Power Regulation and Load Mitigation of Floating Wind Turbines via Reinforcement Learning

Floating offshore wind turbines (FOWTs) are often subjected to heavy structural loads due to challenging operating conditions, which can negatively impact power generation and lead to structural fatigue. This paper proposes a novel reinforcement learning (RL)-based control scheme to address this issue. It combines individual pitch control (IPC) and collective pitch control (CPC) to balance two key objectives: load reduction and power regulation. Specifically, a novel incremental model-based dual heuristic programming (IDHP) strategy is developed as the IPC solution to reduce structural loads. It integrates the online-learned FOWT dynamics into the dual heuristic programming process, making the entire control scheme data-driven and free from dependence on analytical models. Furthermore, the proposed method differs from existing IDHP methods in that only partial system dynamics need to be learned, resulting in a simplified design structure and improved training efficiency. Tests using a high-fidelity FOWT simulator demonstrate the effectiveness of the proposed method. Note to Practitioners-This work achieves power regulation and load reduction simultaneously for FOWTs to guarantee the reliability of wind turbine operations. Such a task is still an open problem because existing FOWT controllers commonly rely on accurate turbine models and lack adaptability to potential uncertainties and errors in practical situations. A new data-driven, model-free control strategy based on the RL technique is developed to address these issues. Our method has the ability to capture potential changes in system dynamics by updating a so-called incremental model via online measurements. Unlike current advances in this direction that need to approximate the whole system dynamics, the proposed control algorithm only needs to update partial system information for the incremental model. This naturally simplifies the design structure and enhances learning effectiveness while providing adaptability and robustness against uncertainties and errors. The proposed control strategy can also be extended and implemented in other systems, such as autonomous systems and other renewable energy systems.