Feature Extraction Based Electricity Theft Detection for Edge Data Center

Yufan ZHANG; Qian AI; Zhaoyu LI; Fei XIAO; Yuze RAO

doi:10.7500/AEPS20190624003

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Feature Extraction Based Electricity Theft Detection for Edge Data Center

Application Research | 更新时间：2025-06-16

- Feature Extraction Based Electricity Theft Detection for Edge Data Center
- Automation of Electric Power Systems Vol. 44, Issue 9, Pages: 128-134(2020)
- 作者机构：
  
  1.电力传输与功率变换控制教育部重点实验室（上海交通大学），上海市 200240
  2.北京交通大学电气工程学院，北京市 100044
  3.国网湖北省电力有限公司电力科学研究院，湖北省武汉市 430077
- 作者简介：
- 基金信息：
  
  National Natural Science Foundation of China-State Grid Joint Fund for Smart Grid
- DOI：10.7500/AEPS20190624003
  CLC：
- Received：24 June 2019，
  
  Revised：2019-10-18，
  
  Published Online：24 March 2020，
  
  Published：10 May 2020
- 稿件说明：
移动端阅览
张宇帆,艾芊,李昭昱等.基于特征提取的面向边缘数据中心的窃电监测[J].电力系统自动化,2020,44(9):128-134.

ZHANG Yufan,AI Qian,LI Zhaoyu,et al.Feature Extraction Based Electricity Theft Detection for Edge Data Center[J].Automation of Electric Power Systems,2020,44(9):128-134.
张宇帆,艾芊,李昭昱等.基于特征提取的面向边缘数据中心的窃电监测[J].电力系统自动化,2020,44(9):128-134. DOI： 10.7500/AEPS20190624003.

ZHANG Yufan,AI Qian,LI Zhaoyu,et al.Feature Extraction Based Electricity Theft Detection for Edge Data Center[J].Automation of Electric Power Systems,2020,44(9):128-134. DOI： 10.7500/AEPS20190624003.

摘要

随着电网信息物理系统的发展，一部分数据处理功能逐渐下沉到靠近终端用户的边缘层。为了给后续分析提供可靠的数据源，及时发现异常用电行为，窃电监测是边缘数据中心重要功能之一。文中提出一种针对边缘数据中心的窃电监测方法，该方法利用深度卷积生成对抗网络(DCGAN)鉴别器提取得到的特征，在边缘数据中心对二范数线性支持向量机(L2SVM)进行训练。实验结果证实，DCGAN具有较好的收敛性能，鉴别器提取得到的正常与窃电行为用电特征具有明显划分，且比基于主成分分析(PCA)特征提取方法更加有效，此外，与基于径向基核函数的支持向量机(SVM)反窃电方法相比，所提方法准确度更好且计算复杂度低，适合边缘数据中心部署。

Abstract

With the development of grid cyber-physical system

some of data processing tasks gradually sink to the edge side near end users. To provide dependable data source and detect anomaly consumption behaviors

electricity theft detection is one of the important functions of the edge data center. In the proposed electricity theft detection framework

the features are extracted by the discriminator of deep convolutional generative adversarial network (DCGAN) and used to train L2-regularized linear support vector machine (L2SVM) in edge data center. The results show that DCGAN has good convergence performance. The features of normal and abnormal data can be easily separated and the proposed feature extraction method is more efficient than the method based on principal component analysis (PCA). Compared with support vector machine

the proposed method can achieve higher accuracy and have lower computation complexity and therefore is suitable for an edge data center.

关键词

Keywords

references

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