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

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.

Identification of Single Leakage Currents in the Phases of a Distribution Grid Controlled by an Auto-mated Metering System

“…Таким образом, расчетные выражения ( 7)-(15) являются решением сформулированной задачи определения утечек в фазах РЭС по данным (3) и условиях (1).…”

“…Указанные воздействия обуславливают возникновение нетехнических потерь электроэнергии в РЭС. Анализу технологий выявления нетехнических потерь в РЭС посвящен ряд обзорных работ [1][2][3][4] и большое количество оригинальных исследований . Сопоставление данных о потреблении электроэнергии, регистрируемых приборами учета абонентов, с соответствующим энергопотреблением головного прибора учета контролируемого участка РЭС предоставляет новую возможность для точного выявления источника нетехнических потерь электроэнергии.…”

Artificial intelligence for energy fraud detection: a review

“…With the development in the field of AI, the detection methods have also become advanced compared to a conventional method. AI detection method is considered superior in terms of accuracy, efficiency, time-consumption, precision, and labor required [7]. There is a growing number of researches been done for NTL detection using AI techniques.…”

Fraud Detection on Power Grids While Transitioning to Smart Meters by Leveraging Multi-Resolution Consumption Data

“…A classic problem in the analysis of electricity consumption data is detecting non-technical losses (NTL), an issue of great interest given the economic impact on energy distribution companies [1], [2]. There are numerous proposals in the literature to address this problem for smart meter infrastructures [3]- [6] and the previous generations of meters [7]- [9].…”