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

Muhammad Salman Saeed, Mohd Wazir Mustafa, Nawaf N. Hamadneh, Nawa A. Alshammari, Usman Ullah Sheikh, Touqeer Ahmed Jumani, Saifulnizam Bin Abd Khalid and Ilyas Khan
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Muhammad Salman Saeed: School of Electrical Engineering, Universiti Teknologi Malaysia, Johor Bahru 81310, Malaysia
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
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

Energies, 2020, vol. 13, issue 18, 1-25

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.

Keywords: non-technical loss; electricity theft; power utilities; Artificial Intelligence; machine learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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