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

Author

Listed:
  • Sara C. Pryor

    (Department of Earth and Atmospheric Sciences, Cornell University, Ithaca, NY 14853, USA)

  • Rebecca J. Barthelmie

    (Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, NY 14853, USA)

  • Jeremy Cadence

    (Wind Energy Institute of Canada, Tignish, PE C0B 2B0, Canada)

  • Ebba Dellwik

    (Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark)

  • Charlotte B. Hasager

    (Department of Wind and Energy Systems, Technical University of Denmark, 4000 Roskilde, Denmark)

  • Stephan T. Kral

    (Geophysical Institute and Bergen Offshore Wind Centre, University of Bergen, and Bjerknes Centre for Climate Research, 5007 Bergen, Norway)

  • Joachim Reuder

    (Geophysical Institute and Bergen Offshore Wind Centre, University of Bergen, and Bjerknes Centre for Climate Research, 5007 Bergen, Norway)

  • Marianne Rodgers

    (Wind Energy Institute of Canada, Tignish, PE C0B 2B0, Canada)

  • Marijn Veraart

    (Ørsted A/S, 7000 Copenhagen, Denmark)

Abstract

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.

Suggested Citation

  • Sara C. Pryor & Rebecca J. Barthelmie & Jeremy Cadence & Ebba Dellwik & Charlotte B. Hasager & Stephan T. Kral & Joachim Reuder & Marianne Rodgers & Marijn Veraart, 2022. "Atmospheric Drivers of Wind Turbine Blade Leading Edge Erosion: Review and Recommendations for Future Research," Energies, MDPI, vol. 15(22), pages 1-41, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8553-:d:973740
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    References listed on IDEAS

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    Cited by:

    1. Cheng Yang & Jun Jia & Ke He & Liang Xue & Chao Jiang & Shuangyu Liu & Bochao Zhao & Ming Wu & Haoyang Cui, 2023. "Comprehensive Analysis and Evaluation of the Operation and Maintenance of Offshore Wind Power Systems: A Survey," Energies, MDPI, vol. 16(14), pages 1-39, July.
    2. Fred Letson & Sara C. Pryor, 2023. "From Hydrometeor Size Distribution Measurements to Projections of Wind Turbine Blade Leading-Edge Erosion," Energies, MDPI, vol. 16(9), pages 1-29, May.
    3. Castorrini, Alessio & Barnabei, Valerio F. & Domenech, Luis & Šakalyté, Asta & Sánchez, Fernando & Campobasso, M. Sergio, 2024. "Impact of meteorological data factors and material characterization method on the predictions of leading edge erosion of wind turbine blades," Renewable Energy, Elsevier, vol. 227(C).
    4. Jeanie A. Aird & Rebecca J. Barthelmie & Sara C. Pryor, 2023. "Automated Quantification of Wind Turbine Blade Leading Edge Erosion from Field Images," Energies, MDPI, vol. 16(6), pages 1-23, March.

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