Reinforcement learning to maximise wind turbine energy generation
Daniel Soler Oscar Mariño David Huergo Martín de Frutos Esteban Ferrer
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.