Wind turbine wake detection and  characterisation utilising blade loads  and SCADA data: a generalised approach

Fojcik, Piotr; Hart, Edward; Hedevang, Emil

doi:https://doi.org/10.5194/wes-10-1943-2025

Articles | Volume 10, issue 9

https://doi.org/10.5194/wes-10-1943-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-10-1943-2025

© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 10, issue 9

Research article

|

11 Sep 2025

Research article |

| 11 Sep 2025

Wind turbine wake detection and characterisation utilising blade loads and SCADA data: a generalised approach

Piotr Fojcik, Edward Hart, and Emil Hedevang

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Final revised paper (published on 11 Sep 2025)
Preprint (discussion started on 12 Feb 2025)

Interactive discussion

Status: closed

RC1:
'Comment on wes-2025-17', Anonymous Referee #1, 05 Mar 2025

The comment was uploaded in the form of a supplement: https://wes.copernicus.org/preprints/wes-2025-17/wes-2025-17-RC1-supplement.pdf

Citation: https://doi.org/10.5194/wes-2025-17-RC1
- AC2: 'Reply on RC1', Piotr Fojcik, 14 Apr 2025
  
  Dear reviewer,
  Thank you for contributing your time to review our paper. We appreciate your constructive feedback - your comments allowed us to improve our work. Please see attached for the author's response to the comments.
  Kind Regards,
  Piotr Fojcik (on behalf of all co-authors)
  
  Citation: https://doi.org/10.5194/wes-2025-17-AC2
RC2:
'Comment on wes-2025-17', Anonymous Referee #2, 07 Mar 2025

Dear authors,
I enjoyed reviewing your relevant publication on turbine-based inflow estimation and wake sensing. An aspect I want to highlight is the deliberate consideration of dimensionality reduction accompanying the machine learning methods. My criticism mainly addresses the paper structure, presentation of results and level of discussion. I think that these aspects do not fully live up to the strong and novel methodology introduced.
Please find my comments in the attached PDF.
With kind regards

Citation: https://doi.org/10.5194/wes-2025-17-RC2
- AC1: 'Reply on RC2', Piotr Fojcik, 14 Apr 2025
  
  Dear reviewer,
  Thank you for contributing your time to review our paper. We appreciate your constructive feedback - your comments allowed us to improve our work. Please see attached for the author's response to the comments.
  Kind Regards,
  Piotr Fojcik (on behalf of all co-authors)
  
  Citation: https://doi.org/10.5194/wes-2025-17-AC1
AC3: 'Response to editor', Piotr Fojcik, 14 Apr 2025

Dear Cristina,
Thank you for handling the review process for this paper. Please see the attached PDF.
Kind regards,
Piotr Fojcik (on behalf of all co-authors)

Citation: https://doi.org/10.5194/wes-2025-17-AC3

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload

AR by Piotr Fojcik on behalf of the Authors (14 Apr 2025) Author's response Author's tracked changes Manuscript

ED: Referee Nomination & Report Request started (15 Apr 2025) by Cristina Archer

RR by Anonymous Referee #1 (29 Apr 2025)

RR by Anonymous Referee #2 (15 May 2025)

ED: Publish subject to minor revisions (review by editor) (23 May 2025) by Cristina Archer

AR by Piotr Fojcik on behalf of the Authors (01 Jun 2025) Author's response Author's tracked changes Manuscript

ED: Publish as is (16 Jun 2025) by Cristina Archer

ED: Publish as is (24 Jun 2025) by Paul Fleming (Chief editor)

AR by Piotr Fojcik on behalf of the Authors (03 Jul 2025) Manuscript

Short summary

Increasing the efficiency of wind farms can be achieved via reducing the impact of wakes – flow regions with lower wind speed occurring downwind from turbines. This work describes training and validation of a novel method for the estimation of the wake effects impacting a turbine. The results show that for most tested wind conditions, the developed model is capable of robust detection of wake presence and accurate characterisation of its properties. Further validation and improvements are planned.

Wind turbine wake detection and characterisation utilising blade loads and SCADA data: a generalised approach

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Interactive discussion

Peer review completion

Suggestions for revision or reasons for rejection

Suggestions for revision or reasons for rejection