Wind Turbine Aerodynamic Load Fluctuation Reduction Using Model Based Iterative Learning Control

Control of aerodynamic loads in wind turbines is a critical issue in terms of keeping them economically competitive with alternative energy sources. This paper continues the investigation of the use of Iterative Learning Control (ILC) for load control in wind turbines with smart devices on rotor blades. Smart devices controlled by ILC are used to modify the blade section aerodynamics such that the fluctuations in lift due to periodic disturbances on the blades are minimized In previous work, simple structure ILC laws were considered where the variables were chosen without the use of a model of the dynamics akin to auto-tuning design in standard control systems. This previous work demonstrated the potential of ILC in this area but, as expected, such controllers are limited in what they can deliver. This paper considers model based ILC for this application area where a Proper Orthogonal Decomposition based reduced order model of the flow is first constructed. The resulting model is used to design a norm optimal ILC scheme whose performance is evaluated in simulation.