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Input feature selection method for wind turbine fault diagnosis based on LightGBM-VIF-MIC-SFS
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Liangyu MA1, Dongyan CHENG1, Shuyuan LIANG1, Yanzhu GENG1, Xinhui DUAN1, 2
Thermal Power Generation | 2024, 53(1) : 154 - 164
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Thermal Power Generation | 2024, 53(1): 154-164
Power generation technology forum
Input feature selection method for wind turbine fault diagnosis based on LightGBM-VIF-MIC-SFS
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Liangyu MA1, Dongyan CHENG1, Shuyuan LIANG1, Yanzhu GENG1, Xinhui DUAN1, 2
Affiliations
  • 1.Department of Automation, North China Electric Power University, Baoding 071003, China
  • 2.Baoding Huafang Technology Co., Ltd., Baoding 071000, China
Published: 2024-01-25 doi: 10.19666/j.rlfd.202306123
Outline
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In order to solve the problems of high error and low classification accuracy in the fault diagnosis process of wind turbines caused by the high dimension, feature redundancy and feature correlation of wind turbine supervisory control and data acquisition (SCADA) data, a three-stage feature selection method based on LightGBM-VIF-MIC-SFS is proposed. Firstly, based on the importance calculation of all features implemented by LightGBM, a preliminary feature space is determined. Secondly, a correlation discriminant matrix is constructed based on the variance inflation factor (VIF) and maximum information coefficient (MIC) to evaluate features with similar importance in a single screening, and discard input features with high similarity. Finally, the sequential forward search method is used to process the features for the third time, input the features obtained from the previous two feature selection one by one, and retain the features that can improve the system performance, so as to achieve the final feature selection. After the establishment of the model, the real SCADA data of the wind farm is used for performance evaluation, and the proposed algorithm is compared with the two comparison algorithms on six data sets. The results show that LightGBM-VIF-MIC-SFS has significant advantages over the two comparison feature selection algorithms. A ablation experiment was conducted on the three modules within the proposed algorithm, effectively verifying the effectiveness of each module within the proposed feature selection method and the rationality and accuracy of the optimal feature space obtained based on the proposed method.

wind turbine  /  feature selection  /  LightGBM  /  variance inflation factor  /  maximum information coefficient  /  sequence forward search
Liangyu MA, Dongyan CHENG, Shuyuan LIANG, Yanzhu GENG, Xinhui DUAN. Input feature selection method for wind turbine fault diagnosis based on LightGBM-VIF-MIC-SFS[J]. Thermal Power Generation, 2024 , 53 (1) : 154 -164 . DOI: 10.19666/j.rlfd.202306123
  • Hebei Province Central Leading Local Science and Technology Development Fund Project(226Z2103G)
Year 2024 volume 53 Issue 1
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Article Info
doi: 10.19666/j.rlfd.202306123
  • Receive Date:2023-06-23
  • Online Date:2025-12-25
  • Published:2024-01-25
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  • Received:2023-06-23
Funding
Hebei Province Central Leading Local Science and Technology Development Fund Project(226Z2103G)
Affiliations
    1.Department of Automation, North China Electric Power University, Baoding 071003, China
    2.Baoding Huafang Technology Co., Ltd., Baoding 071000, China
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https://castjournals.cast.org.cn/joweb/rlfd/EN/10.19666/j.rlfd.202306123
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