Prediction of Wind Turbine Blade Fatigue Life Based on GA-BP Neural Network

A method for predicting the fatigue life of wind turbine blades based on Genetic Algorithm (GA)-BP Neural Network is proposed to address the issues of harsh operating conditions and complex load conditions for wind turbine blades, as well as the limitations of traditional physical models in predicting blade life. Firstly, a simulation model of the blade is established using finite element method, and the relevant data set of blade stress and fatigue life values is obtained by ANSYS finite element analysis. Secondly, a BP neural network is used to establish a prediction model for blade fatigue life, and genetic algorithm is introduced to optimize the prediction accuracy of the BP neural network. Finally, the GA-BP neural network model is tested and verified using the data set and compared with other network models. The results show that the GA-BP model has higher prediction accuracy, with an average absolute error (MAE), root mean square error (RMSE), and average absolute percentage error (MAPE) of 0.0046, 0.0050, and 0.0693%, respectively, which is better than that of Convolutional Neural Network (CNN), Support Vector Regression (SVR), and traditional BP model, validating its reliability and good prediction performance.