Vibration modeling and simulation analysis of wind turbine flexible tower based on BP neural network

For the instability problem of the wind turbine flexible tower caused by mechanical fatigue and vibration, Back Propagation (BP) neural network is used to establish the tower vibration prediction model to solve the wind turbine tower vibration prediction analysis. To find correlation association factors impacting the flexible tower vibration, the multi-source heterogeneous data from various operating situations of wind turbines are optimized using correlation analysis. A BP neural network-based tower vibration prediction model is created. The findings of a simulation investigation using data from a wind farm’s 2 MW wind turbine with a 120 m flexible tower reveal that the BP neural network has the greatest predictive power for operating situations below the rated wind speed. The root mean square error RMSE decreased by 10.6608; the mean absolute error MAE decreased by 9.1915 compared to the above rated wind speed. It is proved that the BP neural network prediction model has high accuracy in predicting the vibration of the flexible tower for working conditions below the rated wind speed, which can provide accurate data support for the vibration control of the flexible towers.