Electricity Theft Detection in Electrical Distribution System Using Long Short-Term Memory

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Published: Nov 23, 2022
DOI: https://doi.org/10.14456/nuej.2022.8
Keywords:
Non-technical loss Power theft Automatic Meter Reading Long Short-Term Memory classification

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Suttichai Premrudeepreechacharn
Department of Electrical Engineering, Faculty of Engineering, Chiangmai University
Chintana Xayalath
Department of Mechanical, Faculty of Engineering, Chiang Mai University, Chiang Mai 50200, Thailand
Kanchit Ngamsanroaj
Electricity Generating Authority of Thailand (EGAT), Nonthaburi, Thailand

Abstract

Abstract


This paper presents the method of detection of power theft in the electrical distribution system by using the real data from industry consumers that use meter AMR. The real data were feature extracted by labeling event types by the number demonstrated to normal, voltage theft, and current theft. Then, the data were fed to Long Short-Term Memory (LSTM) for the created model by training and testing. The accuracy results were shown the model can be classified accurate archive to 99%.


 

Article Details

How to Cite
Premrudeepreechacharn, S., Xayalath, C. ., & Ngamsanroaj, K. . (2022). Electricity Theft Detection in Electrical Distribution System Using Long Short-Term Memory. Naresuan University Engineering Journal, 17(2), 1–9. https://doi.org/10.14456/nuej.2022.8
Issue
Vol. 17 No. 2 (2022): July-December 2022
Section
Research Paper
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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

References

Bula, I., Hoxha, V., Shala, M., & Hajrizi, E. (2016). Minimizing non-technical losses with point-to-point measurement of voltage drop between “SMART”meters. IFAC PapersOnLine, 49(29),206-211.

Buzau, M. M., Tejedor-Aguilera, J., Cruz-Romero, P., & Gómez-Expósito, A. (2019). Hybrid deep neural networks for detection of non-technical losses in electricity smart meters. IEEE Transactions on Power Systems, 35(2), 1254-1263.

EDL (2020). Annual Report. EDL Vientiane.

Ghori, K. M., Abbasi, R. A., Awais, M., Imran, M., Ullah, A., & Szathmary, L. (2019). Performance analysis of different types of machine learning classifiers for non-technical loss detection. IEEE Access, 8, 16033-16048.

Glauner, P., Meira, J. A., Dolberg, L., State, R., Bettinger, F., & Rangoni, Y. (2016, December). Neighborhood features help detecting non-technical losses in big data sets. In Proceedings of the 3rd IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (pp. 253-261).

Long, H., Chen, C., Gu, W., Xie, J., Wang, Z., & Li, G. (2020). A Data-Driven Combined Algorithm for Abnormal Power Loss Detection in the Distribution Network. IEEE Access, 8, 24675-24686.

Micheli, G., Soda, E., Vespucci, M. T., Gobbi, M., & Bertani, A. (2019). Big data analytics: an aid to detection of non-technical losses in power utilities. Computational Management Science, 16(1), 329-343.

Nabil, M., Ismail, M., Mahmoud, M., Shahin, M., Qaraqe, K., & Serpedin, E. (2018, August). Deep recurrent electricity theft detection in AMI networks with random tuning of hyper-parameters. In 2018 24th International Conference on Pattern Recognition (ICPR) (pp. 740-745). IEEE.

Toma, R. N., Hasan, M. N., Nahid, A. A., & Li, B. (2019, May). Electricity theft detection to reduce non-technical loss using support vector machine in smart grid. In 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT) (pp. 1-6). IEEE.

Veerasamy, V., Wahab, N. I. A., Othman, M. L., Padmanaban, S., Sekar, K., Ramachandran, R., ... & Islam, M. Z. (2021). LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system. IEEE Access, 9, 32672-32687.

Working Group on Losses Reduction CIRED WG CC-2015-2 (2017). Reduction of Technical and Non-Technical Losses in Distribution Networks. CIRED http://www.cired.net.