Articles | Volume 9, issue 11
https://doi.org/10.5194/wes-9-2039-2024
https://doi.org/10.5194/wes-9-2039-2024
Research article
 | 
04 Nov 2024
Research article |  | 04 Nov 2024

Data-driven surrogate model for wind turbine damage equivalent load

Rad Haghi and Curran Crawford

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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-2023-157', Francisco de Nolasco Santos, 12 Jan 2024
    • AC2: 'Reply on RC1', Rad Haghi, 27 Jun 2024
    • AC3: 'Correction for Reply on RC1', Rad Haghi, 02 Jul 2024
  • RC2: 'Comment on wes-2023-157', Anonymous Referee #2, 17 Jan 2024
    • AC1: 'Reply on RC2', Rad Haghi, 26 Jun 2024

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Rad Haghi on behalf of the Authors (01 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish subject to minor revisions (review by editor) (19 Jul 2024) by Nikolay Dimitrov
AR by Rad Haghi on behalf of the Authors (28 Jul 2024)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (01 Aug 2024) by Nikolay Dimitrov
ED: Publish as is (04 Sep 2024) by Athanasios Kolios (Chief editor)
AR by Rad Haghi on behalf of the Authors (07 Sep 2024)  Manuscript 
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Short summary
This journal paper focuses on developing surrogate models for predicting the damage equivalent load (DEL) on wind turbines without needing extensive aeroelastic simulations. The study emphasizes the development of a sequential machine learning architecture for this purpose. The study also explores implementing simplified wake models and transfer learning to enhance the models' prediction capabilities in various wind conditions.
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