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Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades

Alessio Castorrini, Valerio F. Barnabei, Luis Domenech, Asta Šakalyté, Fernando Sánchez and M. Sergio Campobasso

Renewable Energy, 2024, vol. 227, issue C

Abstract: Leading edge erosion of wind turbine blades is a major contributor to wind farm energy yield losses and maintenance costs. Presented is a multidisciplinary framework for predicting rain erosion lifetimes of wind turbine blades. Key aim is assessing the sensitivity of lifetime predictions to: modeling aspects (material erosion model, blade aerodynamics), input data and/or their preprocessing (joint frequency distribution of wind speed and droplet size based on synchronous site-specific measurements versus frequency distribution generated with partly site-agnostic modeling standards, wind speed records of nacelle anemometer or extrapolated at hub height from met masts), and environmental conditions (UV weathering). The analyses consider a Northwest England onshore site where a utility-scale turbine is operational, focus on a reference 5 MW turbine assumed operational at the site, and use a typical leading edge coating material. It is found that the largest variations in erosion lifetime predictions are due to material erosion model (based on rain erosion test data or fundamental material properties) and wind and rain model (measurement-based joint wind speed and droplet size distribution or standard-based modeled distribution). The use of joint wind and rain distribution also enables identifying wind/rain states with highest erosion potential, knowledge paramount to deploying erosion-safe turbine control.

Keywords: Blade leading edge erosion; Modeling of material erosion by rain; Wind speed and droplet size joint frequency distribution; Anemometer and disdrometer measurements; Coating material weathering; Wind energy (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:227:y:2024:i:c:s0960148124006141

DOI: 10.1016/j.renene.2024.120549

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