Articles | Volume 8, issue 2
https://doi.org/10.5194/wes-8-173-2023
https://doi.org/10.5194/wes-8-173-2023
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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Cited articles

Bech, J. I., Hasager, C. B., and Bak, C.: Extending the life of wind turbine blade leading edges by reducing the tip speed during extreme precipitation events, Wind Energ. Sci., 3, 729–748, https://doi.org/10.5194/wes-3-729-2018, 2018. a, b
Bech, J. I., Johansen, N. F.-J., Madsen, M. B., Hannesdóttir, Á., and Hasager, C. B.: Experimental Study on the Effect of Drop Size in Rain Erosion Test and on Lifetime Prediction of Wind Turbine Blades, available at SSRN 4011160, https://doi.org/10.1016/j.renene.2022.06.127, 2022.  a
Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W., Gleeson, E., Sass, B. H., Homleid, M., Hortal, M., Ivarsson, K.-I., Lenderink, G., Niemelä, S., Nielsen, K. P., Onvlee, J., Rontu, L., Samuelsson, P., Santos Muñoz, D., Subias, A., Tijm, S., Toll, V., Yang, X., and Køltzow, M. Ø.: The HARMONIE–AROME Model Configuration in the ALADIN–HIRLAM NWP System, Mon. Weather Rev., 145, 1919–1935, https://doi.org/10.1175/MWR-D-16-0417.1, 2017. a
Best, A.: The size distribution of raindrops, Q. J. Roy. Meteorol. Soc., 76, 16–36, 1950. a
Bonab, H. R. and Can, F.: A theoretical framework on the ideal number of classifiers for online ensembles in data streams, in: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, 24–28 October 2016, Indianapolis, IN, USA, 2053–2056, https://doi.org/10.1145/2983323.2983907, 2016. a
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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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