全球能源互联网（英文）2022，Vol.5Issue(1)：108-117,10.DOI:10.14171/j.2096-5117.gei.2022.01.009
基于用户负荷形状词典的自适应窃电检测方法
Adaptive electricity theft detection method based on load shape dictionary of customers
摘要
Abstract
With the application of the advanced measurement infrastructure in power grids, data driven electricity theft detection methods become the primary stream for pinpointing electricity thieves. However, owing to anomaly submergence, which shows that the usage patterns of electricity thieves may not always deviate from those of normal users, the performance of the existing usage-pattern-based method could be affected. In addition, the detection results of some unsupervised learning algorithm models are abnormal degrees rather than "0-1" to ascertain whether electricity theft has occurred. The detection with fixed threshold value may lead to deviation and would not be sufficiently flexible to handle the detection for different scenes and users. To address these issues, this study proposes a new electricity theft detection method based on load shape dictionary of users. A corresponding strategy for tunable threshold is proposed to optimize the detection effect of electricity theft, and the efficacy and applicability of the proposed adaptive electricity theft detection method were verified from numerical experiments.关键词
窃电检测/K-means/负荷形状词典/数据挖掘Key words
Electricity theft detection/K-means/Load shape dictionary/Data mining引用本文复制引用
闫春江,马锋,聂卫刚,韩晓昆,海晓涛,许越杰,彭彦林..基于用户负荷形状词典的自适应窃电检测方法[J].全球能源互联网（英文）,2022,5(1):108-117,10.基金项目
This work was supported by the National Natural Science Foundation of China(U1766210). （U1766210）
