基于改进K-means聚类和皮尔逊相关系数户变关系异常诊断

周纲; 黄瑞; 刘度度; 张芝敏; 胡军华; 高云鹏

周纲,黄瑞,刘度度,张芝敏,胡军华,高云鹏.基于改进K-means聚类和皮尔逊相关系数户变关系异常诊断[J].电测与仪表,2024,61(3):76-82，152.
ZHOU Gang,HUANG Rui,LIU Dudu,ZHANG Zhimin,HU Junhua,GAO Yunpeng.Abnormal diagnosis of household variable relationship based on improved K-means clustering and Pearson correlation coefficient[J].Electrical Measurement & Instrumentation,2024,61(3):76-82，152.

基于改进K-means聚类和皮尔逊相关系数户变关系异常诊断

Abnormal diagnosis of household variable relationship based on improved K-means clustering and Pearson correlation coefficient

DOI：10.19753/j.issn1001-1390.2024.03.011

中文关键词: 户变关系 GIS系统主成分分析改进K-means聚类改进皮尔逊相关系数

英文关键词:household variable relationship, GIS system, principal component analysis, improved K-means clustering

基金项目:国家电网有限公司科技项目(5216A019000S)；国家自然科学基金（51777061）

作者中文名	作者英文名	单位
周纲	ZHOU Gang	国网湖南省电力有限公司；智能电气量测与应用技术湖南省重点实验室
黄瑞	HUANG Rui	国网湖南省电力有限公司；智能电气量测与应用技术湖南省重点实验室
刘度度	LIU Dudu	国网湖南省电力有限公司；智能电气量测与应用技术湖南省重点实验室
张芝敏	ZHANG Zhimin	湖南大学；智能电气量测与应用技术湖南省重点实验室
胡军华	HU Junhua	国网湖南省电力有限公司；智能电气量测与应用技术湖南省重点实验室
高云鹏	GAO Yunpeng	湖南大学；智能电气量测与应用技术湖南省重点实验室

摘要点击次数: 758

中文摘要:

用电信息采集系统易出现台区户变关系错误问题，传统诊断技术主要针对少用户台区出现异常用户情况，但对于多达数百用户台区，存在多相邻台区异常用户特征提取难题。文中首先通过主成分分析对GIS系统获取台区总表和用户电表电压数据实现降维，建立改进K-means聚类提取电压数据特征，提出改进皮尔逊相关系数算法分析待检测用户，据此建立基于改进K-means聚类和改进皮尔逊相关系数的户变关系异常诊断方法，实现多异常用户所属正确台区诊断。实际算例分析结果表明，文中提出算法在识别同一台区一个及多个异常用户、不同台区多个异常用户情况下均能有效实现异常用户的准确检测与分析，相比传统检测方法，实现简单且准确性更高。

英文摘要:

The electricity information acquisition system is prone to errors in the relationship between households in the stations. Traditional diagnostic techniques are mainly aimed at abnormal users in the few stations, but for hundreds of users, there is a difficult problem of extracting the characteristics of abnormal users in multiple adjacent stations. This paper firstly reduces dimension through the principal component analysis of GIS system for area total table and voltage meter data, sets up improved K-means clustering to extract voltage data characteristics, the improved Pearson correlation coefficient algorithm is proposed to analyze the users to be detected, accordingly, the abnormal diagnosis method of household variable relationship based on improved K-means clustering and Pearson correlation coefficient is established to realize the correct diagnosis for multiple abnormal users. The analysis results of practical examples show that the algorithm proposed in this paper can effectively realize the accurate detection and analysis of abnormal users in the case of identifying one or more abnormal users in the same station and multiple abnormal users in different stations. Compared with the traditional detection method, the implementation is simple and more accurate.

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