Fault detection of key components of wind turbine based on combineation prediction model

This paper proposes a method for state modeling and fault identification of key components of wind turbines based on a combined model. Firstly, the SCADA data is identified, and the parameters related to the fault detection are selected. Then, the residual optimization problem is used to establish a nonlinear state estimation and neural network combination prediction model. The front bearing temperature is input into the combined model and the single model as parameters, and the accuracy of the model is reflected by the evaluation index. Finally, the SCADA data of the wind farm is used to analyze the wind turbine generator and gearbox temperature, and the combination prediction model is established to detect the fault. According to the residual temperature of the corresponding part will exceed the set threshold in the fault state, the data recorded before and after the fault are compared. The simulation results show that the proposed method is effective for fault detection of key components by establishing combined model. © 2021, Solar Energy Periodical Office Co., Ltd. All right reserved.