Wind Energy Science
Wind Energy Science
WES
Articles | Volume 5, issue 3
Articles | Volume 5, issue 3
https://doi.org/10.5194/wes-5-1155-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-5-1155-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 3
Research article
 | 
04 Sep 2020
Research article |  | 04 Sep 2020

Augmented Kalman filter with a reduced mechanical model to estimate tower loads on a land-based wind turbine: a step towards digital-twin simulations

Emmanuel Branlard, Dylan Giardina, and Cameron S. D. Brown

Viewed

Total article views: 4,089 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,822 1,155 112 4,089 148 145
  • HTML: 2,822
  • PDF: 1,155
  • XML: 112
  • Total: 4,089
  • BibTeX: 148
  • EndNote: 145
Views and downloads (calculated since 13 Mar 2020)
Cumulative views and downloads (calculated since 13 Mar 2020)

Viewed (geographical distribution)

Total article views: 4,089 (including HTML, PDF, and XML) Thereof 3,745 with geography defined and 344 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 07 Nov 2025
Download
Short summary
The paper presents an application of the Kalman filtering technique to estimate loads on a wind turbine. The approach combines a mechanical model and a set of measurements to estimate signals that are not available in the measurements, such as wind speed, thrust, tower position, and tower loads. The model is severalfold faster than real time and is intended to be run online, for instance, to evaluate real-time fatigue life consumption of a field turbine using a digital twin.
Read more
Share
Altmetrics
Final-revised paper
Preprint