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Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System

Author

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  • Konstantinos V. Blazakis

    (School of Electrical and Computer Engineering, Technical University of Crete, University Campus, GR-73100 Chania, Greece)

  • Theodoros N. Kapetanakis

    (Department of Electronic Engineering, Hellenic Mediterranean University, GR-73100 Chania, Greece)

  • George S. Stavrakakis

    (School of Electrical and Computer Engineering, Technical University of Crete, University Campus, GR-73100 Chania, Greece)

Abstract

Electric power grids are a crucial infrastructure for the proper operation of any country and must be preserved from various threats. Detection of illegal electricity power consumption is a crucial issue for distribution system operators (DSOs). Minimizing non-technical losses is a challenging task for the smooth operation of electrical power system in order to increase electricity provider’s and nation’s revenue and to enhance the reliability of electrical power grid. The widespread popularity of smart meters enables a large volume of electricity consumption data to be collected and new artificial intelligence technologies could be applied to take advantage of these data to solve the problem of power theft more efficiently. In this study, a robust artificial intelligence algorithm adaptive neuro fuzzy inference system (ANFIS)—with many applications in many various areas—is presented in brief and applied to achieve more effective detection of electric power theft. To the best of our knowledge, there are no studies yet that involve the application of ANFIS for the detection of power theft. The proposed technique is shown that if applied properly it could achieve very high success rates in various cases of fraudulent activities originating from unauthorized energy usage.

Suggested Citation

  • Konstantinos V. Blazakis & Theodoros N. Kapetanakis & George S. Stavrakakis, 2020. "Effective Electricity Theft Detection in Power Distribution Grids Using an Adaptive Neuro Fuzzy Inference System," Energies, MDPI, vol. 13(12), pages 1-13, June.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:12:p:3110-:d:372295
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    References listed on IDEAS

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    1. Viegas, Joaquim L. & Esteves, Paulo R. & Melício, R. & Mendes, V.M.F. & Vieira, Susana M., 2017. "Solutions for detection of non-technical losses in the electricity grid: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 1256-1268.
    2. Md. Nazmul Hasan & Rafia Nishat Toma & Abdullah-Al Nahid & M M Manjurul Islam & Jong-Myon Kim, 2019. "Electricity Theft Detection in Smart Grid Systems: A CNN-LSTM Based Approach," Energies, MDPI, vol. 12(17), pages 1-18, August.
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    Cited by:

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    2. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
    3. Savian, Fernando de Souza & Siluk, Julio Cezar Mairesse & Garlet, Taís Bisognin & do Nascimento, Felipe Moraes & Pinheiro, José Renes & Vale, Zita, 2021. "Non-technical losses: A systematic contemporary article review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
    4. Francisco Jonatas Siqueira Coelho & Allan Rivalles Souza Feitosa & André Luís Michels Alcântara & Kaifeng Li & Ronaldo Ferreira Lima & Victor Rios Silva & Abel Guilhermino da Silva-Filho, 2023. "HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms," Energies, MDPI, vol. 16(13), pages 1-22, June.
    5. Mitra, Somalee & Chakraborty, Basab & Mitra, Pabitra, 2024. "Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions," Energy, Elsevier, vol. 289(C).
    6. Hasan M. Salman & Jagadeesh Pasupuleti & Ahmad H. Sabry, 2023. "Review on Causes of Power Outages and Their Occurrence: Mitigation Strategies," Sustainability, MDPI, vol. 15(20), pages 1-34, October.

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