Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 3
Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1007-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-1007-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 3
Research article
 | 
17 Aug 2020
Research article |  | 17 Aug 2020

A surrogate model approach for associating wind farm load variations with turbine failures

Laura Schröder, Nikolay Krasimirov Dimitrov, and David Robert Verelst

Viewed

Total article views: 4,701 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
3,743 857 101 4,701 117 82
  • HTML: 3,743
  • PDF: 857
  • XML: 101
  • Total: 4,701
  • BibTeX: 117
  • EndNote: 82
Views and downloads (calculated since 11 May 2020)
Cumulative views and downloads (calculated since 11 May 2020)

Viewed (geographical distribution)

Total article views: 4,701 (including HTML, PDF, and XML) Thereof 4,318 with geography defined and 383 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 06 Oct 2024
Download
Short summary
We suggest a methodology for correlating loads with component reliability of turbines in wind farms by combining physical modeling with machine learning. The suggested approach is demonstrated on an offshore wind farm for comparing performance, loads and lifetime estimations against recorded main bearing failures from maintenance reports. It is found that turbines positioned at the border of the wind farm with a higher expected AEP are estimated to experience earlier main bearing failures.
Read more
Altmetrics
Final-revised paper
Preprint