Reinforcement learning to maximise wind turbine energy generation
This work addresses energy efficiency for wind power systems, but it is incremental as it applies existing reinforcement learning techniques to a specific domain.
The authors tackled the problem of optimizing wind turbine energy generation by using a reinforcement learning approach to control rotor speed, yaw angle, and blade pitch angle, resulting in the double deep Q-learning method outperforming classic PID and value iteration controls in various wind conditions, including real dynamic winds, as evidenced by higher annual energy production.
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.