The typical faults of wind turbines are summarized. The fault data and non-fault data of converter system

generator system

variable propeller system and auxiliary power system with high fault frequency of wind turbines in a wind farm are selected for fault diagnosis research. The fault diagnosis model is established by ELM

SVM

KELM and WOA-KELM algorithms respectively. At the same time

Laplacian scores are used to sort and select the importance degree of model characteristic variables. WOA-KELM algorithm achieves better diagnostic effect by optimizing the regularization parameter C and kernel parameter γ of KELM algorithm. The results show that

the diagnostic accuracy of the four algorithms for non-fault types is 100% under different sample numbers. The average diagnostic accuracy of WOA-KELM algorithm improves from 88.0% to 93.2% after feature screening by using Laplace scores. In the range of 250～500 samples

the diagnostic accuracy of WOA-KELM algorithm reaches the maximum of 96.0% after feature screening. It is proved that this model can effectively realize the fault diagnosis of wind turbine

and provide guidance and reference for field operation and maintenance personnel.