Convex economic model predictive control for blade loads mitigation on wind turbines

Economic model predictive control (EMPC) has received increasing attention in the wind energy community due to its ability to trade-off economic objectives with ease. However, for wind turbine applications, inherent nonlinearities, such as from aerodynamics, pose difficulties in attaining a convex optimal control problem (OCP), by which real-time deployment is not only possible but also a globally optimal solution is guaranteed. A variable transformation can be utilized to obtain a convex OCP, where nominal variables, such as rotational speed, pitch angle, and torque, are exchanged with an alternative set in terms of power and energy. The ensuing convex EMPC (CEMPC) possesses linear dynamics, convex constraints, and concave economic objectives and has been successfully employed to address power control and tower fatigue alleviation. This work focuses on extending the blade loads mitigation aspect of the CEMPC framework by exploiting its individual pitch control (IPC) capabilities, resulting in a novel CEMPC-IPC technique. This extension is made possible by reformulating static blade and rotor moments in terms of individual blade aerodynamic powers and rotational kinetic energy of the drivetrain. The effectiveness of the proposed method is showcased in a mid-fidelity wind turbine simulation environment in various wind cases, in which comparisons with a basic CEMPC without load mitigation capability and a baseline IPC are made.

Automated summary

Economic model predictive control (EMPC) is gaining attention in the wind energy community. However, the inherent nonlinearities of wind turbines make it difficult to achieve a convex optimal control problem (OCP). This work focuses on extending the application of convex EMPC (CEMPC) to blade loads mitigation by exploiting individual pitch control (IPC) capabilities. The proposed technique is showcased in wind turbine simulations, demonstrating its effectiveness compared to other control methods.