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.
Original language | English |
---|---|
Article number | 120525 |
Journal | Renewable Energy |
Volume | 227 |
Number of pages | 16 |
ISSN | 0960-1481 |
DOIs | |
Publication status | Published - 2024 |
Keywords
- Leading edge erosion
- Wind turbine blade O&M
- Blade erosion degradation
- Wind turbine O&M optimisation