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.

Rain-induced leading-edge erosion (LEE) of wind turbine blades (WTBs) is associated with high repair and maintenance costs. The effects of LEE can be triggered in less than 1 to 2 years for some wind turbine sites, whereas it may take several years for others. In addition, the growth of erosion may also differ for different blades and turbines operating at the same site. Hence, LEE is a site- and turbine-specific problem. In this paper, we propose a probabilistic long-term framework for assessing site-specific lifetime of a WTB coating system. Case studies are presented for 1.5 and 10 MW wind turbines, where geographic bubble charts for the leading-edge lifetime and number of repairs expected over the blade's service life are established for 31 sites in the Netherlands. The proposed framework efficiently captures the effects of spatial and orographic features of the sites and wind turbine specifications on LEE calculations. For instance, the erosion is highest at the coastal sites and for sites located at higher altitudes. In addition, erosion is faster for turbines associated with higher tip speeds, and the effects are critical for such sites where the exceedance probability for rated wind conditions are high. The study will aid in the development of efficient operation and maintenance strategies for wind farms.