Model-free estimation of available  power using deep learning

Göçmen, Tuhfe; Meseguer Urbán, Albert; Liew, Jaime; Lio, Alan Wai Hou

doi:https://doi.org/10.5194/wes-6-111-2021

Articles | Volume 6, issue 1

https://doi.org/10.5194/wes-6-111-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/wes-6-111-2021

© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 6, issue 1

Research article

|

18 Jan 2021

Research article |

| 18 Jan 2021

Model-free estimation of available power using deep learning

Tuhfe Göçmen, Albert Meseguer Urbán, Jaime Liew, and Alan Wai Hou Lio

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Final revised paper (published on 18 Jan 2021)
Preprint (discussion started on 07 Nov 2019)

Interactive discussion

Status: closed

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

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- Supplement

SC1: 'Revision', Fausto Pedro García Márquez, 09 Mar 2020
- RC1: 'Revision', Anonymous Referee #1, 25 Mar 2020
RC2: 'Reviewer comment', Martin Felder, 25 Jun 2020
AC1: 'Response to Reviewers', Tuhfe Gocmen, 29 Jul 2020

Peer-review completion

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

AR by Tuhfe Gocmen on behalf of the Authors (29 Jul 2020) Manuscript

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)

AR by Tuhfe Gocmen on behalf of the Authors (08 Dec 2020) Manuscript

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.