Robust model predictive control of wind turbines based on Bayesian parameter self-optimization - Tang, Minan; Wang, Wenjuan; Yan, Yaguang; Zhang, Yaqi; An, Bo (Frontiers in Energy Research, 2023)
With the increasing importance of wind power systems in the renewable energy sector, random fluctuations in wind speed can affect the smooth operation of wind power systems and the stability of the power output. With the increasing importance of wind power systems in the renewable energy sector and random fluctuations in wind speed can affect the smooth operation and reliability of the active power of wind power systems. To tackle this problem, this paper takes a 5 MW horizontal axis wind turbine as the research object that proposes a parameter adaptive robust control method to achieve self-optimization of controller parameters by means of Bayesian optimization. Firstly, the wind speed and the non-linear mathematical model of the wind turbine are built. Secondly, the wind speed random fluctuations are considered as bounded disturbances and controlled using the Bayesian optimized robust model predictive controller. Thirdly, a disturbance-free nominal model of the wind turbine is introduced to solve the optimization problem to obtain the nominal input and nominal state. Then, the linear matrix inequality is solved so that a feedback controller can be designed to work in the actual wind power system. Finally, the 5 MW wind turbine model is utilized to verify the feasibility of the algorithm by combining the wind speed types commonly found in a high-altitude region in northwestern. The simulation results validate the effectiveness of the proposed scheme. The outcomes demonstrate that Bayesian optimization can significantly decrease the effects of wind speed instability. The output power increases by 1.9% on average at low wind speed and stabilizes on 5 MW at high wind speed.