Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 9
Articles | Volume 10, issue 9
https://doi.org/10.5194/wes-10-2099-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-2099-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 9
Research article
 | 
29 Sep 2025
Research article |  | 29 Sep 2025

In situ condition monitoring of floating offshorewind turbines using kurtosis anddeep-learning-based approaches

Adrien Hirvoas, Cesar Aguilera, Matthieu Perrault, Damien Desbordes, and Romain Ribault

Viewed

Total article views: 3,302 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,198 956 148 3,302 108 175
  • HTML: 2,198
  • PDF: 956
  • XML: 148
  • Total: 3,302
  • BibTeX: 108
  • EndNote: 175
Views and downloads (calculated since 15 Jan 2025)
Cumulative views and downloads (calculated since 15 Jan 2025)

Viewed (geographical distribution)

Total article views: 3,302 (including HTML, PDF, and XML) Thereof 3,146 with geography defined and 156 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Saved (final revised paper)

Latest update: 15 Oct 2026
Download
Short summary
This study compares two methods for detecting downtime in the Zefyros floating offshore wind turbine off Norway's coast. The first method uses kurtosis to find signal anomalies, while the second employs a deep-learning autoencoder to reduce and reconstruct data, identifying irregularities. Using 1 month of sensor data, the study finds the deep-learning method to be more accurate than kurtosis. This research advances the use of deep learning for effective wind turbine monitoring.
Read more
Share
Altmetrics
Final-revised paper
Preprint