An optimal model predictive control based on Hammerstein model considering fatigue load reduction for wind turbines

The development of wind energy challenges to the wind power operation and maintenance for wind turbines (WTs). Previous research is usually limited to the maximum power tracking. However, because of the strong non-linearity and high uncertainty of WT, the fatigue load cannot be ignored. To address these, this paper proposes an optimal cooperative control for WTs, which is defined as Hammerstein-based model predictive control (HMPC). First, a Hammerstein structure is proposed to approximate the nonlinear static and linear dynamic behavior of WTs for linearization. Then, a model predictive control framework is designed by adjusting the generator torque and pitch angle simultaneously. Moreover, to solve the multi-objective optimization problem, a quadratic cost function is given by considering the power reference tracking, control action smoothing, and fatigue load suppression. Several simulations are conducted under different operating conditions. Compared with other MPC-based strategies, the results confirm that the proposed HMPC provides a robust dynamic response to changes in wind disturbance.