Articles | Volume 5, issue 3
Wind Energ. Sci., 5, 885–896, 2020
https://doi.org/10.5194/wes-5-885-2020

Special issue: Wind Energy Science Conference 2019

Wind Energ. Sci., 5, 885–896, 2020
https://doi.org/10.5194/wes-5-885-2020

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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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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)
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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.