Variable speed wind turbine controller adaptation by reinforcement learning

Abstract

The control of Variable Speed Wind Turbines (VSWT) to achieve optimal balance of power generation stability and rotor angular speed is impeded by the non-linear dynamics of the turbine-wind interaction and sudden changes of wind direction and speed. Conventional approaches to design VSWT controllers are not adaptive. However, the wind shear phenomenon introduces a strongly non-stationary environment that requires adaptive control approaches with minimal human intervention, i.e. very little supervision of the adaptation process. Reinforcement Learning (RL) allows minimally supervised learning. Specifically, Actor-Critic is designed to deal with continuous valued state and action spaces. In this paper we apply an Actor-Critic RL architecture to improve the adaptation of the conventional VSWT controllers to changing wind conditions. Simulation results on a benchmark VSWT model under strongly changing wind conditions show that Actor Critic RL approach with functional approximation provide great enhancement over state-of-the-art VSWT controllers.