基于多目标灰狼算法的漂浮式风电机组浮台内TMD参数优化

TMD PARAMETER OPTIMIZATION OF FLOATING WIND TURBINE FLOATING PLATFORM BASED ON MULTI-OBJECTIVE GRAY WOLF OPTIMIZER

针对漂浮式风电机组浮台内调谐质量阻尼器(TMD)参数调优的问题,以5 MW Barge型漂浮式风电机组为研究对象,采用多目标灰狼算法(MOGWO)优化TMD参数配置。首先,基于欧拉-拉格朗日方程建立浮台内含TMD的漂浮式风电机组动力学模型,采用Levenberg-Marquardt(LM)法进行模型未知参数辨识;其次,同时考虑塔顶和塔基控制目标,采用MOGWO算法优化TMD的刚度和阻尼参数;最后,在不同工况下进行仿真分析。结果表明:相对于传统的单目标优化算法,使用MOGWO算法参数优化后的TMD对风电机组具有更好的振动抑制效果。

Aiming at the problem of tuning the parameters of the tuned mass damper(TMD)in the offshore turbine floating platform,this paper takes the 5 MW Barge offshore wind turbine as the research object and uses the multi-objective grey wolf optimizer(MOGWO)to optimize the TMD parameter configuration.Firstly,based on the Euler-Lagrange equation,the dynamic models of offshore wind turbine containing TMD in the floating platform is established,and the Levenberg-Marquardt(LM)method is used to identify the unknown parameters of the model.Secondly,considering the control targets of the top of the tower and the base of the tower,the MOGWO algorithm is used to optimize the stiffness and damping parameters of TMD.Finally,simulation analysis is carried out under different working conditions.The results show that compared with the traditional single-objective optimization algorithm,the TMD optimized by MOGWO algorithm has a better vibration control effect on wind turbines.