic(PV)plants based on prototype extraction and clustering.Firstly,photovoltaic data is preprocessed to eliminate differences in magnitude and dimensionality among data sets.Subse-quently,influential factors on photovoltaic output power are identified using the Pearson correlation coefficient method.Random sampling,k-means++,and an improved spectral clustering method are then employed for sam-pling,prototype extraction,and prototype clustering of PV plants,respectively.Building upon an enumeration ap-proach and hierarchical optimization,optimal hyperparameters for the aforementioned processes are determined.Fi-nally,various scenarios are set up for case study comparisons,calculating both intra-cluster and inter-cluster indi-cators as well as clustering time metrics.Through comprehensive analysis,the effectiveness of the proposed method in addressing the rapid clustering for large-scale PV plants is validated.
photovoltaic power stationimproved spectral clustering algorithmprototype clusteringPearson corre-lation coefficient
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