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Research Papers

A Study of Wind Turbine Wakes in Complex Terrain Through RANS Simulation and SCADA Data

[+] Author and Article Information
Davide Astolfi

Post-Doc Department of Engineering,
University of Perugia,
Via G. Duranti 93,
Perugia 06125, Italy
e-mail: davide.astolfi@unipg.it

Francesco Castellani

Department of Engineering,
University of Perugia,
Via G. Duranti 93,
Perugia 06125, Italy
e-mail: francesco.castellani@unipg.it

Ludovico Terzi

Renvico srl Milano,
Via San Gregorio 34,
Milan 20124, Italy
e-mail: ludovico.terzi@renvico.it

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received July 6, 2017; final manuscript received December 23, 2017; published online February 20, 2018. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 140(3), 031001 (Feb 20, 2018) (9 pages) Paper No: SOL-17-1273; doi: 10.1115/1.4039093 History: Received July 06, 2017; Revised December 23, 2017

This work deals with wind turbine wakes in complex terrain. The test case is a cluster of four 2.3 MW wind turbines, sited in a very complex terrain. Their performances are studied through supervisory control and data acquisition (SCADA) data, suggesting a relevant role of the terrain in distorting the wake of the upstream turbines. The experimental evidences stimulate a deeper comprehension through numerical modeling: computational fluid dynamics (CFD) simulations are run, using the Reynolds-averaged Navier–Stokes (RANS) formulation. A novel way of elaborating the output of the simulations is proposed, providing metrics for quantifying the three-dimensional (3D) evolution of the wake. The main outcome of the numerical analysis is that the terrain distorts the wind flow so that the wake profile is severely asymmetric with respect to the lateral displacement. Further, the role of orography singularities is highlighted in dividing the wake front, thus inducing faster wake recovery with respect to flat terrain. This interpretation is confirmed by SCADA data analysis.

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Figures

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Fig. 5

T10/T12 relative speed up as a function of T11 nacelle position

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Fig. 4

The distribution of T11 nacelle positions. Filter at T10 on 270 ± 5 deg nacelle position, 8 ± 2 m/s nacelle wind speed.

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Fig. 3

The distribution of T10 nacelle positions. Filter at T10 on 270 ± 5 deg nacelle position, 8 ± 2 m/s nacelle wind speed.

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Fig. 2

The 3D layout of the cluster

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Fig. 1

The 2D layout of the cluster

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Fig. 11

Wind deficit profile as a function of lateral displacement: 2.8D downstream in flat terrain. Single wake case.

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Fig. 12

Wind deficit profile as a function of lateral displacement: 8.2D downstream in flat terrain. Single wake case.

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Fig. 13

Wind deficit profile as a function of lateral displacement: 2.8D downstream in real terrain. Single wake case.

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Fig. 6

Average power ratio between T11 and T10 turbines under different wind speed regimes (6, 8, and 10 m/s at T10 nacelle) and under different nacelle position configurations (T10 versus T10 and T11 in the 270 ± 5 deg interval). Comparison against numerical simulations with the disks oriented at 270 deg.

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Fig. 7

Flat terrain: 3D single wake evolution

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Fig. 8

Real terrain: 3D single wake evolution

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Fig. 9

Real terrain: 3D multiple wake evolution

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Fig. 10

The legend for reading Figs. 1114

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Fig. 18

The width of the single wake proceeding downstream, in units of rotor diameter D. Flat terrain.

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Fig. 19

The width of the single wake (1T) proceeding downstream, in units of rotor diameter D. Real terrain.

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Fig. 20

The width of the multiple wake (4T) proceeding downstream, in units of rotor diameter D. Real terrain.

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Fig. 14

Wind deficit profile as a function of lateral displacement: 8.2D downstream in real terrain. Single wake case.

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Fig. 15

The center of the wake under different configurations, at hub height

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Fig. 16

The center of the wake under different configurations, at hub height +0.22D

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Fig. 17

The center of the wake under different configurations, at hub height –0.22D

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