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

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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.
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