Reinforcement learning for real-time flow control of vertical axis wind turbines

Vertical axis wind turbines present several advantages including omni-directionality and low noise production.

But the occurrence of dynamic stall during the rotation of the blades is detrimental to the performance and the life expectancy of the turbines. Closed-loop control is a solution to mitigate the effect of undesired flow features.



Here, we optimize the power generation of a reduced-scale turbine immersed in a water channel. A reinforcement learning agent is trained using the Soft Actor Critic algorithm. It performs 45 pitching action per cycle, based on real-time measurement of the loads on the blade.

The trained agent can multiply the power coefficient of the turbine by 2.5 compared to a non-pitching blade in only one hour of training.

We also provide guidelines on how to choose the reward function or the discount factor, and their effect on the shape and the periodicity of the optimal policies.

Our work shows the potential of reinforcement learning for real-time flow control on a complex fluid mechanics problem.