Articles | Volume 8, issue 2
https://doi.org/10.5194/wes-8-173-2023
https://doi.org/10.5194/wes-8-173-2023
Research article
 | 
15 Feb 2023
Research article |  | 15 Feb 2023

Introducing a data-driven approach to predict site-specific leading-edge erosion from mesoscale weather simulations

Jens Visbech, Tuhfe Göçmen, Charlotte Bay Hasager, Hristo Shkalov, Morten Handberg, and Kristian Pagh Nielsen

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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-2022-55', Anonymous Referee #1, 13 Jul 2022
  • RC2: 'Comment on wes-2022-55', Anonymous Referee #2, 28 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Jens Visbech on behalf of the Authors (30 Sep 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (04 Oct 2022) by Raimund Rolfes
RR by Anonymous Referee #1 (05 Oct 2022)
RR by Anonymous Referee #2 (06 Nov 2022)
ED: Publish subject to technical corrections (15 Nov 2022) by Raimund Rolfes
ED: Publish subject to technical corrections (22 Jan 2023) by Athanasios Kolios (Chief editor)
AR by Jens Visbech on behalf of the Authors (23 Jan 2023)  Author's response   Manuscript 
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Short summary
This paper presents a data-driven framework for modeling erosion damage based on real blade inspections and mesoscale weather data. The outcome of the framework is a machine-learning-based model that can predict and/or forecast leading-edge erosion damage based on weather data and user-specified wind turbine characteristics. The model output fits directly into the damage terminology used by the industry and can therefore support site-specific maintenance planning and scheduling of repairs.
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