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Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model

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  • Liu, Junbo
  • Cai, Chang
  • Song, Dongran
  • Zhong, Xiaohui
  • Shi, Kezhong
  • Chen, Yinpeng
  • Cheng, Shijie
  • Huang, Yupian
  • Jiang, Xue
  • Li, Qing'an

Abstract

The application of floating offshore wind turbines means better exploitation of offshore wind resources. However, the six-degree of freedom motion characteristics of floating platforms bring greater challenges to control system design. Based on the dynamic characteristics of semi-submersible floating offshore wind turbines, this paper establishes a simplified nonlinear dynamic model for control system design. On this basis, a complete framework for nonlinear model predictive control of maximum wind energy extraction for semi-submersible floating offshore wind turbines considering wind and wave disturbances is developed. Based on the previewed wind and wave, a dynamic optimization problem with both state and control constraints is constructed, considering maximum wind energy extraction and torque fluctuation. Then, an improved equilibrium optimizer is proposed to address the nonlinear non-convex dynamic optimization problems, which achieves a better trade-off between exploitation and exploration. Simulation results verify the superiority of the proposed nonlinear model predictive control framework via the improved equilibrium optimizer, and the influences of different control algorithms on the platform motion state, power coefficient, and equivalent wind speed are analyzed.

Suggested Citation

  • Liu, Junbo & Cai, Chang & Song, Dongran & Zhong, Xiaohui & Shi, Kezhong & Chen, Yinpeng & Cheng, Shijie & Huang, Yupian & Jiang, Xue & Li, Qing'an, 2024. "Nonlinear model predictive control for maximum wind energy extraction of semi-submersible floating offshore wind turbine based on simplified dynamics model," Energy, Elsevier, vol. 311(C).
    • Handle: RePEc:eee:energy:v:311:y:2024:i:c:s0360544224031323
    DOI: 10.1016/j.energy.2024.133356
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    References listed on IDEAS

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