Reinforcement learning controller for variable-speedwind energy conversion systems

In this paper, a reinforcement learning based adaptive critic controller is proposed for the power capture control of variable-speed wind energy conversion systems (WECSs). The control objective is to optimize the power capture from wind by tracking the maximum power curve and minimize a predefined long-term cost function in the mean time. By minimizing the long-term cost function, both the power capture and the life time of mechanical part of a wind turbine are considered as opposed to most of existing literatures. The developed controller consists of an action network and a critic network. The critic network is introduced to evaluate the performance of the action network, and learn the cost-to-go function in an online manner. The estimate of cost-to-go function is then transmitted to the action network. The action network is utilized to provide the optimal generator torque rate with the help of the estimate of cost-to-go function. Here, a two-layer neural network structure is employed for both the action and critic network. Finally, the performance of the proposed controller is evaluated on a 1.5MW three-blade wind turbine in simulating environment.