Output details

Iterative learning control for improved aerodynamic load performance of wind turbines with smart rotors

Significance of output:

There has been a push to larger wind turbines as they produce cheaper power. Smart rotor blades, with actuation distributed along the blade, have been proposed as a way of damping down fluctuations in load, to increase efficiency and reduce fatigue. This requires simple and robust control algorithms to link the actuation with measurements of the load on a blade section. Data driven methods which take account of the cyclic nature of the flow but also allow for non-periodic fluctuations are considered, showing that they can, in principle, reduce both the peak and fatigue loads on a blade section.