Wind Energy Science
Wind Energy Science
WES
Articles | Volume 10, issue 3
Articles | Volume 10, issue 3
https://doi.org/10.5194/wes-10-497-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-10-497-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 10, issue 3
Research article
 | 
07 Mar 2025
Research article |  | 07 Mar 2025

A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines

Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli

Viewed

Total article views: 1,314 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
561 167 586 1,314 32 28
  • HTML: 561
  • PDF: 167
  • XML: 586
  • Total: 1,314
  • BibTeX: 32
  • EndNote: 28
Views and downloads (calculated since 02 Sep 2024)
Cumulative views and downloads (calculated since 02 Sep 2024)

Viewed (geographical distribution)

Total article views: 1,314 (including HTML, PDF, and XML) Thereof 1,279 with geography defined and 35 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 08 May 2025
Download
Short summary
In this paper, we propose a novel machine-learning framework for pitch misalignment detection in wind turbines. Using a minimal set of standard sensors, our method detects misalignments as small as 0.1° and localizes the affected blades. It combines signal processing with a hierarchical classification structure and linear regression for precise severity quantification. Evaluation results validate the approach, showing notable accuracy in misalignment classification, regression, and localization.
Read more
Share
Altmetrics
Final-revised paper
Preprint