Wind Energy Science
Wind Energy Science
WES
Articles | Volume 6, issue 1
Articles | Volume 6, issue 1
https://doi.org/10.5194/wes-6-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-6-111-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 6, issue 1
Research article
 | 
18 Jan 2021
Research article |  | 18 Jan 2021

Model-free estimation of available power using deep learning

Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio

Viewed

Total article views: 3,020 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,049 874 97 3,020 117 110
  • HTML: 2,049
  • PDF: 874
  • XML: 97
  • Total: 3,020
  • BibTeX: 117
  • EndNote: 110
Views and downloads (calculated since 07 Nov 2019)
Cumulative views and downloads (calculated since 07 Nov 2019)

Viewed (geographical distribution)

Total article views: 3,020 (including HTML, PDF, and XML) Thereof 2,768 with geography defined and 252 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Latest update: 19 Nov 2024
Download
Short summary
Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.
Read more
Altmetrics
Final-revised paper
Preprint