Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction
The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information...
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MDPI AG
2024-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/6057 |
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| author | Wei Bai Lan Xiong Yubei Liao Zhengyang Tan Jingang Wang Zhanlong Zhang |
| author_facet | Wei Bai Lan Xiong Yubei Liao Zhengyang Tan Jingang Wang Zhanlong Zhang |
| author_sort | Wei Bai |
| collection | DOAJ |
| description | The advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation. |
| format | Article |
| id | doaj-art-9d308c2310dc4528b45e7a1401de272e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-9d308c2310dc4528b45e7a1401de272e2025-08-20T01:55:51ZengMDPI AGSensors1424-82202024-09-012418605710.3390/s24186057Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature ExtractionWei Bai0Lan Xiong1Yubei Liao2Zhengyang Tan3Jingang Wang4Zhanlong Zhang5College of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaCincinnati Joint Co-Op Institute, Chongqing University, Chongqing 400044, ChinaCincinnati Joint Co-Op Institute, Chongqing University, Chongqing 400044, ChinaCollege of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaCollege of Electrical Engineering, Chongqing University, Chongqing 400044, ChinaThe advent of smart grids has facilitated data-driven methods for detecting electricity theft, with a preponderance of research efforts focused on user electricity consumption data. The multi-dimensional power state data captured by Advanced Metering Infrastructure (AMI) encompasses rich information, the exploration of which, in relation to electricity usage behaviors, holds immense potential for enhancing the efficiency of theft detection. In light of this, we propose the Catch22-Conv-Transformer method, a multi-dimensional feature extraction-based approach tailored for the detection of anomalous electricity usage patterns. This methodology leverages both the Catch22 feature set and complementary features to extract sequential features, subsequently employing convolutional networks and the Transformer architecture to discern various types of theft behaviors. Our evaluation, utilizing a three-phase power state and daily electricity usage data provided by the State Grid Corporation of China, demonstrates the efficacy of our approach in accurately identifying theft modalities, including evasion, tampering, and data manipulation.https://www.mdpi.com/1424-8220/24/18/6057electricity theftdata miningCatch22-Conv-Transformerthree-phase system |
| spellingShingle | Wei Bai Lan Xiong Yubei Liao Zhengyang Tan Jingang Wang Zhanlong Zhang Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction Sensors electricity theft data mining Catch22-Conv-Transformer three-phase system |
| title | Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction |
| title_full | Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction |
| title_fullStr | Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction |
| title_full_unstemmed | Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction |
| title_short | Detection Method for Three-Phase Electricity Theft Based on Multi-Dimensional Feature Extraction |
| title_sort | detection method for three phase electricity theft based on multi dimensional feature extraction |
| topic | electricity theft data mining Catch22-Conv-Transformer three-phase system |
| url | https://www.mdpi.com/1424-8220/24/18/6057 |
| work_keys_str_mv | AT weibai detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction AT lanxiong detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction AT yubeiliao detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction AT zhengyangtan detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction AT jingangwang detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction AT zhanlongzhang detectionmethodforthreephaseelectricitytheftbasedonmultidimensionalfeatureextraction |