Articles | Volume 6, issue 1
Wind Energ. Sci., 6, 111–129, 2021
https://doi.org/10.5194/wes-6-111-2021
Wind Energ. Sci., 6, 111–129, 2021
https://doi.org/10.5194/wes-6-111-2021

Research article 18 Jan 2021

Research article | 18 Jan 2021

Model-free estimation of available power using deep learning

Tuhfe Göçmen et al.

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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 Anna Wenzel on behalf of the Authors (06 Aug 2020)  Author's response
ED: Referee Nomination & Report Request started (29 Sep 2020) by Gerard J.W. van Bussel
RR by Martin Felder (04 Oct 2020)
RR by Fausto Pedro García Márquez (04 Dec 2020)
ED: Publish as is (04 Dec 2020) by Gerard J.W. van Bussel
ED: Publish as is (08 Dec 2020) by Joachim Peinke(Chief Editor)
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Short summary
Currently, the available power estimation is highly dependent on the pre-defined performance parameters of the turbine and the curtailment strategy followed. This paper proposes a model-free approach for a single-input dynamic estimation of the available power using RNNs. The unsteady patterns are represented by LSTM neurons, and the network is adapted to changing inflow conditions via transfer learning. Including highly turbulent flows, the validation shows easy compliance with the grid codes.