A Distributed Reinforcement Learning Yaw Control Approach for Wind Farm Energy Capture Maximization

In this paper, we present a reinforcement-learning-based distributed approach to wind farm energy capture maximization using yaw-based wake steering. In order to maximize the power output of a wind farm, individual turbines can use yaw misalignment to deflect their wakes away from downstream turbines. Although using model-based methods to achieve yaw misalignment is one option, a model-free method might be better suited to incorporate changing conditions and uncertainty. We propose an algorithm that adapts concepts of temporal difference reinforcement learning distributed to a multiagent environment that empowers individual turbines to optimize overall wind farm output and react to unforeseen disturbances.