一种模糊多粒度用电行为异常检测方法  

Fuzzy multi-granularity-based abnormal power consumption detection method

作　　者：李琪林[1] 严平[1] 陈白杨 袁钟 彭德中 刘益志 Li Qilin;Yan Ping;Chen Baiyang;Yuan Zhong;Peng Dezhong;Liu Yizhi(Metering Center of State Grid Sichuan Electric Power Corporation,Chengdu 610045,China;College of Computer Science,Sichuan University,Chengdu 610065,China)

机构地区：[1]国网四川省电力公司计量中心,成都610045 [2]四川大学计算机学院,成都610065

出　　处：《计算机应用研究》2023年第11期3348-3352,3357,共6页Application Research of Computers

基　　金：国网四川省电力公司科技项目(521997230015)。

摘　　要：异常用电检测旨在识别和定位电力系统中与常规用电行为显著偏离的用户。现有基于机器学习、深度学习的有监督的检测方法通常需要大量人工标记数据,且对于离散型数据需要进行类型转换,因而容易导致重要信息的丢失。模糊粗糙集理论提供了一种处理离散数据的有效工具,并能直接应用于包含连续数据和离散数据在内的异质信息的知识分类。在文本模糊粗糙集理论的基础上,提出了一个基于多粒度模糊相对差的无监督异常检测方法,并将其应用于智能电网的异常用电检测。具体而言,首先利用模糊近似空间的信息熵来度量属性在知识分类中的重要性,然后根据属性集重要性构造模糊信息粒序列,接着在此序列上定义样本的模糊相对差,最后构建基于多粒度模糊相对差的异常检测方法,并在公开的数据集上进行验证。实验结果表明了所提检测算法的有效性和优越性。实验相关的代码和数据已在网络上公开(http://www.github.com/chenbaiyang/FRAD)。Abnormal power consumption detection aims to identify and locate customers in the power system that deviate significantly from regular power consumption behavior.Existing supervised detection methods based on machine learning or deep learning generally require a large amount of manually labeled data,and require transformation for discrete data,thus leading to the loss of important information.FRS theory provides an effective tool for tackling discrete data.Therefore,FRS can be directly applied to the knowledge classification of heterogeneous information that includes continuous and discrete data.This paper proposed an unsupervised anomaly detection method with multi-granularity fuzzy relative differences based on FRS theory,and applied it to detect anomalous power consumption users in smart grid.Specifically,it first used information entropy of fuzzy approximation space to measure the importance of attributes for knowledge classification,then constructed a fuzzy granule sequence based on the attribute set’s importance,and defined the fuzzy relative difference of the samples on top of this sequence.Finally,it constructed the anomaly detection method based on multi-granularity fuzzy relative differences and conducted evaluation on public datasets.Experimental results demonstrate the effectiveness and efficiency of the proposed algorithm.The code and data for the experiments are publicly available online(http://www.github.com/chenbaiyang/FRAD).

关 键 词：无监督异常用电检测 异质信息 模糊粗糙集 模糊相对差 模糊多粒度 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]