An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion

Contreras, Javier and Kolios, Athanasios (2024) An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion. Renewable Energy. 120525. ISSN 0960-1481 (https://doi.org/10.1016/j.renene.2024.120525)

[thumbnail of Contreras-Kolios-RE-2024-An-autonomous-decision-making-agent-for-offshore-wind-turbine-blades]
Preview
 Text. Filename: Contreras-Kolios-RE-2024-An-autonomous-decision-making-agent-for-offshore-wind-turbine-blades.pdf
Accepted Author Manuscript
License: Creative Commons Attribution 4.0 logo
Download (2MB)| Preview

Abstract

The increasing pressure of offshore wind developments is leading to projects being located in areas with more difficult access and greater weather barriers. As these constraints increase, O&M costs also grow in importance. Therefore, the current scenario requires a careful planning to avoid unnecessary costly maintenance decisions or unexpected failures. To overcome the problem of increasing O&M costs and difficult access, this manuscript presents an autonomous decision-making Reinforcement Learning (RL) agent to improve O&M planning for the Leading Edge Erosion (LEE) problem. The method developed in this work makes use of a linear degradation model to account for the damage progression dynamics and site-specific weather models. The RL-based agent proposed in this manuscript is able to reduce expected O&M costs in the range of 12%–21% when compared with condition-based policies.

CORE (COnnecting REpositories)