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Electricity theft detection method based on multi‐domain feature fusion

Authors :
Hong‐shan Zhao
Cheng‐yan Sun
Li‐bo Ma
Yang Xue
Xiao‐mei Guo
Jie‐ying Chang
Source :
IET Science, Measurement & Technology, Vol 17, Iss 3, Pp 93-104 (2023)
Publication Year :
2023
Publisher :
Wiley, 2023.

Abstract

Abstract To solve the problem of low accuracy of the previous electricity theft detection methods, the authors propose a multi‐domain feature (MDF) fusion electricity theft detection method based on improved tensor fusion (ITF). Firstly, the original electricity consumption series is transformed by gram angle field (GAF) to obtain the time‐domain matrix. The original electricity consumption series is converted into frequency‐domain by Maximal Overlap Discrete Wavelet Transform (MODWT) to obtain the frequency‐domain matrix. Then, the convolutional neural networks (CNN) are used to extract features of the time‐domain matrix and frequency‐domain matrix, respectively. Next, in order to fuse single‐domain feature information and MDF interaction information while reducing redundant information, the authors propose an ITF method to obtain a multi‐domain fusion tensor. Finally, the multi‐domain fusion tensor is input into the electricity theft inference module to judge whether the user implements electricity theft behaviour. The authors simulate six electricity theft types and evaluate the method's performance separately for each electricity theft type. The results show that the proposed method outperforms other methods.

Details

Language :
English
ISSN :
17518830 and 17518822
Volume :
17
Issue :
3
Database :
Directory of Open Access Journals
Journal :
IET Science, Measurement & Technology
Publication Type :
Academic Journal
Accession number :
edsdoj.29258a6da844969f3cd56078403115
Document Type :
article
Full Text :
https://doi.org/10.1049/smt2.12133