20 Jul 2022
20 Jul 2022
Status: this preprint is currently under review for the journal WES.

Lifetime prediction of turbine blades using global precipitation products from satellites

Merete Badger, Haichen Zuo, Ásta Hannesdóttir, Abdalmenem Owda, and Charlotte Hasager Merete Badger et al.
  • Department of Wind and Energy Systems, Technical University of Denmark, Roskilde, 4000, Denmark

Abstract. The growing size of wind turbines leads to extremely high tip speeds when the blades are rotating. The blades are prone to leading edge erosion when raindrops hit the blades at such high speeds and blade damage will eventually affect the power production until repair or replacement of the blade is performed. Since these actions come with a high cost, it is relevant to estimate the blade lifetime for a given wind farm site prior to wind farm construction. Modelling tools for blade lifetime prediction require input time series of rainfall intensities and wind speeds in addition to a turbine-specific tip speed curve. In this paper, we investigate the suitability of satellite-based precipitation data from the Global Precipitation Measurement (GPM) Mission in the context of blade lifetime prediction. We first evaluate satellite-based rainfall intensities from the Integrated Multi-Satellite Retrievals for GPM (IMERG) final product against in situ observations at 18 weather stations located in Germany, Denmark, and Portugal. We then use the satellite and in situ rainfall intensities as input to a model for blade lifetime prediction together with the wind speeds measured at the stations. We find that blade lifetimes estimated with rainfall intensities from satellites and in situ observations are in good agreement despite the very different nature of the observation methods and the fact that IMERG products have a 30 minute temporal resolution whereas in situ stations deliver 10 minute accumulated rainfall intensities. Our results indicate that the wind speed has a large impact on the estimated blade lifetimes. Inland stations show significantly longer blade lifetimes than coastal stations, which are more exposed to high mean wind speeds. One station located in mountainous terrain shows large differences between rainfall intensities and blade lifetimes based on satellite and in situ observations. IMERG rainfall products are known to have a limited accuracy in mountainous terrain. Our analyses also confirm that IMERG overestimates light rainfall and underestimates heavy rainfall. Given that networks of in situ stations have large gaps over the oceans, there is a potential for utilizing rainfall products from satellites to estimate and map blade lifetimes. This is useful as more wind power is installed offshore including floating installations very far from the coast.

Merete Badger et al.

Status: open (until 31 Aug 2022)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CC1: 'Comment on wes-2022-59', Jackson Tan, 27 Jul 2022 reply
    • AC1: 'Reply on CC1', Merete Badger, 28 Jul 2022 reply
  • RC1: 'Comment on wes-2022-59', Kirsten Dyer, 12 Aug 2022 reply
    • AC2: 'Reply on RC1', Merete Badger, 15 Aug 2022 reply

Merete Badger et al.

Merete Badger et al.


Total article views: 188 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
146 36 6 188 2 1
  • HTML: 146
  • PDF: 36
  • XML: 6
  • Total: 188
  • BibTeX: 2
  • EndNote: 1
Views and downloads (calculated since 20 Jul 2022)
Cumulative views and downloads (calculated since 20 Jul 2022)

Viewed (geographical distribution)

Total article views: 188 (including HTML, PDF, and XML) Thereof 188 with geography defined and 0 with unknown origin.
Country # Views %
  • 1
Latest update: 15 Aug 2022
Short summary
When wind turbine blades are exposed to strong winds and heavy rainfall, they may be damaged and their efficiency reduced. The problem is most pronounced offshore where turbines are tall and the climate is harsh. Satellites provides global half-hourly rain observations. We use these rain data as input to a model for blade lifetime prediction and find that the satellite-based predictions agree well with predictions based on observations from weather stations on the ground.