一种相关性与聚类自适应融合技术窃电检测方法

An Electricity Theft Detection Method with Correlation and Clustering Fusion Technique

篡改电表数据是一种典型的窃电行为。针对此类窃电行为,现有的检测方法需要标记好的数据集或额外的电力系统状态信息,这在现实中很难获得或即使获得也与实际值存在较大误差。因此,利用较低维度的数据来实现对此类窃电行为进行检测的方法亟待深入研究。创新性地结合最大互信息系数(maximum information coefficient,MIC)技术和基于密度峰值的快速聚类算法提出了一种新的融合检测方法。该方法利用最大互信息系数度量管理线损与用户特定行为之间的相关性,采用CFSFDP定位异常用电用户,适用性强,能够检测多种不同类型的窃电行为。最后利用爱尔兰智能电表数据集进行了算法验证,结果证明了该方法的良好性能。

Tampering with the data of electricity meters is a typical electricity theft.For such electricity theft behaviors,the existing detection methods require a labeled data set or additional power system state information,which is difficult to obtain or has a large error with the actual value.Therefore,it is urgent to utilize the lower dimension data to realize the detection of electricity theft behavior.In this paper,a novel fusion detection method is proposed by combining the maximum information coefficient(MIC)technique and the clustering by fast search and find of density peaks.This method uses MIC to measure the correlation between management line loss and specific behaviors of consumers and uses CFSFDP to locate abnormal electricity consumers with high applicability,which can detect various types of electricity theft.This paper also uses the Irish smart meter data set to verify the algorithm,and the good performance of the proposed method is proved by the result.