This paper proposes an adjoint-based model predictive control for optimal energy extraction of wind farms. It employs the axial induction factor of wind turbines to influence their aerodynamic interactions through the wake. The performance index is defined here as the total power production of the wind farm over a finite prediction horizon. A medium-fidelity wind farm model is utilized to predict the inflow propagation in advance. The adjoint method is employed to solve the formulated optimization problem in a cost effective way and the first part of the optimal solution is implemented over the control horizon. This procedure is repeated at the next controller sample time providing the feedback into the optimization. The effectiveness and some key features of the proposed approach are studied for a two turbine test case through simulations.

This paper studies the active rotor load controller design for a large wind turbine via individual pitch control (IPC). A multivariable IPC is designed to reject the periodic load disturbances, in an optimal manner, by penalizing the control effort according to the pitch actuator constraints. Frequency response analysis of the well-known multi-blade coordinate (MBC) transformation describes how the rotational speed variations influence the flexible modes of the blades. Therefore, a multivariable plant is constructed in the frequency-domain, compatible with applying the disturbance rejection control approaches. Then, a mixed sensitivity H∞ optimization problem is formulated based on the obtained MIMO model. The performance of the synthesized controller is analyzed and compared with the PI-based IPC. Finally, the dynamic load mitigation of the developed controller is studied through the fatigue load analysis with a high-fidelity aeroelastic simulator. Results show a significant amount of load alleviation in return for an even lower level of the pitch activity, with respect to the PI-based IPC.