High-speed winds and strong waves greatly affect the stability of floating wind turbines, leading to lower efficiency and a shorter lifespan. Thrusters and gyrostabilizers are used to help mitigate these effects.
The goal of this project was to leverage deep reinforcement learning in a simulated environment to develop a control policy that determines the optimal actions for the turbine’s thrusters and gyrostabilizers. This innovative approach enables floating offshore wind turbines to achieve greater platform stability, higher power output, and improved structural resilience in complex environmental conditions.
