Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations
In one sentence. Feed-forward neural networks with ensemble learning predict wind turbine leading-edge erosion from mesoscale NWP weather data and blade inspections in Northern Europe.
Abstract
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.
Labels
- Topic
- Applications & impacts
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.5194/wes-8-173-2023 OpenAlex:W4320916079
- Added
- 2026-10-22
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