Optimal control for wind turbine based on reinforcement learning

Abstract

In this conference paper, an optimal control method is designed for a variable speed wind turbine system. Due to the inherent nonlinearity of the wind turbine arising from the aerodynamic torque, a linearized model is derived to handle the system's nonlinearities. An online update cost function is created based on the resulting linearized model. The critic neural network weight vector is updated with the steepest decent algorithm to design an optimal control able to minimize the given cost function. To validate the effectiveness of the optimal control based on reinforcement learning, simulation results with varying wind speed profile for different values of learning parameters are presented.