Detection and identification of non-technical loss based on electricity consumption curve and deep learning
Non-technical loss in power grid not only has a significant impact on the economic benefits of the power company, but also poses a serious threat to power quality and operational safety of the power system. In addition, measures taken by malicious users to seek profits grow in complexity, resulting...
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| Format: | Article |
| Language: | zho |
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Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.
2025-06-01
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| Series: | Diance yu yibiao |
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| Online Access: | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220902008&flag=1&journal_id=dcyyb&year_id=2025 |
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| author | WANG Yunjing XIAO Keyu QU Zhengwei HAN Xiaoming DONG Haiyan Popov Maxim Georgievitch |
| author_facet | WANG Yunjing XIAO Keyu QU Zhengwei HAN Xiaoming DONG Haiyan Popov Maxim Georgievitch |
| author_sort | WANG Yunjing |
| collection | DOAJ |
| description | Non-technical loss in power grid not only has a significant impact on the economic benefits of the power company, but also poses a serious threat to power quality and operational safety of the power system. In addition, measures taken by malicious users to seek profits grow in complexity, resulting in traditional detection methods gradually falling to limitation. Implementation means for non-technical loss based on electricity consumption curve are studied and tampering strategies used to generate false data are summarized. Behavior features of power users are extracted from the electricity consumption curve and associated with the results of electrical tampering implementation by bidirectional long short-term memory network. Finally, a multi-level neural network architecture is designed and deep learning is utilized to solve the multiclass classification problem of the feature sequences. Simulation based on actual power consumption dataset of a certain area shows that the research content can realize an effective detection of non-technical loss as well as identification of specific tampering strategies. |
| format | Article |
| id | doaj-art-c9836eb8a65945cc9bb9bf016863e22e |
| institution | Kabale University |
| issn | 1001-1390 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Harbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd. |
| record_format | Article |
| series | Diance yu yibiao |
| spelling | doaj-art-c9836eb8a65945cc9bb9bf016863e22e2025-08-20T03:24:21ZzhoHarbin Jinhe Electrical Measurement & Instrumentation Magazine Publishing Co., Ltd.Diance yu yibiao1001-13902025-06-0162620221110.19753/j.issn1001-1390.2025.06.0221001-1390(2025)06-0202-10Detection and identification of non-technical loss based on electricity consumption curve and deep learningWANG Yunjing0XIAO Keyu1QU Zhengwei2HAN Xiaoming3DONG Haiyan4Popov Maxim Georgievitch5Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaInstitute of Electrical Engineering, Yanshan University, Qinhuangdao 066004, Hebei, ChinaInstitute of Energy, Peter the Great St. Petersburg Polytechnic University, St. Petersburg 195251, RussiaNon-technical loss in power grid not only has a significant impact on the economic benefits of the power company, but also poses a serious threat to power quality and operational safety of the power system. In addition, measures taken by malicious users to seek profits grow in complexity, resulting in traditional detection methods gradually falling to limitation. Implementation means for non-technical loss based on electricity consumption curve are studied and tampering strategies used to generate false data are summarized. Behavior features of power users are extracted from the electricity consumption curve and associated with the results of electrical tampering implementation by bidirectional long short-term memory network. Finally, a multi-level neural network architecture is designed and deep learning is utilized to solve the multiclass classification problem of the feature sequences. Simulation based on actual power consumption dataset of a certain area shows that the research content can realize an effective detection of non-technical loss as well as identification of specific tampering strategies.http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220902008&flag=1&journal_id=dcyyb&year_id=2025non-technical lossdeep learningelectricity consumption curvebidirectional long short-term memorymulticlass classification problem |
| spellingShingle | WANG Yunjing XIAO Keyu QU Zhengwei HAN Xiaoming DONG Haiyan Popov Maxim Georgievitch Detection and identification of non-technical loss based on electricity consumption curve and deep learning Diance yu yibiao non-technical loss deep learning electricity consumption curve bidirectional long short-term memory multiclass classification problem |
| title | Detection and identification of non-technical loss based on electricity consumption curve and deep learning |
| title_full | Detection and identification of non-technical loss based on electricity consumption curve and deep learning |
| title_fullStr | Detection and identification of non-technical loss based on electricity consumption curve and deep learning |
| title_full_unstemmed | Detection and identification of non-technical loss based on electricity consumption curve and deep learning |
| title_short | Detection and identification of non-technical loss based on electricity consumption curve and deep learning |
| title_sort | detection and identification of non technical loss based on electricity consumption curve and deep learning |
| topic | non-technical loss deep learning electricity consumption curve bidirectional long short-term memory multiclass classification problem |
| url | http://www.emijournal.net/dcyyb/ch/reader/create_pdf.aspx?file_no=20220902008&flag=1&journal_id=dcyyb&year_id=2025 |
| work_keys_str_mv | AT wangyunjing detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning AT xiaokeyu detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning AT quzhengwei detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning AT hanxiaoming detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning AT donghaiyan detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning AT popovmaximgeorgievitch detectionandidentificationofnontechnicallossbasedonelectricityconsumptioncurveanddeeplearning |