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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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References

  1. Jin S C, Lee S, Chun S. J. (2021) A queueing network analysis of a hierarchical communication architecture for advanced metering Infrastructure[J]. IEEE Trans Smart Grid 12(05):4318–4326

    Article  Google Scholar 

  2. Ghosal A, Conti M. (2019) Key management systems for smart grid advanced metering infrastructure: a survey communications surveys & Tutorials[J]. EEE Commun Surv Tutor 24(3):2831–2848

    Article  Google Scholar 

  3. Singh N K, Mahajan V (2021) End-User Privacy Protection Scheme from Cyber Intrusion in Smart Grid Advanced Metering Infrastructure[J]. Int J Crit Infrastruct Prot 34:100410

    Article  Google Scholar 

  4. Garg S, Kaur K, Kaddoum G, et al. (2020) Secure and lightweight authentication scheme for smart metering infrastructure in smart Grid[J]. IEEE Trans Ind Inform 16(5):3548–3557

    Article  Google Scholar 

  5. Yao R, Wang N, Liu Z, et al. (2021) Intrusion detection system in the advanced metering infrastructure: a cross-layer feature-fusion cnn-lstm-based approach[J]. Sensors 21(2):626

    Article  Google Scholar 

  6. Jokar P, Arianpoo N, Leung V. (2017) Electricity theft detection in AMI using customers’ consumption patterns[J]. IEEE Trans Smart Grid 7(1):216–226

    Article  Google Scholar 

  7. Ismail M, Shaaban M F, Naidu M, et al. (2020) Deep learning detection of electricity theft Cyber-Attacks in renewable distributed Generation[J]. IEEE Trans Smart Grid 11(4):3428–3437

    Article  Google Scholar 

  8. Buzau M M, Tejedor-Aguilera J, Cruz-Romero P, Gómez-Expósito A (2019) Detection of Non-Technical losses using smart meter data and supervised Learning [J]. IEEE Trans Smart Grid 10(3):2661–2670

    Article  Google Scholar 

  9. Leite J B, Mantovani J (2018) Detecting and Locating Non-technical Losses in Modern Distribution Networks[J]. IEEE Trans Smart Grid 9(2):3428–3437

    Article  Google Scholar 

  10. Han W, Yang X (2017) NFD: Non-technical Loss fraud detection in Smart Grid[J]. Comput Secur 65(MAR.):187–201

    Article  Google Scholar 

  11. He Y, Mendis G J, Jin W (2017) Real-Time Detection of false data injection attacks in smart grid: a deep learning-based intelligent mechanism[J]. IEEE Trans Smart Grid 8(5):2505–2516

    Article  Google Scholar 

  12. Raggi L M R, Trindade F C L, Cunha V C, Freitas W (2020) Non-technical loss identification by using data analytics and customer smart meters[J]. IEEE Trans Power Deliv 35(6):2700–2710

    Google Scholar 

  13. Hussain S, Mustafa M W, Jumani T A, et al. (2021) A novel feature engineered-CatBoost-based supervised machine learning framework for electricity theft detection[J]. Energy Rep 7(12):4425–4436

    Article  Google Scholar 

  14. Gunturi S K, Sarkar D (2020) Ensemble machine learning models for the detection of energy theft[J]. Electr Power Syst Res 192(April):106904

    Google Scholar 

  15. Euha B, Jh A, Hx B, et al. (2021) A hybrid approach based on deep learning and support vector machine for the detection of electricity theft in power grids[J]. Energy Rep 7(6):349–356

    Google Scholar 

  16. Zheng K, Chen Q, Wang Y, et al. (2019) A novel combined Data-Driven approach for electricity theft Detection[J]. IEEE Trans Ind Inform 15(3):1809–1819

    Article  Google Scholar 

  17. Gajowniczek K, Sodenkamp M, Zbkowski T (2018) Revealing household characteristics from electricity meter data with grade analysis and machine learning algorithms[J]. Appl Sci 8(9):1654

    Article  Google Scholar 

  18. Yan Z, Wen H (2021) Electricity theft detection base on extreme gradient boosting in AMI[j]. IEEE Trans Instrum Meas 70:1–9

    Google Scholar 

  19. Choe S, Punmiya R (2019) Energy theft detection using gradient boosting theft detector with feature engineering-based preprocessing[J]. IEEE Trans Smart Grid 10(2):2326–2329

    Article  Google Scholar 

  20. Kong X, Zhao X, Liu C, et al. (2021) Electricity theft detection in low-voltage stations based on similarity measure and DT-KSVM[J]. Int J Electr Power Energy Syst 125(3):106544

    Article  Google Scholar 

  21. Aa A, Taa B, Zak C, et al. (2022) Towards efficient energy utilization using big data analytics in smart cities for electricity theft detection - ScienceDirect[J]. Big Data Res 27(3):100285

    Google Scholar 

  22. Buzau M M, Tejedor-Aguilera J, Cruz-Romero P, et al. (2020) Hybrid deep neural networks for detection of Non-Technical losses in electricity smart Meters[J]. IEEE Trans Power Syst 35(2):1254–1263

    Article  Google Scholar 

  23. Hasan M N, Toma R N, Nahid A A, et al. (2019) Electricity theft detection in smart grid systems: a CNN-LSTM based approach[J]. Energies 12(17):3310

    Article  Google Scholar 

  24. Yao D, Wen M, Liang X, et al. (2019) Energy theft detection with energy privacy preservation in the smart grid[J]. IEEE Internet Things J 6(5):7659–7669

    Article  Google Scholar 

  25. Gong X, Tang B, Zhu R, et al. (2020) Data augmentation for electricity theft detection using conditional variational auto-encoder[J]. Energies 13(17):4291

    Article  Google Scholar 

  26. Takiddin A, Ismail M, Nabil M, et al. (2020) Detecting electricity theft cyber-attacks in AMI networks using deep vector embeddings[J]. IEEE Syst J 15(3):4189–4198

    Article  Google Scholar 

  27. Javaid N, Jan N, Javed M U (2021) An adaptive synthesis to handle imbalanced big data with deep siamese network for electricity theft detection in smart grids - ScienceDirect[J]. J Parallel Distrib Comput 153:44–52

    Article  Google Scholar 

  28. Pereira J, Saraiva F (2021) Convolutional neural network applied to detect electricity theft: a comparative study on unbalanced data handling techniques[J]. Int J Electr Power Energy Syst 131(9):107085

    Article  Google Scholar 

  29. Huang Y, Xu Q (2021) Electricity theft detection based on stacked sparse denoising autoencoder[J]. Int J Electr Power Energy Syst 125(2):106448

    Article  Google Scholar 

  30. Zheng Z, Yang Y, Niu X, Dai H -N, Zhou Y (2018) Wide and deep convolutional neural networks for Electricity-Theft detection to secure smart Grids[J]. IEEE Trans Ind Inf 14(4):1606– 1615

    Article  Google Scholar 

  31. http://www.sgcc.com. Accessed 4 Jan 2022.

  32. http://www.ucd.ie/issda/data. Accessed 30 May 2022.

  33. Zhang J, Zhao X (2021) Wind farm wake modeling based on deep convolutional conditional generative adversarial network[J]. Energy 238:121747

    Article  Google Scholar 

  34. Zhang AQ, Xiao F, et al. (2020) Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network - ScienceDirect[J]. Int J Electr Power Energy Syst 114:105388

    Article  Google Scholar 

  35. Duan Y, Li H, He M, et al. (2021) A biGRU autoencoder remaining useful life prediction scheme with attention mechanism and skip connection[J]. IEEE Sensors J 21(9):10905– 10914

    Article  Google Scholar 

  36. Zicari P, Folino G, Guarascio M, Pontieri L (2022) Combining deep ensemble learning and explanation for intelligent ticket management[J]. Exp Syst Appl 206:117815

    Article  Google Scholar 

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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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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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