Real-time optimization of wind farms using  modifier adaptation and machine learning

Andersson, Leif Erik; Imsland, Lars

doi:https://doi.org/10.5194/wes-5-885-2020

Articles | Volume 5, issue 3

https://doi.org/10.5194/wes-5-885-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Wind Energy Science Conference 2019

https://doi.org/10.5194/wes-5-885-2020

© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 5, issue 3

Research article

|

13 Jul 2020

Research article |

| 13 Jul 2020

Real-time optimization of wind farms using modifier adaptation and machine learning

Leif Erik Andersson and Lars Imsland

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Final revised paper (published on 13 Jul 2020)
Preprint (discussion started on 04 Feb 2020)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Interesting article; can be improved in presentation', Bart Doekemeijer, 25 Feb 2020
- AC1: 'Response to Referee #1: Interactive comment on “Real-time optimization of wind farms using modifier adaptation and machine learning” by Leif Erik Andersson and Lars Imsland', Leif Andersson, 14 Apr 2020
RC2: 'Review of wes-2020-18', Anonymous Referee #2, 06 Mar 2020
- AC2: 'Response to Referee #2: Interactive comment on “Real-time optimization of wind farms using modifier adaptation and machine learning” by Leif Erik Andersson and Lars Imsland', Leif Andersson, 14 Apr 2020

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Leif Andersson on behalf of the Authors (12 May 2020) Author's response Manuscript

ED: Referee Nomination & Report Request started (18 May 2020) by Carlo L. Bottasso

RR by Anonymous Referee #2 (22 May 2020)

RR by Bart M. Doekemeijer (25 May 2020)

ED: Publish as is (04 Jun 2020) by Carlo L. Bottasso

ED: Publish as is (06 Jun 2020) by Gerard J.W. van Bussel (Chief editor)

AR by Leif Andersson on behalf of the Authors (10 Jun 2020) Manuscript

Short summary

The article describes a hybrid modeling approach to optimize the energy capture of wind farms. Hybrid modeling combines mechanistic and data-driven models. The data-driven part is used to correct inaccuracies of the mechanistic model. The hybrid approach allows for adjustment of the mechanistic model beyond simple parameter estimation. It is, therefore, an attractive approach in wind farm control. The approach is illustrated in several numerical case studies.