Articles | Volume 5, issue 2
Wind Energ. Sci., 5, 647–673, 2020
https://doi.org/10.5194/wes-5-647-2020
Wind Energ. Sci., 5, 647–673, 2020
https://doi.org/10.5194/wes-5-647-2020

Research article 27 May 2020

Research article | 27 May 2020

Improving wind farm flow models by learning from operational data

Johannes Schreiber et al.

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AR: Author's response | RR: Referee report | ED: Editor decision
AR by Anna Mirena Feist-Polner on behalf of the Authors (01 Apr 2020)  Author's response
ED: Referee Nomination & Report Request started (09 Apr 2020) by Alessandro Croce
RR by Anonymous Referee #2 (13 Apr 2020)
ED: Publish as is (20 Apr 2020) by Alessandro Croce
ED: Publish as is (20 Apr 2020) by Joachim Peinke(Chief Editor)
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Short summary
The paper describes a new method that uses standard historical operational data and reconstructs the flow at the rotor disk of each turbine in a wind farm. The method is based on a baseline wind farm flow and wake model, augmented with error terms that are learned from operational data using an ad hoc system identification approach. Both wind tunnel experiments and real data from a wind farm at a complex terrain site are used to show the capabilities of the new method.