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research article

Analytical Modeling of Wind Farms: A New Approach for Power Prediction

Niayifar, Amin  
•
Porte-Agel, Fernando  
2016
Energies

Wind farm power production is known to be strongly affected by turbine wake effects. The purpose of this study is to develop and test a new analytical model for the prediction of wind turbine wakes and the associated power losses in wind farms. The new model is an extension of the one recently proposed by Bastankhah and Porte-Agel for the wake of stand-alone wind turbines. It satisfies the conservation of mass and momentum and assumes a self-similar Gaussian shape of the velocity deficit. The local wake growth rate is estimated based on the local streamwise turbulence intensity. Superposition of velocity deficits is used to model the interaction of the multiple wakes. Furthermore, the power production from the wind turbines is calculated using the power curve. The performance of the new analytical wind farm model is validated against power measurements and large-eddy simulation (LES) data from the Horns Rev wind farm for a wide range of wind directions, corresponding to a variety of full-wake and partial-wake conditions. A reasonable agreement is found between the proposed analytical model, LES data, and power measurements. Compared with a commonly used wind farm wake model, the new model shows a significant improvement in the prediction of wind farm power.

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Type
research article
DOI
10.3390/en9090741
Web of Science ID

WOS:000383547900077

Author(s)
Niayifar, Amin  
•
Porte-Agel, Fernando  
Date Issued

2016

Publisher

Mdpi Ag

Published in
Energies
Volume

9

Issue

9

Start page

741

Subjects

analytical model

•

Gaussian velocity deficit

•

turbulence intensity

•

velocity deficit superposition

•

wake growth rate

•

wind farm power production

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
WIRE  
RIVER  
Available on Infoscience
October 18, 2016
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/130235
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