Reinforcement Learning-Based Adaptive Control for Floating Wind Turbines

Research projects

Project Description:

This Research Project is part of the Aura CDT’s Innovations in Offshore Floating Wind Energy Systems Cluster.

Wind energy is a vital component of renewable energy sources, and floating offshore wind turbines (FOWTs) are gaining attention due to their potential for efficient power generation in deep waters. However, FOWTs are subjected to complex wind-wave environments, leading to challenges in maintaining power generation efficiency and structural integrity. This research proposal aims to develop an innovative reinforcement learning (RL)-based adaptive control strategy for wind turbine pitch control in FOWTs. The proposed approach will focus on simultaneously achieving power regulation and load mitigation without relying on accurate analytical models. The control strategy will utilize the Incremental Dual Heuristic Programming (IDHP) algorithm within a critic-actor RL framework, enabling real-time adaptation to changing environmental conditions and improving FOWT performance.

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