Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2175-2024
https://doi.org/10.5194/wes-9-2175-2024
Research article
 | 
13 Nov 2024
Research article |  | 13 Nov 2024

Machine-learning-based virtual load sensors for mooring lines using simulated motion and lidar measurements

Moritz Gräfe, Vasilis Pettas, Nikolay Dimitrov, and Po Wen Cheng

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wes-2024-25', Anonymous Referee #1, 30 Mar 2024
  • RC2: 'Comment on wes-2024-25', Anonymous Referee #2, 29 May 2024
  • AC1: 'Comment on wes-2024-25', Moritz Gräfe, 03 Jul 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Moritz Gräfe on behalf of the Authors (03 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (06 Aug 2024) by Julie Teuwen
RR by Anonymous Referee #1 (13 Aug 2024)
ED: Publish as is (15 Aug 2024) by Julie Teuwen
ED: Publish as is (04 Sep 2024) by Athanasios Kolios (Chief editor)
AR by Moritz Gräfe on behalf of the Authors (14 Sep 2024)  Manuscript 
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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.
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