Wind Energy Science
Wind Energy Science
WES
Articles | Volume 11, issue 5
Articles | Volume 11, issue 5
https://doi.org/10.5194/wes-11-1791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-11-1791-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 11, issue 5
Research article
 | 
20 May 2026
Research article |  | 20 May 2026

Uniform blade pitch misalignment in wind turbines: a learning-based detection and classification approach

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

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Short summary
This work introduces a novel method to detect and quantify uniform pitch misalignment in wind turbines. This fault, where all blades are equally misaligned, is hard to detect because it causes no immediate imbalance but reduces efficiency over time. By combining physics-based features with machine learning techniques, our approach reliably identifies and quantifies this fault under various wind regime conditions, improving turbine maintenance and energy production.
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