基于数据挖掘的新型低压窃电识别方法

针对现今反窃电技术往往采用单一算法分析,导致反窃电效果差强人意的现状,文中提出一种针对低压用户窃电的识别方法.剥离台区线损当中的技术线损部分,采用K-means聚类算法对处理过的线损数据进行分析,识别出线损率异常波动或持续偏高的台区,并根据聚类结果定义时间离散度来衡量窃电疑似度.分析异常台区下的用户,通过相关性分析研究...

A Novel Judgment Method to Uncover Low Voltage Electricity Theft Based on Data Mining

In view of the current situation that anti-theft technology is often analyzed by a single algorithm,which results in unsatisfactory anti-theft effect,a recognition method for low-voltage anti-theft users is proposed in this paper.Firstly,the technical line loss part of the line loss in the station area is separated.Then,K-means clustering algorithm is adopted to analyze the processed line loss data to identify the station area where the line loss rate fluctuates abnormally or is continuously high,and defines the time dispersion according to the clustering result to measure the suspected degree of electricity theft.Then,it analyzes the users under the abnormal station area,and studies the possible relationship between the change of electricity quantity of single users and the change of line loss rate in the station area through the correlation analysis.The outlier algorithm and K-means clustering algorithm are used to analyze the daily electricity consumption data of users,judge the suspected electricity theft of a single user,and determine the specific electricity theft behavior.The research results show that this method can identify the electricity theft of low-voltage users more effectively,which provides a new way for electricity theft identification and remediation.