# Fault Diagnosis for Wind Turbines Based on ReliefF and eXtreme Gradient Boosting  
[PERSON]  
School of Mechanical, Electrical and Information Engineering, Shandong University, Weihai 264209, China; wuzidong@mail. for generator and gearbox has a great significance in reducing downtime and improving wind power production efficiency. The wind farm Supervisory Control And Data Acquisition (SCADA) system has a wind farm operation monitoring module and a wind farm fault alarm module. The wind farm operation monitoring module will record the values of observation parameters in real time. The fault alarm module of wind farm will generate corresponding operation records and alarm information [7; 8]. Various faults of wind turbines are hidden in the observed parameters of the SCADA system that can characterize their operating status. Therefore, by mining the data records in the wind turbine SCADA system, the fault diagnosis of the wind turbine can be completed. With the continuous development of the wind power industry, research on the fault diagnosis of wind turbine is also in-depth. Some scholars analyze the vibration signal of rotating parts to realize fault diagnosis. A method of fault diagnosis based on wavelet packet transform of vibration signal can be found in [9]. A wind turbine fault diagnosis method is proposed based on the Morlet wavelet transformation and Wigner-Ville Distribution (WVD) in [10]. This method uses continuous wavelet transform (CWT) to filter out the useless noise in the original vibration signal, and uses the Automatic iTem Window (ATW) function to suppress the cross term in WVD. Some people study the data of the SCADA system of wind turbine to realize fault diagnosis. An optimal variable selection algorithm based on principal component analysis (PCA) was proposed for wind turbine fault identification in [11]. A new method using SCADA data for detecting and classifying faults in wind turbine actuators and sensors can be found in [12], but in the algorithm experiment, the similar faults are combined to be identified, so it is impossible to give the specific fault types accurately. In the work of [PERSON] et al. [13], a strategy is given to complete the condition monitoring of a wind turbine based on statistical data modeling. In this method, multi-channel principal component analysis (MPCA) is used to obtain the standard model of data from healthy wind turbines. There are also some scholars who use algorithms to predict a SCADA system variable of a wind turbine and diagnose faults by judging the difference between the predicted and actual values. [PERSON] established a regression prediction model based on the Support Vector Regression (SVR) algorithm with the monitoring items of SCADA system as the input and active power as the output, and judged whether the wind turbine was in fault according to the residual of the real-time power and predicted power of the wind turbine in [14]. In the work of [PERSON] et al. [15], a method of wind turbine condition monitoring was proposed based on Artificial Neural Networks (ANN), which uses a neural network to predict the temperature value of the gearbox bearing, and calculates the Markov distance between the predicted and actual measured values to determine whether the bearing is faulty. A method is proposed for predicting the gearbox oil pressure using a deep neural network and determining whether the gearbox is faulty based on the oil pressure in [16]. In fact, using classification algorithms to analyze the characteristics of each type of fault data and the characteristics of normal data can also achieve fault diagnosis [17]. Although the aforementioned efforts give specific methods for wind turbine fault diagnosis, there are still some shortcomings. First, there are difficulties in feature extraction and selection. Due to the large number of monitoring characteristic parameters in the SCADA system of a wind turbine, how to select the characteristic monitoring parameters related to a fault has great influence on the accuracy of fault diagnosis.