Wind Energy Science
Wind Energy Science
WES
Articles | Volume 8, issue 2
Articles | Volume 8, issue 2
https://doi.org/10.5194/wes-8-173-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-8-173-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 8, issue 2
Research article
 | 
15 Feb 2023
Research article |  | 15 Feb 2023

Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations

Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen

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Latest update: 05 Oct 2024
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Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
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