Study on Typical Output Scenario Characteristics of Photovoltaic Power Station Based on Improved FCM Clustering
In order to improve the operation safety and reliability of the new power system,it is necessary to quantitatively evaluate the output characteristics of the photovoltaic(PV)power station,and master the operation law of the PV power station from many power generation scenarios with high uncertainty.Therefore,this paper analyzes the PV output based on the theory of scene clustering and reduction.Firstly,it proposes the evaluation indexes of photovoltaic output characteristics,including two primary indexes,volatility and output efficiency,and corresponding secondary indexes.Then it uses the fuzzy C-means(FCM)clustering algorithm to determine the initial cluster center based on the density idea and the max-min principle of distance,and the clustering results of PV output in different scenarios are obtained through continuous iterations.Considering local convergence of traditional clustering algorithms,it is difficult to determine the optimal number of clusters,the paper proposes the clustering efficiency index to determine the optimal numbers of clusters,and then uses the the forward push back method based on probabilistic distance for clustering result reduction.Finally,the typical seasonal output scenarios of PV power stations are obtained.Through the analysis of the actual output data of a PV power station in Guangdong province,the effectiveness of the proposed index and algorithm is verified.
PV output scenarioclustering algorithmscene reductionFCM algorithmclustering index