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.

Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades / Castorrini, Alessio; Barnabei, Valerio F.; Domenech, Luis; Šakalyté, Asta; Sánchez, Fernando; Campobasso, M. Sergio. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 227:(2024). [10.1016/j.renene.2024.120549]

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

Castorrini, Alessio
Primo
;
Barnabei, Valerio F.
;
2024

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.
2024
anemometer and disdrometer measurements; blade leading edge erosion; coating material weathering; modeling of material erosion by rain; wind energy; wind speed and droplet size joint frequency distribution
01 Pubblicazione su rivista::01a Articolo in rivista
Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades / Castorrini, Alessio; Barnabei, Valerio F.; Domenech, Luis; Šakalyté, Asta; Sánchez, Fernando; Campobasso, M. Sergio. - In: RENEWABLE ENERGY. - ISSN 0960-1481. - 227:(2024). [10.1016/j.renene.2024.120549]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1710175
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