Wind Energy Science
Wind Energy Science
WES
Articles | Volume 7, issue 4
Articles | Volume 7, issue 4
https://doi.org/10.5194/wes-7-1605-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-7-1605-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 7, issue 4
Research article
 | 
03 Aug 2022
Research article |  | 03 Aug 2022

Lidar-assisted model predictive control of wind turbine fatigue via online rainflow counting considering stress history

Stefan Loew and Carlo L. Bottasso

Viewed

Total article views: 2,542 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,804 670 68 2,542 79 97
  • HTML: 1,804
  • PDF: 670
  • XML: 68
  • Total: 2,542
  • BibTeX: 79
  • EndNote: 97
Views and downloads (calculated since 25 Oct 2021)
Cumulative views and downloads (calculated since 25 Oct 2021)

Viewed (geographical distribution)

Total article views: 2,542 (including HTML, PDF, and XML) Thereof 2,471 with geography defined and 71 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 28 Oct 2025
Download
Short summary
This publication presents methods to improve the awareness and control of material fatigue for wind turbines. This is achieved by enhancing a sophisticated control algorithm which utilizes wind prediction information from a laser measurement device. The simulation results indicate that the novel algorithm significantly improves the economic performance of a wind turbine. This benefit is particularly high for situations when the prediction quality is low or the prediction time frame is short.
Read more
Share
Altmetrics
Final-revised paper
Preprint