Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 4
Articles | Volume 11, issue 4
https://doi.org/10.5194/wes-11-1185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1185-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 4
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

Viewed

Total article views: 951 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
528 387 36 951 31 65
  • HTML: 528
  • PDF: 387
  • XML: 36
  • Total: 951
  • BibTeX: 31
  • EndNote: 65
Views and downloads (calculated since 07 Nov 2025)
Cumulative views and downloads (calculated since 07 Nov 2025)

Viewed (geographical distribution)

Total article views: 951 (including HTML, PDF, and XML) Thereof 919 with geography defined and 32 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 13 Apr 2026
Download
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint