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Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review

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  • Muhammad Salman Saeed

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
    Multan Electric Power Company (MEPCO), Multan 60000, Pakistan)

  • Mohd Wazir Mustafa

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Nawaf N. Hamadneh

    (Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Nawa A. Alshammari

    (Department of Basic Sciences, College of Science and Theoretical Studies, Saudi Electronic University, Riyadh 11673, Saudi Arabia)

  • Usman Ullah Sheikh

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Touqeer Ahmed Jumani

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
    Department of Electrical Engineering, Mehran University of Engineering and Technology, SZAB Campus, Khairpur Mirs 66020, Pakistan)

  • Saifulnizam Bin Abd Khalid

    (School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia)

  • Ilyas Khan

    (Faculty of Mathematics & Statistics, Ton Duc Thang University, Ho Chi Minh City 72915, Vietnam)

Abstract

Electricity theft and fraud in energy consumption are two of the major issues for power distribution companies (PDCs) for many years. PDCs around the world are trying different methodologies for detecting electricity theft. The traditional methods for non-technical losses (NTLs) detection such as onsite inspection and reward and penalty policy have lost their place in the modern era because of their ineffective and time-consuming mechanism. With the advancement in the field of Artificial Intelligence (AI), newer and efficient NTL detection methods have been proposed by different researchers working in the field of data mining and AI. The AI-based NTL detection methods are superior to the conventional methods in terms of accuracy, efficiency, time-consumption, precision, and labor required. The importance of such AI-based NTL detection methods can be judged by looking at the growing trend toward the increasing number of research articles on this important development. However, the authors felt the lack of a comprehensive study that can provide a one-stop source of information on these AI-based NTL methods and hence became the motivation for carrying out this comprehensive review on this significant field of science. This article systematically reviews and classifies the methods explored for NTL detection in recent literature, along with their benefits and limitations. For accomplishing the mentioned objective, the opted research articles for the review are classified based on algorithms used, features extracted, and metrics used for evaluation. Furthermore, a summary of different types of algorithms used for NTL detection is provided along with their applications in the studied field of research. Lastly, a comparison among the major NTL categories, i.e., data-based, network-based, and hybrid methods, is provided on the basis of their performance, expenses, and response time. It is expected that this comprehensive study will provide a one-stop source of information for all the new researchers and the experts working in the mentioned area of research.

Suggested Citation

  • Muhammad Salman Saeed & Mohd Wazir Mustafa & Nawaf N. Hamadneh & Nawa A. Alshammari & Usman Ullah Sheikh & Touqeer Ahmed Jumani & Saifulnizam Bin Abd Khalid & Ilyas Khan, 2020. "Detection of Non-Technical Losses in Power Utilities—A Comprehensive Systematic Review," Energies, MDPI, vol. 13(18), pages 1-25, September.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4727-:d:411917
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    References listed on IDEAS

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    Cited by:

    1. Sufian A. Badawi & Djamel Guessoum & Isam Elbadawi & Ameera Albadawi, 2022. "A Novel Time-Series Transformation and Machine-Learning-Based Method for NTL Fraud Detection in Utility Companies," Mathematics, MDPI, vol. 10(11), pages 1-16, May.
    2. Darragh Carr & Murray Thomson, 2022. "Non-Technical Electricity Losses," Energies, MDPI, vol. 15(6), pages 1-14, March.
    3. Rui Xia & Yunpeng Gao & Yanqing Zhu & Dexi Gu & Jiangzhao Wang, 2022. "An Efficient Method Combined Data-Driven for Detecting Electricity Theft with Stacking Structure Based on Grey Relation Analysis," Energies, MDPI, vol. 15(19), pages 1-25, October.

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