Non-Hardware-Based Non-Technical Losses Detection Methods: A Review

Fernando G. K. Guarda, Bruno K. Hammerschmitt, Marcelo B. Capeletti, Nelson K. Neto (), Laura L. C. dos Santos, Lucio R. Prade and Alzenira Abaide
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Fernando G. K. Guarda: Santa Maria Technical and Industrial School, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Bruno K. Hammerschmitt: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Marcelo B. Capeletti: Graduate Program in Electrical Engineering, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
Nelson K. Neto: Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Brazil
Laura L. C. dos Santos: Academic Coordination, Federal University of Santa Maria, Cachoeira do Sul 96503-205, Brazil
Lucio R. Prade: Polytechnic School, University of Vale dos Sinos, São Leopoldo 93022-750, Brazil
Alzenira Abaide: Santa Maria Technical and Industrial School, Federal University of Santa Maria, Santa Maria 97105-900, Brazil

Energies, 2023, vol. 16, issue 4, 1-27

Abstract: Non-Technical Losses (NTL) represent a serious concern for electric companies. These losses are responsible for revenue losses, as well as reduced system reliability. Part of the revenue loss is charged to legal consumers, thus, causing social imbalance. NTL methods have been developed in order to reduce the impact in physical distribution systems and legal consumers. These methods can be classified as hardware-based and non-hardware-based. Hardware-based methods need an entirely new system infrastructure to be implemented, resulting in high investment and increased cost for energy companies, thus hampering implementation in poorer nations. With this in mind, this paper performs a review of non-hardware-based NTL detection methods. These methods use distribution systems and consumers’ data to detect abnormal energy consumption. They can be classified as network-based, which use network technical parameters to search for energy losses, data-based methods, which use data science and machine learning, and hybrid methods, which combine both. This paper focuses on reviewing non-hardware-based NTL detection methods, presenting a NTL detection methods overview and a literature search and analysis.

Keywords: Non-Technical Losses; machine learning; non-hardware-based methods; distribution systems; artificial intelligence (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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