Wind Energy Science
Wind Energy Science
Wind Energy Science
Articles | Volume 5, issue 2
Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 647–673, 2020
https://doi.org/10.5194/wes-5-647-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Wind Energ. Sci., 5, 647–673, 2020
https://doi.org/10.5194/wes-5-647-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 5, issue 2
Research article
27 May 2020
Research article | 27 May 2020

Improving wind farm flow models by learning from operational data

Johannes Schreiber et al.

Viewed

Total article views: 2,394 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,556 795 43 2,394 52 35
  • HTML: 1,556
  • PDF: 795
  • XML: 43
  • Total: 2,394
  • BibTeX: 52
  • EndNote: 35
Views and downloads (calculated since 02 Dec 2019)
Cumulative views and downloads (calculated since 02 Dec 2019)

Viewed (geographical distribution)

Total article views: 1,906 (including HTML, PDF, and XML) Thereof 1,838 with geography defined and 68 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 29 Jun 2022
Download
Short summary
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are learned from operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.
Read more