Wind turbine fault diagnosis based on ReliefF-PCA and DNN

A large amount of data would be generated during the operation of wind turbine (WT), which is easy to cause dimensional disaster, and if more than one WT fault occur, multiple sensors would alarm. To solve the problems of big data, inaccurate and untimely fault diagnosis and so on, a hybrid fault diagnosis method is developed based on ReliefF, principal component analysis (PCA) and deep neural network (DNN) in this paper. Firstly, the ReliefF method is used to select the fault features and reduce the data dimensions. Secondly, PCA algorithm is used to further reduce the data dimensions, which is mainly used to reduce the redundancy among the data and improve the accuracy of fault diagnosis. Finally, the ReliefF-PCA-DNN model is constructed, optimized and used for the fault case of a wind farm in Jilin Province. The experimental results show that, for the single fault, the accuracies of the proposed hybrid models are all more than 98.5% and for the multi faults, the accuracy of the proposed model is more than 96%, which both are all much higher than the comparison methods. So, the method could diagnose the WT faults well.