Deep Reinforcement Learning-Based Pitch Control for Floating Offshore Wind Turbines

The floating offshore wind turbine (FOWT) technology has great energy potential, however, minimizing the movement of the structure, under the combined effect of wind and waves, while ensuring maximum power extraction on all operating ranges remains a challenge. This paper proposes the design of a deep reinforcement learning (DRL) controller for FOWTs in the operating area III. To our knowledge, this is the first time that DRL-based control approach is used for this application. The proposed DRL controller is based on trust region policy optimization (TRPO) algorithm, composed of two neural networks, the actor and the critic networks, for the learning of the optimal control law. Simulation results and comparison study are provided to validate the proposed DRL controller for the 5-MW baseline ITI Barge wind turbine model on OpenFAST.