Wind Energy Science
Wind Energy Science
WES
Articles | Volume 9, issue 11
Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-2175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 9, issue 11
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements

Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng

Viewed

Total article views: 2,293 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,621 588 84 2,293 118 167
  • HTML: 1,621
  • PDF: 588
  • XML: 84
  • Total: 2,293
  • BibTeX: 118
  • EndNote: 167
Views and downloads (calculated since 22 Mar 2024)
Cumulative views and downloads (calculated since 22 Mar 2024)

Viewed (geographical distribution)

Total article views: 2,293 (including HTML, PDF, and XML) Thereof 2,223 with geography defined and 70 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 02 Apr 2026
Download
Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
Read more
Share
Altmetrics
Final-revised paper
Preprint