Electricity theft detection method based on multi‐domain feature fusion

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.

Electricity theft detection algorithm based on contrast learning and cluster combination discrimination

“…When K-means algorithm is initialized, it is necessary to determine the number of clusters, and this parameter has a great impact on the performance of the algorithm. Based on the analysis of existing research [14], this paper shows that electricity stealing behavior can be divided into six basic types, and other types of electricity stealing can be considered as the combination of these six types of electricity stealing. Therefore, the value of 7 is set in this paper for clustering to distinguish the power stealing users from the normal users.…”