Lidar-assisted feedforward and feedback control design for wind turbine tower load mitigation and power capture enhancement

In the wind industry, research needs exist in the areas of reduction of wind turbine structural loads and maximization of wind energy capture in order to reduce the cost of wind energy. For modern large scale variable-speed variable-pitch wind turbines, these goals can be achieved via the use of modern controllers. Current commercial wind turbine control algorithms are typically feedback only and operate on a feedback signal such as the error in rotor speed or power out of the turbine. Recent advances in light detection and ranging (LIDAR) systems, which can provide realtime upcoming wind speed or direction measurements in front of the turbine using lasers, open a new area of research in feedforward wind turbine control. Feedforward controllers that use preview wind measurements can compensate for the effect of wind disturbances on rotor speed and turbine structural components (blades, tower, shaft, etc.). Feedback controllers can be augmented with these feedforward control strategies to improve turbine performance compared to feedback only controllers. In the dissertation, a number of combined feedforward and feedback control designs are proposed and developed for use with the Alstom ECO-100 3 MW turbine and the 600 kW controls advanced research turbine (CART3) at the National Renewable Energy Laboratory's (NREL) National Wind Technology Center (NWTC). Two research directions of wind turbine performance are pursued using LIDAR-enabled feedforward and feedback control designs: mitigating each turbine's fatigue loads and improving each turbine's energy production. For mitigating fatigue loads, an adaptive feedforward controller based on a filtered-x recursive least squares (FX-RLS) algorithm has been designed to augment a predesigned collective pitch feedback controller for the CART3. Adaptive control has the potential to overcome some of the drawbacks of linear time-invariant (LTI) control, because the control law in this case can be updated at every time step according to the wind input conditions. For the Alstom ECO-100, a collective pitch LQ-based preview control scheme that augments the existing feedback controller has been designed with a Kalman filter in the control loop as the observer. The LQ-based preview control strategy is a scheme to synthesize closed-loop controllers including both a feedforward term and a feedback term for LTI systems during tracking of previewed wind inputs by minimizing a defined output error. This design results in two feedback control loops: one is the baseline pitch control loop for speed regulation; the other is solved from the optimal preview control strategy for tower fore-aft fatigue load mitigation. Regarding maximizing energy production, two advanced LIDAR-enabled torque con- trollers have been developed for the CART3: 1) disturbance tracking control (DTC) augmented with LIDAR, where the wind speed estimator is replaced with the LIDAR measurement in the hopes of improving accuracy, but the feedback gain is kept at the same value as when the wind speed is estimated, and 2) optimally tracking rotor (OTR) control augmented with LIDAR, where we use a LIDAR preview measurement to provide the potential aerodynamic torque to give the rotor additional acceleration and deceleration. For the Alstom ECO-100, a nonlinear and a linear below rated feedforward torque controllers are designed to alleviate the effect from wind disturbance on rotor speed. The nonlinear feedforward torque term is designed according to the wind measurement that can predict the required future torque command to regulate rotor speed and the linear feedforward torque controller according to disturbance accommodating control (DAC) has one benefit over the nonlinear strategy of not requiring the feedback signal. Also, a tower fore-aft feedback damping pitch controller combined with a feedforward pitch controller designed through the method of Lagrange multipliers optimization has been developed in this research. Below rated feedforward pitch control strategy could assist the turbine to regain the optimal power coefficient values without increasing the thrust coefficient. The proposed LIDAR-enabled controllers are evaluated in simulation with the full nonlinear turbine models and numerous stochastic turbulent wind conditions. The control effectiveness is evaluated by comparing to a feedback only controller for tower fore-aft and side-to-side bending moments, blade flapwise and edgewise bending moments, low speed shaft torsional load, averaged power, rotor speed regulation and required control authority.