一种基于蒙特卡洛T方检验的变电站模糊聚类方法

A Novel Method of Fuzzy Clustering for Substation Classification Based on Monte Carlo T-statistic Inspection

电力系统负荷建模中的负荷分类是一项重要工作。文中分析各典型变电站日负荷曲线,从中选取用于变电站负荷分类的特征数据。针对聚类过程中无法判断聚类类别数的合理性以及缺乏检验方法判断模糊聚类结果有效性的问题,提出一种基于蒙特卡洛T方统计检验的模糊聚类方法,以指导聚类过程、检验聚类结果。最后运用以上方法对某省网72座220 kV变电站进行聚类分析,验证了该方法的有效性。

Load classification is of great importance in load modeling. It is well known that it＇s difficult to judge the rationality of the type of a load substation through real time data and to check the validity and accuracy of the clustering results for lack of a checking method, The daily load data from EMS/SCADA is employed and its relative parameters are chosen as eigenvectors to classify the substation. A hypothesis proo^test based on formula T and Monte Carlo method are presented to guide the fuzzy clustering process and inspect the correctness of the clustering results. A case study shows the validity of the novel method through its application in a local power system.