Research On Wind Turbine Tower Load Control Strategy Considering Extreme Wind Condition Prediction

Wind energy,as one of the most promising renewable energy sources,is crucial to the growth of social production.Inertia rises in tandem with the size,rotor diameter,and tower height of wind turbines.At the same time,because the traditional anemometer is mostly located on the nacelle behind the wind wheel,the wind speed measurement has a certain lag,which makes the pitch system always lag behind the change of wind speed.Especially in the face of extreme wind conditions,the contradiction is highlighted,resulting in a substantial increase in the load of the tower.This has a negative impact on the wind turbine’s ability to operate reliably.Therefore,through the identification of the extreme wind conditions in the wind speed sequence and the improvement of the control strategy,it is of great significance to reduce the tower load of the wind turbine under the extreme wind conditions and ensure the stable operation of the wind turbine under the extreme wind conditions.In this thesis,a comprehensive wind model containing extreme wind conditions and a wind turbine model are established for the problem of excessive load on the tower under extreme wind conditions.By comparing the wind speed data measured by lidar and traditional anemometer,the necessity of fitting the lidar wind data is illustrated.Simulation is carried out by establishing the model.It is assumed that under the same wind speed environment,the load on the bottom of the tower is considerably larger than that on the top,and the change of the load on the bottom of the tower has been the core of this study.The establishment of wind speed and wind turbine model also lays a foundation for subsequent simulation analysis of different control strategies.Secondly,aiming at the difference between the wind speed measured by lidar and the wind speed reaching the hub,this paper proposes a wind speed fitting strategy based on the wind speed measured by lidar.The ideal number of training samples and neurons is determined,and the wind speed fitting model of the BP neural network is created.Considering the rapid change of extreme gust wind speed,the gust identification model is established by calculating the change rate of wind speed,comparing the change rate of normal turbulent wind speed and extreme wind condition,and determining the appropriate threshold in accordance with the IEC standard in order to solve the identification problem of extreme gust in wind speed series.Simulation is used to verify the model’s accuracy,laying the groundwork for the subsequent advance control.Finally,the pitch control method for changing in advance based on wind conditions identification is given to address the conventional pitch control system’s lag problem.Through the application of the superior advantage of lidar for wind measurement,the gust can be recognized in advance.Fuzzy reasoning is used to determine the propeller pitch angle that needs to be changed.The problem of excessive load on wind turbine towers in extreme wind conditions can be reduced.The variable pitch controller’s control performance is improved by using fuzzy control to optimize the control parameters.The proposed control method’s simulation results are confirmed in a 2MW wind turbine.To demonstrate the efficiency of the proposed control method,the simulation results are compared to the control method of without advance variable pitch control and traditional PID control with optimization,respectively.