Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1185-2026
https://doi.org/10.5194/wes-11-1185-2026
Review article
 | 
13 Apr 2026
Review article |  | 13 Apr 2026

Review of deep reinforcement learning for offshore wind farm maintenance planning

Marco Borsotti, Xiaoli Jiang, and Rudy R. Negenborn

Cited articles

Aafif, Y., Chelbi, A., Mifdal, L., Dellagi, S., and Majdouline, I.: Optimal preventive maintenance strategies for a wind turbine gearbox, Energy Reports, 8, 803–814, https://doi.org/10.1016/j.egyr.2022.07.084, 2022. a
Abbas, A.: A Hierarchical Framework for Interpretable, Safe, and Specialised Deep Reinforcement Learning, Doctoral thesis, Technological University Dublin, https://doi.org/10.21427/p05p-az54, 2024. a, b
Abbas, A. N., Chasparis, G. C., and Kelleher, J. D.: Hierarchical framework for interpretable and specialized deep reinforcement learning-based predictive maintenance, Data Knowl. Eng., 149, 102240, https://doi.org/10.1016/j.datak.2023.102240, 2024. a, b, c, d
Abkar, M., Zehtabiyan-Rezaie, N., and Iosifidis, A.: Reinforcement learning for wind farm flow control: Current state and future actions, Renew. Energ., 205, 271–289, https://doi.org/10.1016/j.renene.2023.01.001, 2023. a
Adadi, A. and Berrada, M.: Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI), IEEE Access, https://doi.org/10.1109/ACCESS.2018.2870052, 2018. a
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Short summary
This study explores how artificial intelligence can improve the maintenance of offshore wind farms. By reviewing recent research, we show that learning-based methods can predict failures, plan repairs more efficiently, and reduce costs compared to traditional strategies. We identify key gaps in current approaches and suggest ways to make these models more realistic and practical for real-world wind energy operations.
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