A new analytical model for wind farm power prediction

In this study, a new analytical approach is presented and validated to predict wind farm power production. The new model is an extension of the recently proposed by Bastankhah and Porté-Agel for a single wake. It assumes a self-similar Gaussian shape of the velocity deficit and satisfies conservatio...

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Bibliographic Details
Published in:Journal of physics. Conference series 2015-06, Vol.625 (1), p.12039
Main Authors: Niayifar, Amin, Porté-Agel, Fernando
Format: Article
Language:English
Subjects:
Large eddy simulation
Mathematical models
Physics
Self-similarity
Superposition (mathematics)
Turbulence intensity
Wind farms
Wind power
Wind turbines
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Summary:In this study, a new analytical approach is presented and validated to predict wind farm power production. The new model is an extension of the recently proposed by Bastankhah and Porté-Agel for a single wake. It assumes a self-similar Gaussian shape of the velocity deficit and satisfies conservation of mass and momentum. To estimate the velocity deficit in the wake, this model needs the local wake growth rate parameter which is calculated based on the local turbulence intensity in the wind farm. The interaction of the wakes is modeled by use of the velocity deficit superposition principle. Finally, the power curve is used to estimate the power production from the wind turbines. The wind farm model is compared to large-eddy simulation (LES) data and measurments of Horns Rev wind farm for a wide range of wind directions. Reasonable agreement between the proposed analytical model, LES data and measurments is obtained. This prediction is also found to be substantially better than the one obtained with a commonly used wind farm wake model.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/625/1/012039