Reducing aerodynamic load fluctuation in wind turbines using iterative learning control laws designed using reduced order models of the flow

Developments in actuators and sensors have led to considerable interest in their use for aerodynamic load control for wind turbines, thereby increasing power extraction efficiency, including economic competitiveness against other sources of alternative energy. In particular, the route is to embed smart devices into the rotor blades and use them in combination with active control to modify the blade section aerodynamics, aiming to minimize lift fluctuations due to disturbances. Previous research has shown that iterative learning control can be used in this area, starting with model-free designs and proceeding to model-based designs. This paper uses proper orthogonal decomposition to construct finite-dimensional models from the computational fluid dynamics-based representations of the defining partial differential equations. The performance of the resulting control laws is examined using the computational fluid dynamics representation of the dynamics