Reinforcement learning to maximise wind turbine energy generation

• URL: http://arxiv.org/abs/2402.11384v1
• Date: Sat, 17 Feb 2024 21:35:13 GMT
• Title: Reinforcement learning to maximise wind turbine energy generation
• Authors: Daniel Soler, Oscar Mari\~no, David Huergo, Mart\'in de Frutos, Esteban Ferrer
• Abstract summary: 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 real dynamic turbulent winds, showing good performance.
• Score: 0.8437187555622164
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 real dynamic turbulent winds, showing good performance. The double deep Q- learning is compared with a classic value iteration reinforcement learning control and both strategies outperform a classic PID control in all environments. Furthermore, the reinforcement learning approach is well suited to changing environments including turbulent/gusty winds, showing great adaptability. Finally, we compare all control strategies with real winds and compute the annual energy production. In this case, the double deep Q-learning algorithm also outperforms classic methodologies.

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