Data Augmentation for Electricity Theft Detection Using Conditional Variational Auto-Encoder

Xuejiao Gong, Bo Tang, Ruijin Zhu, Wenlong Liao and Like Song
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Xuejiao Gong: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
Bo Tang: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
Ruijin Zhu: Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, China
Wenlong Liao: Key Laboratory of Smart Grid of Ministry of Education, Tianjin University, Tianjin 300072, China
Like Song: Maintenance Branch of State Grid Jibei Electric Power Co., Ltd., Beijing 102488, China

Energies, 2020, vol. 13, issue 17, 1-14

Abstract: Due to the strong concealment of electricity theft and the limitation of inspection resources, the number of power theft samples mastered by the power department is insufficient, which limits the accuracy of power theft detection. Therefore, a data augmentation method for electricity theft detection based on the conditional variational auto-encoder (CVAE) is proposed. Firstly, the stealing power curves are mapped into low dimensional latent variables by using the encoder composed of convolutional layers, and the new stealing power curves are reconstructed by the decoder composed of deconvolutional layers. Then, five typical attack models are proposed, and the convolutional neural network is constructed as a classifier according to the data characteristics of stealing power curves. Finally, the effectiveness and adaptability of the proposed method is verified by a smart meters’ data set from London. The simulation results show that the CVAE can take into account the shapes and distribution characteristics of samples at the same time, and the generated stealing power curves have the best effect on the performance improvement of the classifier than the traditional augmentation methods such as the random oversampling method, synthetic minority over-sampling technique, and conditional generative adversarial network. Moreover, it is suitable for different classifiers.

Keywords: power theft detection; data augmentation; conditional variational auto-encoder; convolutional neural network; deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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