A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss
Owing to the contagiousness of theft behaviors among customers, collaborative energy theft, such as village fraud, has become particularly common. In this study, a bunch of electricity thieves that steal energy at a constant ratio were considered. Conventional correlation-sorting-based methods may h...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9674926/ |
| _version_ | 1818744526222655488 |
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| author | Yining Yang Runan Song Yang Xue Penghe Zhang Yuejie Xu Jinping Kang Haisen Zhao |
| author_facet | Yining Yang Runan Song Yang Xue Penghe Zhang Yuejie Xu Jinping Kang Haisen Zhao |
| author_sort | Yining Yang |
| collection | DOAJ |
| description | Owing to the contagiousness of theft behaviors among customers, collaborative energy theft, such as village fraud, has become particularly common. In this study, a bunch of electricity thieves that steal energy at a constant ratio were considered. Conventional correlation-sorting-based methods may have some trouble handling these electricity thieves when they exist in the same area. To overcome such limitation, we firstly establish the mathematical model of non-technical loss (NTL) and the load data of fixed ratio electricity thieves (FRETs). Subsequently, an interesting correlation trend, which can be exploited to locate FRETs, was observed and analyzed. Based on this trend, we propose a correlation analysis-based detection method. It adopts a standardized covariance to measure the correlation between the NTL and user data. The detection of FRETs is realized by solving a combinatorial optimization problem. A corresponding framework in practice was also designed. Finally, numerical experiments based on a realistic dataset and an electricity theft dataset from an electricity theft emulator (ETE) are conducted to validate the effectiveness and superiority of the proposed method in terms of accuracy, stability, and scalability. |
| first_indexed | 2024-12-18T02:45:42Z |
| format | Article |
| id | doaj.art-203321c8cbc64be7ac57e293f3b6f242 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T02:45:42Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-203321c8cbc64be7ac57e293f3b6f2422022-12-21T21:23:34ZengIEEEIEEE Access2169-35362022-01-01105608561910.1109/ACCESS.2022.31416109674926A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical LossYining Yang0Runan Song1Yang Xue2Penghe Zhang3Yuejie Xu4Jinping Kang5https://orcid.org/0000-0002-5571-9034Haisen Zhao6https://orcid.org/0000-0003-3178-2490China Electric Power Research Institute, Beijing, Haidian, ChinaChina Electric Power Research Institute, Beijing, Haidian, ChinaChina Electric Power Research Institute, Beijing, Haidian, ChinaChina Electric Power Research Institute, Beijing, Haidian, ChinaState Key Laboratory of Alternative Electricity Power System With Renewable Energy Sources, North China Electric Power University, Beijing, Changping, ChinaState Key Laboratory of Alternative Electricity Power System With Renewable Energy Sources, North China Electric Power University, Beijing, Changping, ChinaState Key Laboratory of Alternative Electricity Power System With Renewable Energy Sources, North China Electric Power University, Beijing, Changping, ChinaOwing to the contagiousness of theft behaviors among customers, collaborative energy theft, such as village fraud, has become particularly common. In this study, a bunch of electricity thieves that steal energy at a constant ratio were considered. Conventional correlation-sorting-based methods may have some trouble handling these electricity thieves when they exist in the same area. To overcome such limitation, we firstly establish the mathematical model of non-technical loss (NTL) and the load data of fixed ratio electricity thieves (FRETs). Subsequently, an interesting correlation trend, which can be exploited to locate FRETs, was observed and analyzed. Based on this trend, we propose a correlation analysis-based detection method. It adopts a standardized covariance to measure the correlation between the NTL and user data. The detection of FRETs is realized by solving a combinatorial optimization problem. A corresponding framework in practice was also designed. Finally, numerical experiments based on a realistic dataset and an electricity theft dataset from an electricity theft emulator (ETE) are conducted to validate the effectiveness and superiority of the proposed method in terms of accuracy, stability, and scalability.https://ieeexplore.ieee.org/document/9674926/Data miningelectricity theft detectionfixed ratio electricity theftcovariance analysis |
| spellingShingle | Yining Yang Runan Song Yang Xue Penghe Zhang Yuejie Xu Jinping Kang Haisen Zhao A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss IEEE Access Data mining electricity theft detection fixed ratio electricity theft covariance analysis |
| title | A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss |
| title_full | A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss |
| title_fullStr | A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss |
| title_full_unstemmed | A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss |
| title_short | A Detection Method for Group Fixed Ratio Electricity Thieves Based on Correlation Analysis of Non-Technical Loss |
| title_sort | detection method for group fixed ratio electricity thieves based on correlation analysis of non technical loss |
| topic | Data mining electricity theft detection fixed ratio electricity theft covariance analysis |
| url | https://ieeexplore.ieee.org/document/9674926/ |
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