太原理工大学学报2024，Vol.55Issue(1)：66-72,7.DOI:10.16355/j.tyut.1007-9432.20230243
基于VMD分解和随机矩阵理论的异常用电状态检测
Detection of Abnormal Power Consumption State Based on VMD Decomposition and Random Matrix Theory
摘要
Abstract
[Purposes]Users'abnormal power consumption behaviors need to be distinguished quickly and accurately.[Methods]An abnormal state detection model is proposed on the basis of smart meter data and data decomposition and random matrix theory,realizing the identification of users'abnormal power consumption behaviors.The variational mode decomposition(VMD)al-gorithm is used to eliminate the noise of power data and the influence of noise data.The Random Matrix Theory(RMT)is combined with the Auto-Regressive Moving Average Model(ARMA)to improve the applicability of RMT to time series and realize the judgment of abnormal state of electricity consumption.[Findings]Taking the actual power consumption data of a certain area as an example,the method conveniency and efficiency for the case of large data samples and non-Gaussian distribution have been verified,which provides a new direction for the identification of abnormal power consumption behavior.关键词
用户行为/随机矩阵/核密度估计/异常用电/数据分解Key words
user behavior/random matrix/kernel density estimation/abnormal power con-sumption/data decomposition分类
信息技术与安全科学引用本文复制引用
秦志沁,韩玉环,张毅,郭志军,许英玮,金泽璇..基于VMD分解和随机矩阵理论的异常用电状态检测[J].太原理工大学学报,2024,55(1):66-72,7.基金项目
国网山西省电力公司科技项目(5205E0220003) （5205E0220003）
