Optimal control of a wind farm in time-varying wind using deep reinforcement learning

A deep-reinforcement-learning (DRL) based control method to take the advantage of complex wake interactions in a wind farm is developed. Although the wind over a wind farm is changing, steady wind has been assumed in the most conventional methods for wind farm control. Under unsteady wind, the generated power of a wind farm becomes stochastic due to intermittent and fluctuating wind. To tackle the difficulty, a DRL-based method with which the pitch and yaw angles of wind turbines in a wind farm are strategically controlled is developed. Time-histories of the past wind and the predicted future wind are both utilized to identify the relation between the generated power and control. The present neural network is trained and validated using an experimental wind farm. A multi-fan wind tunnel is developed to generate unsteady wind for experiments with miniature wind farms, where the improvement in the generated power by the present DRL-based control method is demonstrated.