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Power Data Identification Method Based on Multi-Domain Feature Analysis and Selection
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De-hua HONG1, Cui-ling LIU1, Lin-yan ZHAO1, Qin-yi LEI1, Hai-xin WANG2
Water Resources and Power | 2023, 41(9) : 211 - 215
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Water Resources and Power | 2023, 41(9): 211-215
• ELECTRICAL ENGINEERING •
Power Data Identification Method Based on Multi-Domain Feature Analysis and Selection
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De-hua HONG1, Cui-ling LIU1, Lin-yan ZHAO1, Qin-yi LEI1, Hai-xin WANG2
Affiliations
  • 1.Information and Communication Branch of State Grid Anhui Electric Power Co., Ltd., Hefei 230061, China
  • 2.School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
Published: 2023-09-25 doi: 10.20040/j.cnki.1000-7709.2023.20222030
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To solve the problem of low recognition accuracy caused by insufficient power data feature mining, this paper proposed a novel power data identification method based on multi-domain feature analysis and feature selection. Firstly, aiming at the shortcomings of existing power data feature extraction methods, a feature extraction method based on empirical mode decomposition (EMD) and Hilbert transform (EMD-Hilbert) was proposed, and the power features and V-I trajectory features of power data were quantified. Secondly, based on random forest and generalized sequence backward selection search strategy, the optimal feature subset was obtained. The random forest was employed to build a recognition model for the power data. Finally, the experimental results verified the effectiveness and identification accuracy of the proposed method. The results show that the proposed method can utilize the complementarity of different features to overcome the problem of low accuracy by single feature, and further improve the model recognition performance through feature selection.

power data identification  /  multi-domain feature extraction  /  feature selection  /  random forest  /  generalized sequence backward selection
De-hua HONG, Cui-ling LIU, Lin-yan ZHAO, Qin-yi LEI, Hai-xin WANG. Power Data Identification Method Based on Multi-Domain Feature Analysis and Selection[J]. Water Resources and Power, 2023 , 41 (9) : 211 -215 . DOI: 10.20040/j.cnki.1000-7709.2023.20222030
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Year 2023 volume 41 Issue 9
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Article Info
doi: 10.20040/j.cnki.1000-7709.2023.20222030
  • Receive Date：2022-09-28
  • Online Date：2026-01-28
  • Published：2023-09-25
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  • Received：2022-09-28
  • Revised：2022-11-18
Funding
Affiliations
    1.Information and Communication Branch of State Grid Anhui Electric Power Co., Ltd., Hefei 230061, China
    2.School of Electrical Engineering, Shenyang University of Technology, Shenyang 110870, China
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