Energy Science & Engineering (May 2024)

Robust control of wind turbines to reduce wind power fluctuation

  • Minan Tang,
  • Wenjuan Wang,
  • Xiaofei Zhen,
  • Bo An,
  • Yaqi Zhang,
  • Yaguang Yan

DOI
https://doi.org/10.1002/ese3.1680
Journal volume & issue
Vol. 12, no. 5
pp. 1818 – 1834

Abstract

Read online

Abstract The wind power generation system of a 5 MW horizontal axis wind turbine has a high wind energy conversion efficiency. The proportion of installed capacity in practical production applications is increasing year on year, so that the stability of its operation becomes a central factor in determining the productivity of the wind farm in question. This paper takes a 5 MW wind turbine as the research object and proposes a parameter‐adaptive robust model predictive control method to achieve self‐optimization of controller parameters through a Bayesian optimization approach. A robust model predictive control strategy, aiming to reduce the power fluctuation while maximizing the power output, is developed in this paper to enhance the dynamic economic performance under uncertain wind speed variation. A Bayesian algorithm is used in this paper to optimize the parameters of the controller. Moreover, wind speeds are simulated using TurbSim for different turbulence intensities of 5%, 10%, and 15% turbulence. Finally, the robust model predictive control toolbox in MATLAB is designed and simulated. The results show that the operational instability of the wind energy system is overcome. Meanwhile, the robustness of the wind energy system operation is improved compared to the traditional model predictive control approach.

Keywords