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

Shuhao Cheng, Yixiao Gao, Jia Liu, Changhao Guo, Fang Xu and Lei Fu ()
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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

Energies, 2025, vol. 18, issue 17, 1-21

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.

Keywords: wind turbine; fatigue load suppression; soft switch; model predictive control (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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