Detection method for group fixed-proportion electricity theft behaviors

When there are a fixed proportion of theft users in a station area of power system, the Non Technical Loss (NTL) is determined by the power stealing amount of the fixed proportion users. In this paper, it is found in the problem statement part that there is an incremental phenomenon between the NTL of theft users and the data of electric meters, the mathematical model between NTL and electricity consumption is established and the sufficient conditions for this phenomenon are demonstrated in modeling part. After that, a detection method for group fixed-proportion electricity theft behaviors is proposed and the scheme of this method is designed. Finally, the effectiveness of this method is verified by case study in simulation analysis part, which shows that the accuracy of this method is improved by nearly 25%.

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Publication Details

Published
2025-04-18
DOI
https://doi.org/10.1117/12.3065112
Primary Topic
Electricity Theft Detection Techniques
Type
article
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article

Detection method for group fixed-proportion electricity theft behaviors

Yan Xing, Chenghu Ma, Xiaofeng Han, Hui Han
Electricity Theft Detection Techniques
article

Detection method for group fixed-proportion electricity theft behaviors

Yan Xing, Chenghu Ma, Xiaofeng Han, Hui Han
article en

Abstract

When there are a fixed proportion of theft users in a station area of power system, the Non Technical Loss (NTL) is determined by the power stealing amount of the fixed proportion users. In this paper, it is found in the problem statement part that there is an incremental phenomenon between the NTL of theft users and the data of electric meters, the mathematical model between NTL and electricity consumption is established and the sufficient conditions for this phenomenon are demonstrated in modeling part. After that, a detection method for group fixed-proportion electricity theft behaviors is proposed and the scheme of this method is designed. Finally, the effectiveness of this method is verified by case study in simulation analysis part, which shows that the accuracy of this method is improved by nearly 25%.

State Grid Corporation of China (China) (CN), Shanghai Electric (China) (CN)
Openalex Percentile: Top 94%
Electricity Theft Detection Techniques
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