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

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 damage based on weather data/simulations and user-specified wind turbine characteristics. The model is based on feedforward 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.