Preprints
https://doi.org/10.5194/wes-2025-153
https://doi.org/10.5194/wes-2025-153
01 Sep 2025
 | 01 Sep 2025
Status: this preprint is currently under review for the journal WES.

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

Abstract. Maintaining wind turbines in efficient and optimal working conditions is crucial to maximize energy production and reduce unexpected downtime, especially in remote or offshore installations. Pitch misalignment is one of the most common issues affecting wind turbine performance. Our previous studies addressed the automatic detection of such fault using either signals from mechanical moments collected from the fixed and rotating reference frames. Specifically, the introduced approaches involve applying machine learning techniques to ad-hoc designed physics-based indicators, extracted from the mentioned signals, to detect the misalignment and localize the fault. Despite these approaches working effectively in case of both single or multiple blades misaligned simultaneously, conditions in which all blades are misaligned by the same quantity have not been taken into account. Unlike individual blade misalignments, this fault presents unique challenges in its detection due to the symmetrical nature of the fault, which minimizes immediate operational disruptions but gradually impacts turbine performance and energy efficiency. To also account for this condition, in this paper, we present an innovative methodology to identify and classify uniform pitch misalignment across all wind turbine blades. This issue has been scarcely explored in existing literature, leaving a critical gap in the understanding and diagnosis of uniform pitch misalignment. Extensive results conducted with linear and turbulent wind conditions prove the effectiveness of our approach at identifying and quantifying the entity of the misalignment, thus paving the way for more efficient and reliable wind turbine diagnostics.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Wind Energy Science.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
Share
Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli

Status: open (until 29 Sep 2025)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli
Sabrina Milani, Jessica Leoni, Stefano Cacciola, Alessandro Croce, and Mara Tanelli
Metrics will be available soon.
Latest update: 01 Sep 2025
Download
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.
Share
Altmetrics