Posters

Abstract

The study focuses on how satellite-based rain data can be utilized for estimation of the environmental conditions causing leading edge erosion of wind turbine blades. The satellite data is from the Global Precipitation Measurement (GPM) Mission. The data product we analyze is the Integrated Multi-Satellite Retrievals for GPM (IMERG) final run, i.e. the data set with the best quality. The steps done in our study are 1) evaluation of the satellite-based rainfall intensities comparing to ground-based rain observations from weather stations, 2) applied use of the satellite-based rain data in modelling of expected damage progress at hypothetical wind turbines located at the same sites as the weather stations, 3) comparison of results using satellite-based and ground-based rain data for the blade lifetime assessment. The rainfall intensity time-series from 2014 to 2019 of 30 minute data from IMERG and 10 minute data from 18 weather stations are analyzed at daily, monthly and annual time scales. It is found that daily, monthly and inter-annual rain patterns are recognizable in both data sets. Dependent upon site (coastal, inland, mountain) some statistical variation and mainly positive bias exist. For all sites it is found that light rainfall is overestimated and heavy rainfall is underestimated from IMERG compared to weather stations. Interestingly, the blade lifetime predictions are fairly similar using IMERG or weather station data for all sites, except for a mountain site where IMERG fails to detect rain well. Similar to other research studies, it is found that turbines at coastal stations suffer shorter blade lifetimes than at inland stations. This is due to a combination of rain events during times with high wind speeds occurring more frequently at coastal stations. Thus we conclude the wind regimes are important. IMERG covers the globe and can provide data for offshore sites.