基于K均值聚类的光伏电站运行状态模式识别研究

摘要

在阐述光伏电站运行状态模式识别意义的基础上，提取了表征光伏电站运行状态的相关特征参量。基于K-means聚类原理，对广东佛山某光伏电站的实际运行数据进行相关数据处理得到相应的特征矩阵。利用K均值算法进行聚类分析，结果表明K均值聚类算法在光伏电站运行状态的模式识别上具有良好的聚类综合能力，可有效解决光伏电站运行状态模式分类处理的复杂性问题，具有重要的理论和应用价值。%Based on expounding the PV power plant state recognition, this paper extracts the relevant characteristics of the operating state of the PV station. Based on the principle of K-means clustering, through the actual operation data processing of a PV station in the city of Foshan, Guangdong Province, the corresponding feature matrix is obtained. Using K-means clustering analysis, the results show that it has important theoretical and applied value not only because the K-means clustering algorithm has a good effective on the pattern recognition of the PV station, but also can effectively solve the complexity problem of the PV station operation mode classification.