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

By A. Niayifar, Fernando Port'e-Agel. 2016

357 citations recorded by the source corpus.

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.

Subfields: Aerospace Engineering, Computational Mechanics, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/a9a5d49dc1ec5aba21805cd8e7405df3cc126b82

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In Aerospace Engineering

In Computational Mechanics

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Citations in the corpus

A momentum-conserving wake superposition method for wind farm power prediction (2020, methodology): “Specifically, the wake velocity deficits caused by the individual wind turbines are first computed from analytical wake models, and then superimposed to get the total wake deficit (Jensen 1983; Niayifar & Porté-Agel 2016), as sketched in figure 1.”
Wind-Turbine and Wind-Farm Flows: A Review (2019, methodology): “Niayifar and Porté-Agel (2016) used LES data to propose the following empirical linear relation to estimate k as a function of the streamwise turbulence intensity I (for 0.06 I 0.15), k ≈ α1 I + α2, (15) withα1 = 0.38 andα2 = 0.004.”