Design, analysis, and learning control of a fully actuated micro wind turbine

Wind power represents one of the most promising sources of renewable energy, and improvements to wind turbine design and control can have a significant impact on energy sustainability. In this paper we make two primary contributions: first, we develop and present a actuated micro wind turbine intended for research purposes. While most academic work on wind turbine control has largely focused on simulated evaluations, most turbine simulators are quite limited in their ability to model unsteady aerodynamic effects induced by the turbine; thus, there is a huge value to validating wind turbine control methods on a physical system, and the platform we present here makes this possible at a very low cost. The second contribution of this paper a novel policy search method, applied to optimizing power production in Region II wind speeds. Our method is similar in spirit to Reinforcement Learning approaches such as the REINFORCE algorithm, but explicitly models second order terms of the cost function and makes efficient use of past execution data. We evaluate this method on the physical turbine and show it it is able to quickly and repeatably achieve near-optimal power production within about a minute of execution time without an a priori dynamics model.