Preprints
https://doi.org/10.5194/wes-2022-55
https://doi.org/10.5194/wes-2022-55
 
20 Jun 2022
20 Jun 2022
Status: this preprint is currently under review for the journal WES.

Introducing a data-driven approach to predict site-specific leading edge erosion

Jens Visbech1, Tuhfe Göçmen1, Charlotte Bay Hasager1, Hristo Shkalov2, Morten Handberg2, and Kristian Pagh Nielsen3 Jens Visbech et al.
  • 1Department of Wind and Energy Systems, Technical University of Denmark (DTU), 4000 Roskilde, Denmark
  • 2Wind Power LAB, 1150 Copenhagen, Denmark
  • 3Danish Meteorological Institute (DMI), 2100 Copenhagen, Denmark

Abstract. Modeling leading edge erosion has been a challenging task due to its multidisciplinary nature involving several variables such as weather conditions, blade coating properties, and operational characteristics. While the process of wind turbine blade erosion is often described by engineering models that rely on the well-known Springer model, there is a glaring need for modeling approaches supported by field data. This paper presents a data-driven framework for modeling erosion damage based on blade inspections from several wind farms in Northern Europe and mesoscale numerical weather prediction (NWP) models. The outcome of the framework is a machine-learning based model that can be used to predict and/or forecast leading edge erosion damages based on weather data/simulations and user-specified wind turbine characteristics. The model is based on feed-forward artificial neural networks utilizing ensemble learning for robust training and validation. The model output fits directly into the damage terminology used by industry and can therefore support site-specific planning and scheduling of repairs as well as budgeting of operation and maintenance costs.

Jens Visbech et al.

Status: open (until 01 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Jens Visbech et al.

Jens Visbech et al.

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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 damages based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by industry and can therefore support site-specific maintenance planning and scheduling of repairs.