Offshore wind power generation system control using robust economic MPC scheme

Ma, Lele; Kong, Xiaobing; Liu, Xiangjie; Abdelbaky, Mohamed Abdelkarim; Besheer, Ahmad H.; Wang, Mingyu; Lee, Kwang Y.

doi:10.1016/j.oceaneng.2023.115178

Cited by 7 publications

(1 citation statement)

References 33 publications

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“…In recent years, there has been an increasing amount of research on RMPC for WPGS, mainly focusing on dynamic response control of wind power mechanical systems, intelligent scheduling methods for WPGS, and wind farm scheduling optimization 20–22 . A robust EMPC strategy was proposed to achieve maximizing power output while reducing damage to the turbine, improving the dynamic economic performance under uncertain wind speed variation conditions 23 . Li et al proposed an RMPC controller that takes into account the uncertainty and nonlinearity, which improves the stability of the control system 24 .…”

Section: Introductionmentioning

confidence: 99%

Robust control of wind turbines to reduce wind power fluctuation

Tang,

Wang,

Zhen

et al. 2024

Energy Science & Engineering

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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.

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Section: Introductionmentioning

confidence: 99%