基于负荷尖峰特征LSTM自编码器的窃电识别方法  

Identification Method of Electricity Theft Based on Long Short-term Memory Autoencoder with Load Peak Features

作　　者：刘康[1] 刘鑫 张蓬鹤 薛阳 李彬[1,4] 苏盛 LIU Kang;LIU Xin;ZHANG Penghe;XUE Yang;LI Bin;SU Sheng(Hunan Key Laboratory of Smart Grid Operation and Control(Changsha University of Science and Technology),Changsha 410114,China;Marketing Service Center of State Grid Hunan Electric Power Company(Metering Center),Changsha 410114,China;China Electric Power Research Institute Co.,Ltd.,Beijing 100180,China;School of Mechanical and Electrical Engineering,Hunan City University,Yiyang 413000,China)

机构地区：[1]智能电网运行与控制湖南省重点实验室(长沙理工大学),湖南省长沙市410114 [2]国网湖南省供电服务中心(计量中心),湖南省长沙市410114 [3]中国电力科学研究院有限公司,北京市100180 [4]湖南城市学院机械与电气工程学院,湖南省益阳市413000

出　　处：《电力系统自动化》2023年第2期96-104,共9页Automation of Electric Power Systems

基　　金：国家自然科学基金资助项目(51777015);湖南省教育厅重点项目(19A011);湖南省教育厅科研项目(19C0349)。

摘　　要：近年来,面向高损线路的窃电检测方法得到大面积工程应用,对降低窃电检测误报率和推动数据驱动窃电检测的工程应用起到了重要作用。但如何准确检出非高损线路的专变窃电用户,仍是亟待解决的难题。基于实践经验中部分窃电用户存在用电量异常尖峰这一特点,提出基于负荷尖峰特征长短期记忆(LSTM)自编码器的用户窃电识别方法。首先,分析典型窃电用户曲线形态,提炼了区分正常及窃电用户的用电量尖峰特征。然后,结合该特征和用户分时数据周期性规律,构建LSTM自编码模型重构输入得到拟合值,基于拟合值与真实值的均方误差设定自适应阈值,从而识别窃电嫌疑用户并提供具体预警尖峰时段。最后,应用实际专变用户用电数据进行算例分析,结果表明所提方法在准确率、命中率和误报率上均优于对比方法。In recent years, electricity theft detection methods for high-loss lines have been applied in a large area of engineering,which plays an important role in reducing the false alarm rate of electricity theft detection and promoting the engineering application of data-driven electricity theft detection. However, it is still a difficult problem to accurately detect the special transformer users of non-high-loss lines. Based on the characteristic that some users who steal electricity have abnormal power consumption peaks in the practical experience, a method of identifying users with electricity theft actions based on long short-term memory(LSTM)autoencoder(AE)with load peak features is proposed. First, the peak characteristics of power consumption that distinguish normal users and users with electricity theft actions are extracted by analyzing the curves of typical users with electricity theft actions.Then, the LSTM-AE model is constructed to reconstruct the input and obtain the fitting value by combining this feature and the periodic law of user time-sharing data. The adaptive threshold is set based on the mean square error between the fitting value and the real value, so as to identify the suspected users of electricity theft and provide specific warning peak time. Finally, an example is used to analyze the actual power consumption data of special transformer users. The results show that the proposed method is superior to the comparison method in terms of accuracy, hit rate and false alarm rate.

关 键 词：窃电 长短期记忆 神经网络 自编码器 用电特征 异常检测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM73[自动化与计算机技术—控制科学与工程]