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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Cited articles

Bastankhah, M. and Porté-Agel, F.: Experimental and theoretical study of wind turbine wakes in yawed conditions, J. Fluid Mech., 806, 506–541, 2016. a, b, c, d, e, f
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Bleeg, J., Purcell, M., Ruisi, R., and Traiger, E.: Wind Farm Blockage and the Consequences of Neglecting Its Impact on Energy Production, Energies, 11, 1609, https://doi.org/10.3390/en11061609, 2018. a, b
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Bottasso, C. L., Cacciola, S., and Iriarte, X.: Calibration of wind turbine lifting line models from rotor loads, J. Wind Eng. Ind. Aerod., 124, 29–45, https://doi.org/10.1016/j.jweia.2013.11.003, 2014a. a, b, c, d, e
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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.