Mechanical Engineering Science

Application of SABO-VMD-KELM in Fault Diagnosis of Wind Turbines

HEYuling (Department of Mechanical Engineering, North China Electric Power University), CUIHao (Department of Mechanical Engineering, North China Electric Power University)


In order to improve the accuracy of wind turbine fault diagnosis, a wind turbine fault diagnosis method based on Subtraction-Average-Based Optimizer (SABO) optimized Variational Mode Decomposition (VMD) and Kernel Extreme Learning Machine (KELM) is proposed. Firstly, the SABO algorithm was used to optimize the VMD parameters and decompose the original signal to obtain the best modal components, and then the nine features were calculated to obtain the feature vectors. Secondly, the SABO algorithm was used to optimize the KELM parameters, and the training set and the test set were divided according to different proportions. The results were compared with the optimized model without SABO algorithm. The experimental results show that the fault diagnosis method of wind turbine based on SABO-VMD-KELM model can achieve fault diagnosis quickly and effectively, and has higher accuracy.


Wind turbine generator; Fault diagnosis; Subtraction-Average-Based Optimizer (SABO);Variational Mode Decomposition (VMD); Kernel Extreme Learning Machine (KELM)

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