基于传递熵密度聚类的用户窃电识别方法  

User Electric Theft Detection Method Based On Transfer Entropy Density Clustering

作　　者：刘康 李彬[1] 薛阳 杨艺宁 徐英辉 刘爱国 苏盛[1] LIU Kang;LI Bin;XUE Yang;YANG Yining;XU Yinghui;LIU Aiguo;SU Sheng(College of Electrical and Information Engineering,Changsha University of Science and Technology,Changsha 410114,Hunan Province,China;China Electric Power Research Institute Co.,Ltd.,Haidian District,Beijing 100192,China;Ningdong Power Supply Company of State Grid Ningxia Electric Power Co.,Ltd.,Ningwu 751401,Ningxia Hui Autonomous Region,China)

机构地区：[1]长沙理工大学电气与信息工程学院,湖南省长沙市410114 [2]中国电力科学研究院有限公司,北京市海淀区100192 [3]国网宁夏电力有限公司宁东供电公司,宁夏回族自治区宁武市751401

出　　处：《中国电机工程学报》2022年第20期7535-7545,共11页Proceedings of the CSEE

基　　金：国家自然科学基金项目(51777015);国家电网公司总部科技项目(反窃电及稽查监控关键技术研究);湖南省自然科学基金项目(2020JJ4611)。

摘　　要：在配电线路/台区中,接入用户的用电量与线损电量间存在因果关系,正常用户电量变化对线损电量的影响有限,而窃电用户的用电量对线损电量的影响异于正常用户。传递熵能衡量变量间的信息传递,是评价因果性的重要指标。该文提出基于传递熵密度聚类的用户窃电识别方法。首先运用传递熵指向性筛选出对线路/台区线损电量因果关联较强的用户;然后构建其与线损电量的传递熵模型,计算不同时长的用户用电量对线损电量的传递熵值,以衡量其信息传递量;再结合密度聚类算法,将传递熵曲线偏离正常用户类簇的识别为与线损有强因果性的窃电用户。最后,基于已查证的高损台区和长距离配电线路实际数据,验证所提方法的有效性。In distribution lines/stations,there is a causal relationship between the electricity consumption of access users and the line loss.The change of the normal users’electricity quantity has a limited influence on the line loss;while the users’power theft will make the influence of the electricity consumption on the line loss different from that of the normal users.Transfer entropy can measure the information transfer between variables,and it is an important index to evaluate causality.This paper proposed a method to identify electricity stealing users based on transfer entropy density clustering.Firstly,the users with strong causal correlation to line loss electric quantity in line/station areas were selected by using transmission entropy information directivity.Then,the transfer entropy model between electricity consumption and line loss quantity was constructed to calculate the transfer entropy value of electricity consumption of different time length to line loss quantity to measure its information transfer quantity.Combined with the density clustering algorithm,the users whose transfer entropy curve deviates from the normal user cluster were identified as those who steal electricity with strong causality to line loss.Finally,the effectiveness of the proposed method was proved based on the verified data of high-loss stations and high-loss long-distance distribution lines.

关 键 词：窃电 传递熵 基于密度的噪声应用空间聚类 因果关联 线损电量 

分 类 号：TM93[电气工程—电力电子与电力传动]