Dynamic Equivalent Model of Two‐Staged Distributed Photovoltaic Cluster Based on Multiple Clustering Algorithms

Aiming at the modeling problem of bipolar distributed photovoltaic (DPV) cluster, this paper proposes a clustering equivalent modeling method based on clustering algorithm. First, by analyzing the detailed model of bipolar DPV, it is found that the indexes that can reflect its steady‐state and dynamic characteristics mainly include energy storage element parameters such as inductance and capacitance and PI control parameters. Then, combined with the five commonly used clustering algorithms, the clusters composed of ten DPVs are clustered and grouped. Finally, the dynamic simplified model of DPV cluster is obtained by parameter aggregation and model equivalence of DPV in the same group. The above analysis is simulated and verified on the IEEE33 node system containing 10 DPVs, and the traditional single‐machine equivalent model and double‐machine equivalent model and clustering model are added for comparative analysis. The simulation results show that the clustering equivalent model can correctly reflect the dynamic response characteristics of DPV clusters under different working conditions. The error between each clustering model and the detailed model is not more than 10%. Among them, the fuzzy C ‐mean (FCM) clustering model has the best effect, the minimum error is 0.11%, and the maximum error of the single machine equivalent model is 9.3%.