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

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  • Lopez, Javier Contreras
  • Kolios, Athanasios

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.

Suggested Citation

  • Lopez, Javier Contreras & Kolios, Athanasios, 2024. "An autonomous decision-making agent for offshore wind turbine blades under leading edge erosion," Renewable Energy, Elsevier, vol. 227(C).
    • Handle: RePEc:eee:renene:v:227:y:2024:i:c:s0960148124005901
    DOI: 10.1016/j.renene.2024.120525
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    References listed on IDEAS

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