Reinforcement learning to maximise wind turbine energy generation

arXiv:2402.11384v1 Announce Type: new
Abstract: We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent is coupled with a blade element momentum model and is trained to allow control for changing winds. The agent is trained to decide the best control (speed, yaw, pitch) for simple steady winds and is subsequently challenged with …

abstract agent arxiv blade control cs.lg element energy experience math.mp math.oc math-ph pitch q-learning reinforcement reinforcement learning speed strategy type wind