Improving Floating Offshore Wind Farm Flow Control With Scalable Model-Based Deep Reinforcement Learning

This paper proposes a model-based deep reinforcement learning (DRL) framework to maximize the total power output and minimize the fatigue load of a floating offshore wind farm subject to wake effect. Recognizing the extensive interactions required for the DRL training, we first develop an open-source physics-based model that describes the time-averaged dynamics of the floating wind farm. This model is designed with sufficient fidelity to support the wind farm control and high computational efficiency to facilitate DRL training. Subsequently, a model-based DRL approach is proposed, featuring simultaneous learning of system dynamics and optimal control policies. This dual learning process enhances the scalability of the DRL agent, making the framework suitable for large-scale floating wind farms. Finally, the effectiveness of the proposed scheme is validated by case studies with a dynamic floating wind farm simulator FAST.Farm.