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Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation

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Abstract

Electricity theft is a major cause of non-technical loss (NTL) in smart grids. However, existing research on electricity theft detection (ETD) lacks a generalized analysis of the characteristics of theft behaviors and fails to effectively address the problem of unbalanced sample distribution; the electricity theft features they recognize are also singular. To address these problems, a novel approach consisting of a sample augmentation mechanism based on convolutional transformer-Wasserstein generative adversarial networks (CT-WGANs) and an electricity theft detection scheme using bridged multiscale convolutional neural network-bidirectional gate recurrent units (MCNN-BiGRUs) is proposed in this paper. First, the generalized characteristics of electricity theft behavior in multiple time dimensions are analyzed, and the data slices are constructed. Then, with the aim of reducing the influence of unbalanced sample distribution, CT-WGAN, which focuses on the generalized characteristics of electricity theft in various time dimensions, is designed and has better sample generation capability. Finally, a bridged MCNN-BiGRU is proposed to recognize the temporal, transition, and persistence characteristics of electricity theft to improve the efficiency of ETD. Experimental results on the State Grid Corporation of China (SGCC) and Irish Smart Energy Trial (ISET) datasets show that the proposed approach outperforms traditional schemes in terms of the area under curve (AUC), precision, recall, f1-score, and accuracy.

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Authors and Affiliations

  1. Electronic Information and Electrical Engineering, Dalian University of Technology, No.2 Linggong Road, Ganjingzi District, Dalian, 116024, Liaoning, China

    Ruizhe Yao, Ning Wang, Weipeng Ke, Peng Chen & Xianjun Sheng

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  1. Ruizhe Yao
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  2. Ning Wang
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  3. Weipeng Ke
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  4. Peng Chen
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  5. Xianjun Sheng
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Correspondence to Ning Wang.

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Yao, R., Wang, N., Ke, W. et al. Electricity theft detection in unbalanced sample distribution: a novel approach including a mechanism of sample augmentation. Appl Intell 53, 11162–11181 (2023). https://doi.org/10.1007/s10489-022-04069-z

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