基于多元聚类模型与两阶段聚类修正算法的变电站特性分析  

Analysis of Substation Characteristics Based on Multivariate Clustering Model and Two-stage Clustering-correction Algorithm

作　　者：蒋正邦 吴浩[1] 程祥 孙维真[3] 商佳宜 JIANG Zhengbang;WU Hao;CHENG Xiang;SUN Weizhen;SHANG Jiayi(College of Electrical Engineering,Zhejiang University,Hangzhou 310027,China;Hangzhou Power Supply Company of State Grid Zhejiang Electric Power Company,Hangzhou 310011,China;Zhejiang Electric Power Dispatch and Communication Center,Hangzhou 310007,China)

机构地区：[1]浙江大学电气工程学院,浙江省杭州市310027 [2]国网浙江省电力有限公司杭州供电公司,浙江省杭州市310011 [3]浙江电力调度通信中心,浙江省杭州市310007

出　　处：《电力系统自动化》2018年第15期157-163,共7页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(51377143);国家电网公司科技项目(52110415000B)~~

摘　　要：变电站负荷包含多种用户负荷,其特性非常复杂,选择单一的日负荷曲线或是用户构成比例作为指标进行聚类,可能忽略其他因素并导致聚类结果不够全面。由此提出了同时考虑变电站日负荷曲线与变电站用户构成的多元聚类模型。为求解该模型,首先对日负荷曲线数据采用Kmeans算法进行聚类。然后,提出一种两阶段聚类修正算法,用于依照变电站用户构成数据修正日负荷曲线聚类结果。研究结果表明,所提方法所得的聚类结果准确度高,可降低聚类结果跌入局部最优的可能性,且所得结果能明确体现各个变电站在日负荷曲线上及用户构成上的差异。Substation load consists of a variety of user load,whose characteristics are very complex.Singly choosing daily load curve or the proportion of users as indicators for clustering may cause ignorance of other factors and lead to inaccuracy results.Therefore,in this paper,a multivariate clustering model is proposed,which takes both daily load curve and the users of substations into account.To solve the model,K-means algorithm is used to analyze the daily load curve data,then a two-stage clustering-correction algorithm is put forward to correct the last clustering results according to proportion of users in substations.In conclusion,the clustering result after correction achieves high accuracy,which can be effective to prevent the result from falling into the local optimum and clearly reflect the difference of both load curve and user proportion among substations.

关 键 词：变电站聚类 多元聚类模型 用户构成 日负荷曲线 

分 类 号：TM63[电气工程—电力系统及自动化]