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.
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.
the Creative Commons Attribution 4.0 License.
Research article
| 
07 Mar 2025
Research article |
| 07 Mar 2025
| 07 Mar 2025
A machine-learning-based approach for active monitoring of blade pitch misalignment in wind turbines
Sabrina Milani
CORRESPONDING AUTHOR
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza L. Da Vinci 32, Milan, 20133, Italy
Jessica Leoni
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza L. Da Vinci 32, Milan, 20133, Italy
Stefano Cacciola
Dipartimento di Scienze e Tecnologie Aerospaziali (DAER), Politecnico di Milano, Via La Masa, 34, Milan, 20156, Italy
Alessandro Croce
Dipartimento di Scienze e Tecnologie Aerospaziali (DAER), Politecnico di Milano, Via La Masa, 34, Milan, 20156, Italy
Mara Tanelli
Dipartimento di Elettronica, Informazione e Bioingegneria (DEIB), Politecnico di Milano, Piazza L. Da Vinci 32, Milan, 20133, Italy
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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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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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In recent years, research has focused on the development of wind farm controllers with the aim of minimizing interactions between machines and thus improving the production of the wind farm.
In this work we have analyzed the effects of these recent technologies on a single wind turbine, with the aim of understanding the impact of these controllers on the design of the machine itself.
The analyses have shown there are non-negligible effects on some components of the wind turbine.
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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.
In this paper, we propose a novel machine-learning framework for pitch misalignment detection in...
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