Vibration monitoring of wind turbine tower based on XGBoost

A tower vibration monitoring method based on XGBoost is proposed to predict the tower vibration trends under different operating conditions. Firstly, the wind turbine operating conditions are classified based on the Kmeans clustering algorithm. Secondly, the impact of state parameters of the wind turbine on the tower vibration is analyzed, and the tower vibration monitoring model is established based on XGBoost algorithm. Finally, the actual SCADA data of the wind farm is used to verify the proposed method. The results show that the vibration monitoring accuracy of tower is effectively improved by considering the operating conditions of the wind turbine.