摘要
针对工业用户的行业属性对其用电模式的影响,本文提出一种考虑行业关联度的工业用户用电异常识别方法。基于真实工业用户用电负荷数据生成多个行业类别的典型负荷特征曲线。运用改进灰色关联度算法计算电力用户用电特征与各个行业典型用电特征之间的关联性,生成用户的行业关联特征;利用多头注意力机制(MHA)提取用户负荷序列特征,与行业关联特征相结合,采用变分自动编码器(VAE)所提供的重构误差作为异常判定度量,建立MHA-VAE深度异常检测模型,实现对多种类型工业用户用电异常的识别。结果表明,引入用户的行业关联特征后异常检测的准确率、检出率和误检率分别为96.84%、98.02%、4.35%,与仅考虑用户负荷特征相比准确率提高1.06%,误检率降低2.24%。
In view of the influence of industrial users′industry attributes on their power consumption patterns,a power consumption anomaly identification method considering industry relevance is proposed in this article.Based on the real industrial consumer power consumption data,the typical load characteristic curves of each industry are generated,and the improved grey correlation degree algorithm is used to calculate the relevance between the power consumption characteristics of power users and the typical power consumption characteristics of the industry.In this way,the industry relevance characteristics of users are achieved.The multi-head attention(MHA)is used to extract the features contained in load sequences.Combined with the industry relevance features,the reconstruction error provided by the variational autoencoder(VAE)is used as the anomaly decision metric to formulate the MHA-VAE depth anomaly detection model to identify various types of industrial users′power consumption anomalies.Results show that,the accuracy,detection rate and false detection rate after introducing users′industry relevance are 96.84%,98.02%,and 4.35%,respectively.Compared with only considering the load characteristics of users,the accuracy is increased by 1.06%and the error detection rate is reduced by 2.24%.
作者
陈静
郑垂锭
李桂敏
江灏
缪希仁
Chen Jing;Zheng Chuiding;Li Guimin;Jiang Hao;Miao Xiren(College of Electrical Engineering and Automation,Fuzhou University,Fuzhou 350108,China)
出处
《仪器仪表学报》
EI
CAS
CSCD
北大核心
2023年第4期72-81,共10页
Chinese Journal of Scientific Instrument
基金
福建省自然科学基金(2022J01566)项目资助。
关键词
用电异常识别
多头注意力机制
变分自动编码器
行业关联度
power consumption anomaly identification
multi-head attention
variational auto-encoder
industry relevance
作者简介
陈静,分别于2013年和2016年于厦门大学获得硕士和博士学位,现为福州大学副教授,主要研究方向为电力大数据和智能电网。E-mail:chenj@fzu.edu.cn;通信作者:江灏,分别于2011年和2013年于厦门大学获得硕士和博士学位,现为福州大学副教授,主要研究方向为电力故障辨识和智能感知系统。E-mail:jiangh@fzu.edu.cn。
