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

RENEWABLE ENERGY

卷:178

Article

Verma, Amrit Shankar^1,4  Jiang, Zhiyu²  Caboni, Marco³  Verhoef, Hans³  Meijer, Harald van der Mijle³  Castro, Saullo G. P.¹  Teuwen, Julie J. E.¹ 

[1] Delft Univ Technol TU Delft, Fac Aerosp Engn, NL-2629 HS Delft, Netherlands

[2] Univ Agder, Dept Engn Sci, Grimstad, Norway

[3] TNO, Westerduinweg 3, NL-1755 LE Petten, Netherlands

[4] SINTEF Ocean AS, Trondheim, Norway

关键词: wind turbine blade;    Leading-edge erosion;    Probabilistic analysis;    Analytical method;    Long term analysis;   

DOI  :  10.1016/j.renene.2021.06.122

来源: Elsevier

【 摘 要 】

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. (c) 2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

【 授权许可】

Free