Articles | Volume 2, issue 1
Wind Energ. Sci., 2, 175–187, 2017
https://doi.org/10.5194/wes-2-175-2017
Wind Energ. Sci., 2, 175–187, 2017
https://doi.org/10.5194/wes-2-175-2017

Research article 28 Mar 2017

Research article | 28 Mar 2017

Monitoring offshore wind farm power performance with SCADA data and an advanced wake model

Niko Mittelmeier 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
ED: Reconsider after major revisions (26 Sep 2016) by Gerard J.W. van Bussel
AR by Niko Mittelmeier on behalf of the Authors (01 Nov 2016)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (27 Nov 2016) by Gerard J.W. van Bussel
RR by Anonymous Referee #3 (28 Nov 2016)
RR by Anonymous Referee #1 (09 Dec 2016)
RR by Anonymous Referee #2 (12 Dec 2016)
ED: Publish subject to minor revisions (review by editor) (22 Jan 2017) by Gerard J.W. van Bussel
AR by Niko Mittelmeier on behalf of the Authors (30 Jan 2017)  Author's response    Manuscript
ED: Publish as is (21 Feb 2017) by Gerard J.W. van Bussel
ED: Publish as is (05 Mar 2017) by Gerard J.W. van Bussel(Chief Editor)
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Short summary
Efficient detection of wind turbines operating below their expected power output and immediate corrections help maximize asset value. The method presented estimates the environmental conditions from turbine states and uses pre-calculated power lookup tables from a numeric wake model to predict the expected power output. Deviations between the expected and the measured power output are an indication of underperformance. A demonstration of the method's ability to detect underperformance is given.