ITERATIVE LEARNING CONTROL FOR SMART MOTORS ON WIND TURBINES Weronika Natalia Nowicka Supervisors: Dr Bing Chu, Prof. Eric Rogers, Prof. Owen Tutty University of Southampton UKACC PhD Presentation Showcase

UKACC PhD Presentation Showcase Plan Motivation Iterative Learning Control (ILC) Current Status Conclusions and Future Work UKACC PhD Presentation Showcase

UKACC PhD Presentation Showcase Motivation As the typical size of wind turbines has increased significantly (larger and heavier blades) passive control became impossible and there exists a need of developing advanced control strategies in order to control the aerodynamic loads; There are many advanced control techniques used for wind turbines, but the knowledge about Iterative Learning Control in this field is still limited; Effective control of rotor loads would reduce maintenance, system reliability and increase component lifespan and therefore decrease the cost of energy. UKACC PhD Presentation Showcase

Iterative Learning Control ILC assumes that for systems operating in a repetitive mode the tracking error from previous trials can be stored as data in system’s memory; The data is used to improve performance in the future trials so the convergence of the error between subsequent trials is obtained. UKACC PhD Presentation Showcase

UKACC PhD Presentation Showcase Current Status Just started : Literature review; Testing simple ILC laws for computational fluid dynamics model of a blade equipped with a smart rotor. UKACC PhD Presentation Showcase

Conclusions and future work What’s next? Constructing a full state space model of the wind turbine with circulation control as a smart rotor; Designing and testing advanced Iterative Learning Controllers for obtained model. UKACC PhD Presentation Showcase