Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 2
Articles | Volume 8, issue 2
https://doi.org/10.5194/wes-8-173-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-173-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 2
Research article
 | 
15 Feb 2023
Research article |  | 15 Feb 2023

Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations

Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen

Viewed

Total article views: 4,238 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,977 1,166 95 4,238 127 180
  • HTML: 2,977
  • PDF: 1,166
  • XML: 95
  • Total: 4,238
  • BibTeX: 127
  • EndNote: 180
Views and downloads (calculated since 20 Jun 2022)
Cumulative views and downloads (calculated since 20 Jun 2022)

Viewed (geographical distribution)

Total article views: 4,238 (including HTML, PDF, and XML) Thereof 4,135 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 05 Jun 2026
Download
Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
Read more
Share
Altmetrics
Final-revised paper
Preprint