Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research

• Published in: Energies (Basel) Vol. 15; no. 22; p. 8553
• Main Authors: Pryor, Sara C., Barthelmie, Rebecca J., Cadence, Jeremy, Dellwik, Ebba, Hasager, Charlotte B., Kral, Stephan T., Reuder, Joachim, Rodgers, Marianne, Veraart, Marijn
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-11-2022; MDPI
• Subjects: Aerodynamics; Air-turbines; blade reliability; Blades; Corrosion and anti-corrosives; Datasets; droplet size distributions; Energy; Energy transfer; Environmental aspects; erosion; hail; Hydrometeors; Kinetic energy; kinetic energy transfer; Laboratories; Leading edges; Maintenance costs; metrology; Mitigation; Morphology; Precipitation; Rainfall; Rainfall intensity; Rotation; Sea level; Thermal cycling; Turbine blades; Turbines; Velocity; WIND ENERGY; Wind power; Wind speed; wind turbines; United States
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15228553

Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.