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

Contreras Lopez, Javier and Kolios, Athanasios (2024) An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion. Renewable Energy, 227. 120525. ISSN 0960-1481 (https://doi.org/10.1016/j.renene.2024.120525)

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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.

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