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A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system

Author

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  • Verma, Amrit Shankar
  • Jiang, Zhiyu
  • Caboni, Marco
  • Verhoef, Hans
  • van der Mijle Meijer, Harald
  • Castro, Saullo G.P.
  • Teuwen, Julie J.E.

Abstract

Rain-induced leading-edge erosion of wind turbine blades is associated with high repair and maintenance costs. For efficient operation and maintenance, erosion models are required that provide estimates of blade coating lifetime at a real scale. In this study, a statistical rainfall model is established that describes probabilistic distributions of rain parameters that are critical for site-specific leading-edge erosion assessment. A new droplet size distribution (DSD) is determined based on two years’ onshore rainfall data of an inland site in the Netherlands and the obtained DSD is compared with those from the literature. Joint probability distribution functions of rain intensities and droplet sizes are also established for this site as well as for a coastal site in the Netherlands. Then, the application of the proposed model is presented for a 5 MW wind turbine, where the model is combined with wind statistics along with an analytical surface fatigue model that describes lab-scale coating degradation. The expected lifetime of the blade coating is found three to four times less for the wind turbine operating at the coastal site than for the inland site - primarily due to rainfall at higher wind speeds. Further, the robustness of the proposed model is found consistent with varying data periods used for the analyses.

Suggested Citation

  • Verma, Amrit Shankar & Jiang, Zhiyu & Caboni, Marco & Verhoef, Hans & van der Mijle Meijer, Harald & Castro, Saullo G.P. & Teuwen, Julie J.E., 2021. "A probabilistic rainfall model to estimate the leading-edge lifetime of wind turbine blade coating system," Renewable Energy, Elsevier, vol. 178(C), pages 1435-1455.
    • Handle: RePEc:eee:renene:v:178:y:2021:i:c:p:1435-1455
    DOI: 10.1016/j.renene.2021.06.122
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    References listed on IDEAS

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    1. Amrit Shankar Verma & Zhiyu Jiang & Zhengru Ren & Zhen Gao & Nils Petter Vedvik, 2019. "Response-Based Assessment of Operational Limits for Mating Blades on Monopile-Type Offshore Wind Turbines," Energies, MDPI, vol. 12(10), pages 1-26, May.
    2. Hasager, C. & Vejen, F. & Bech, J.I. & Skrzypiński, W.R. & Tilg, A.-M. & Nielsen, M., 2020. "Assessment of the rain and wind climate with focus on wind turbine blade leading edge erosion rate and expected lifetime in Danish Seas," Renewable Energy, Elsevier, vol. 149(C), pages 91-102.
    3. Slot, H.M. & Gelinck, E.R.M. & Rentrop, C. & van der Heide, E., 2015. "Leading edge erosion of coated wind turbine blades: Review of coating life models," Renewable Energy, Elsevier, vol. 80(C), pages 837-848.
    4. Han, Woobeom & Kim, Jonghwa & Kim, Bumsuk, 2018. "Effects of contamination and erosion at the leading edge of blade tip airfoils on the annual energy production of wind turbines," Renewable Energy, Elsevier, vol. 115(C), pages 817-823.
    5. Sheng Dong & Chun-Shuo Jiao & Shan-Shan Tao, 2017. "Joint return probability analysis of wind speed and rainfall intensity in typhoon-affected sea area," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 86(3), pages 1193-1205, April.
    6. Herring, Robbie & Dyer, Kirsten & Martin, Ffion & Ward, Carwyn, 2019. "The increasing importance of leading edge erosion and a review of existing protection solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
    7. Hu, Weifei & Chen, Weiyi & Wang, Xiaobo & Jiang, Zhiyu & Wang, Yeqing & Verma, Amrit Shankar & Teuwen, Julie J.E., 2021. "A computational framework for coating fatigue analysis of wind turbine blades due to rain erosion," Renewable Energy, Elsevier, vol. 170(C), pages 236-250.
    8. Fraisse, Anthony & Bech, Jakob Ilsted & Borum, Kaj Kvisgaard & Fedorov, Vladimir & Frost-Jensen Johansen, Nicolai & McGugan, Malcolm & Mishnaevsky, Leon & Kusano, Yukihiro, 2018. "Impact fatigue damage of coated glass fibre reinforced polymer laminate," Renewable Energy, Elsevier, vol. 126(C), pages 1102-1112.
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    Cited by:

    1. Verma, Amrit Shankar & Yan, Jiquan & Hu, Weifei & Jiang, Zhiyu & Shi, Wei & Teuwen, Julie J.E., 2023. "A review of impact loads on composite wind turbine blades: Impact threats and classification," Renewable and Sustainable Energy Reviews, Elsevier, vol. 178(C).
    2. Hoksbergen, T.H. & Akkerman, R. & Baran, I., 2023. "Rain droplet impact stress analysis for leading edge protection coating systems for wind turbine blades," Renewable Energy, Elsevier, vol. 218(C).
    3. López, Javier Contreras & Kolios, Athanasios & Wang, Lin & Chiachio, Manuel, 2023. "A wind turbine blade leading edge rain erosion computational framework," Renewable Energy, Elsevier, vol. 203(C), pages 131-141.
    4. Alessio Castorrini & Paolo Venturini & Aldo Bonfiglioli, 2022. "Generation of Surface Maps of Erosion Resistance for Wind Turbine Blades under Rain Flows," Energies, MDPI, vol. 15(15), pages 1-14, August.
    5. Bech, Jakob Ilsted & Johansen, Nicolai Frost-Jensen & Madsen, Martin Bonde & Hannesdóttir, Ásta & Hasager, Charlotte Bay, 2022. "Experimental study on the effect of drop size in rain erosion test and on lifetime prediction of wind turbine blades," Renewable Energy, Elsevier, vol. 197(C), pages 776-789.
    6. Fang, Jianhao & Hu, Weifei & Liu, Zhenyu & Chen, Weiyi & Tan, Jianrong & Jiang, Zhiyu & Verma, Amrit Shankar, 2022. "Wind turbine rotor speed design optimization considering rain erosion based on deep reinforcement learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    7. 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).

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