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.

Frequency regulation of wind turbines can improve the stability of the power system. However, it would cause generator torque fluctuation, increasing the risk of fatigue load. Previous research works were restricted to inertial and droop responses. Less attention has been paid to striking a balance between frequency regulation and fatigue load. To overcome these, a hybrid control strategy is proposed to consider both frequency response characteristic and fatigue load mitigation. First, a small signal linearization model is built to reveal the impact of the correlation mechanism of frequency regulation on drive train torque load. Second, a multivariable cost function is constructed to optimize the proportional integral (PI) controller, which combines the total fluctuation with the dispersion of the fatigue load and frequency. Then, a hybrid controller based on PI control optimized by particle swarm optimization algorithm and active disturbance rejection control is designed to restrain rapid frequency changes as well as fatigue torque fluctuation simultaneously. Several experiments are performed to verify the significance of the proposed method under different scenarios. Compared with the existing methods, the proposed hybrid control exhibits superiority in improving frequency response and fatigue load mitigation.

In traditional wind turbine (WT) operation and maintenance, fault diagnosis and repair have long been relied on, yet the demand for continuous operation under faults persists. To address this, this study proposes a triggered fault-tolerant control framework for wind turbines with zonotope-based interval estimation. The method enhances safety from point to range estimation of FDI, reduces network traffic load via a WT load region-based adaptive event-triggered mechanism, and enables fast, robust fault diagnosis/isolation using interval residuals. A damage equivalent load (DEL)-sensitive cost term balances structural fatigue suppression while ensuring power tracking and safety constraints. Theoretically, Linear Matrix Inequality (LMI) conditions based on common quadratic Lyapunov ensure closed-loop stability and bounded observation errors, with proven interval residual fault sensitivity and triggering reliability. Numerically, on the standard NREL 5-MW WT model under multi-conditions (turbulence, faulty communication), it achieves an average power tracking accuracy of 95.56%, 28.68% fatigue suppression, and 67.40% bandwidth saving. Overall, it synergistically optimizes robust estimation, economical communication, and fatigue-aware control, providing a theoretically rigorous and experimentally validated technical framework for engineering-scale WT reliability improvement and lifespan extension.