Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 6
Articles | Volume 7, issue 6
https://doi.org/10.5194/wes-7-2497-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-2497-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 6
Research article
 | 
20 Dec 2022
Research article |  | 20 Dec 2022

Lifetime prediction of turbine blades using global precipitation products from satellites

Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager

Viewed

Total article views: 3,371 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
2,419 866 86 3,371 138 110 162
  • HTML: 2,419
  • PDF: 866
  • XML: 86
  • Total: 3,371
  • Supplement: 138
  • BibTeX: 110
  • EndNote: 162
Views and downloads (calculated since 20 Jul 2022)
Cumulative views and downloads (calculated since 20 Jul 2022)

Viewed (geographical distribution)

Total article views: 3,371 (including HTML, PDF, and XML) Thereof 3,268 with geography defined and 103 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved (final revised paper)

Latest update: 06 May 2026
Download
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provide global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.
Read more
Share
Altmetrics
Final-revised paper
Preprint