LIDAR-assisted preview controllers design for a MW-scale commercial wind turbine model

Abstract

Existing commercial wind turbine control algorithms are typically feedback only. Nacelle-based commercial light detection and ranging (LIDAR) systems, which can detect preview wind information in front of the turbine to be used in feedforward controller design, can improve wind turbine control performance compared to a baseline standard proportional-integral (PI) feedback controller. Combined feedforward and feedback collective pitch control strategies are investigated in this research for both mitigating tower fore-aft fatigue load above rated wind speed and enhancing power capture below rated wind speed. When the wind speed is above rated, we consider a collective pitch LQ-based preview control scheme that augments the existing feedback controller and uses a Kalman filter in the control loop as the observer. When the wind speed is below rated, we combine a tower foreaft feedback damping pitch controller with a feedforward controller designed through the method of Lagrange multipliers optimization. Control effectiveness verifications are conducted through FAST simulations with multiple turbulent wind cases.