摘要
针对风电机组数据采集与监视控制(SCADA)系统数据维数较高、特征冗余、特征相关性高导致风电机组的故障诊断过程存在误差大、分类正确率低的问题,提出一种基于LightGBM-VIF-MIC-SFS的三段式特征选择方法。首先,根据LightGBM实现对所有特征的重要性计算,确定初步特征空间;其次,根据方差膨胀因子(VIF)和最大信息系数(MIC)构建相关性判别阵,据此评估一次筛选中重要性相近的特征,舍弃相似性高的输入特征;最后,使用序列前向搜索法对特征进行第3次处理,逐个输入前2次特征选择获得的特征,保留能提升系统性能的特征,从而实现最终特征的选取。在完成了模型的建立后,使用风电场真实SCADA系统数据进行性能评估,将所提方法与2种对比算法在6个数据集上进行对比,结果显示所提出的LightGBM-VIF-MIC-SFS相较2种对比特征选择算法有显著优势。对所提方法内部的3个模块进行了消融实验,有效验证了所提特征选取方法内部各个模块的有效性以及基于所提方法得到的最优特征空间的合理性及准确性。
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 LightGBMVIF-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.
作者
马良玉
程东炎
梁书源
耿妍竹
段新会
MA Liangyu;CHENG Dongyan;LIANG Shuyuan;GENG Yanzhu;DUAN Xinhui(Department of Automation,North China Electric Power University,Baoding 071003,China;Baoding Huafang Technology Co.,Ltd.,Baoding 071000,China)
出处
《热力发电》
CAS
CSCD
北大核心
2024年第1期154-164,共11页
Thermal Power Generation
基金
河北省中央引导地方科技发展资金项目(226Z2103G)。
