热力发电2024，Vol.53Issue(1)：154-164,11.DOI:10.19666/j.rlfd.202306123
基于LightGBM-VIF-MIC-SFS的风电机组故障诊断输入特征选择方法
Input feature selection method for wind turbine fault diagnosis based on LightGBM-VIF-MIC-SFS
摘要
Abstract
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.关键词
风电机组/特征选择/LightGBM/方差膨胀因子/最大信息系数/序列前向搜索Key words
wind turbine/feature selection/LightGBM/variance inflation factor/maximum information coeffiicient/sequence forward search引用本文复制引用
马良玉,程东炎,梁书源,耿妍竹,段新会..基于LightGBM-VIF-MIC-SFS的风电机组故障诊断输入特征选择方法[J].热力发电,2024,53(1):154-164,11.基金项目
河北省中央引导地方科技发展资金项目(226Z2103G)Hebei Province Central Leading Local Science and Technology Development Fund Project(226Z2103G) （226Z2103G）
