Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wes-9-2175-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Nov 2024
Research article |
| 13 Nov 2024
| 13 Nov 2024
Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements
University of Stuttgart, Stuttgart Wind Energy (SWE), Allmandring 5b, 70569 Stuttgart, Germany
Vasilis Pettas
University of Stuttgart, Stuttgart Wind Energy (SWE), Allmandring 5b, 70569 Stuttgart, Germany
Nikolay Dimitrov
DTU Wind and Energy Systems, Technical University of Denmark, Frederiksborgvej 399, Roskilde 4000, Denmark
Po Wen Cheng
University of Stuttgart, Stuttgart Wind Energy (SWE), Allmandring 5b, 70569 Stuttgart, Germany
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Short summary
This study explores a methodology using floater motion and nacelle-based lidar wind speed measurements to estimate the tension and damage equivalent loads (DELs) on floating offshore wind turbines' mooring lines. Results indicate that fairlead tension time series and DELs can be accurately estimated from floater motion time series. Using lidar measurements as model inputs for DEL predictions leads to similar accuracies as using displacement measurements of the floater.
This study explores a methodology using floater motion and nacelle-based lidar wind speed...
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