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

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 r...

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Bibliographic Details
Published in:Energies (Basel) 2023-02, Vol.16 (4), p.2054
Main Authors: Guarda, Fernando, Hammerschmitt, Bruno, Capeletti, Marcelo, Neto, Nelson, dos Santos, Laura, Prade, Lucio, Abaide, Alzenira
Format: Article
Language:English
Subjects:
Artificial intelligence
Component reliability
Consumers
Data science
Decision making
Decision trees
distribution systems
Electric utilities
Electricity
Energy consumption
Energy dissipation
Energy industry
Energy prices
Fraud
Infrastructure
Machine learning
non-hardware-based methods
Non-Technical Losses
Reliability (Engineering)
Revenue
Smart grid technology
Support vector machines
System reliability
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Summary: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.
ISSN:1996-1073
1996-1073
DOI:10.3390/en16042054