基于改进K-means聚类和皮尔逊相关系数户变关系异常诊断

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。本文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，本文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。

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

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 problem of extracting the characteristics of abnormal users in multiple adjacent stations.This article first through the principal component analysis of GIS system for area total table and voltage meter data dimension reduction, set up to improve voltage data extracted K - means clustering characteristics, improve the Pearson correlation coefficient algorithm analysis of the user to be detected, accordingly based on improved K means clustering and Pearson correlation coefficient between abnormal change diagnosis method, realize the abnormal area user belongs to correct diagnosis.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 platform and multiple abnormal users in different platforms. Compared with the traditional detection method, the implementation is simple and more accurate.