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An Optimal Control Strategy Considering Fatigue Load Suppression for Wind Turbines with Soft Switch Multiple Model Predictive Control Based on Membership Functions

Author

Listed:
  • Shuhao Cheng

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Yixiao Gao

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Jia Liu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Changhao Guo

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Fang Xu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

  • Lei Fu

    (College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China)

Abstract

Model predictive control (MPC) has been proven effective in terms of cooperative control for wind turbines (WTs). Previous work was limited to segmented linearization at a specific operating point, which significantly affected the robustness of the MPC performance. Moreover, due to nonlinearity, frequent control switching would result in the instability and fluctuation of the closed-loop control system. To address these issues, this paper proposes a novel cooperative control strategy considering fatigue load suppression for wind turbines, which is named soft switch multiple model predictive control (SSMMPC). Firstly, based on the gap metric, a model bank is constructed to divide the nonlinear WT model into several linear segments. Then, the multiple MPC is designed in a wide range of operating points. To settle the control signal oscillation problem, a soft-switching rule based on the triangular–trapezoidal hybrid membership function is proposed during controller selection. Several simulations are performed to verify the effectiveness and flexibility of SSMMPC in the partial-load region and full-load region. The results confirm that the proposed SSMMPC exhibits excellent performance in both reference operating point tracking and fatigue load mitigation, especially for the main shaft torque and tower bending load.

Suggested Citation

  • Shuhao Cheng & Yixiao Gao & Jia Liu & Changhao Guo & Fang Xu & Lei Fu, 2025. "An Optimal Control Strategy Considering Fatigue Load Suppression for Wind Turbines with Soft Switch Multiple Model Predictive Control Based on Membership Functions," Energies, MDPI, vol. 18(17), pages 1-21, September.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:17:p:4695-:d:1741887
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    References listed on IDEAS

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    1. Hawari, Qusay & Kim, Taeseong & Ward, Christopher & Fleming, James, 2022. "A robust gain scheduling method for a PI collective pitch controller of multi-MW onshore wind turbines," Renewable Energy, Elsevier, vol. 192(C), pages 443-455.
    2. Tang, Shize & Tian, De & Wu, Xiaoxuan & Huang, Mingyue & Deng, Ying, 2022. "Wind turbine load reduction based on 2DoF robust individual pitch control," Renewable Energy, Elsevier, vol. 183(C), pages 28-40.
    3. Yao, Qi & Hu, Yang & Deng, Hui & Luo, Zhiling & Liu, Jizhen, 2020. "Two-degree-of-freedom active power control of megawatt wind turbine considering fatigue load optimization," Renewable Energy, Elsevier, vol. 162(C), pages 2096-2112.
    4. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
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