A Grouping Strategy for Reinforcement Learning-Based Collective Yaw Control of Wind Farms

Reinforcement learning (RL) algorithms are expected to become the next generation of wind farm control methods. However, as wind farms continue to grow in size, the computational complexity of collective wind farm control will exponentially increase with the growth of action and state spaces, limiting its potential in practical applications. In this Letter, we employ a RL-based wind farm control approach with multi-agent deep deterministic policy gradient to optimize the yaw manoeuvre of grouped wind turbines in wind farms. To reduce the computational complexity, the turbines in the wind farm are grouped according to the strength of the wake interaction. Meanwhile, to improve the control efficiency, each subgroup is treated as a whole and controlled by a single agent. Optimized results show that the proposed method can not only increase the power production of the wind farm but also significantly improve the control efficiency.