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.
