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Technical Brief

Analysis of Wind Turbine Wakes Through Time-Resolved and SCADA Data of an Onshore Wind Farm

[+] Author and Article Information
Francesco Castellani

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

Paolo Sdringola

Department of Engineering,
University of Perugia,
via G. Duranti 93,
Perugia 06125, Italy
e-mail: paolo.sdringola@unipg.it

Davide Astolfi

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

1Corresponding author.

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 September 13, 2017; final manuscript received January 30, 2018; published online March 13, 2018. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 140(4), 044501 (Mar 13, 2018) (6 pages) Paper No: SOL-17-1382; doi: 10.1115/1.4039347 History: Received September 13, 2017; Revised January 30, 2018

An experimental study is conducted on wind turbine wakes and their effects on wind turbine performances and operation. The test case is a wind farm located on a moderately complex terrain, featuring four turbines with 2 MW of rated power each. Two interturbine distances characterize the layout: 4 and 7.5 rotor diameters. Therefore, it is possible to study different levels of wake recovery. The processed data are twofold: time-resolved series, whose frequency is in the order of the hertz, and supervisory control and data acquisition (SCADA) data with 10 min of sampling time. The wake fluctuations are investigated adopting a “slow” point of view (SCADA), on a catalog of wake events spanned over a long period, and a “fast” point of view of selected time-resolved series of wake events. The power ratios between downstream and upstream wind turbines show that the time-resolved data are characterized by a wider range of fluctuations with respect to the SCADA. Moreover, spectral properties are assessed on the basis of time-resolved data. The combination of meandering wind and yaw control is observed to be associated with different spectral properties depending on the level of wake recovery.

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Figures

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Fig. 1

The layout of the test-case wind farm with turbines distance in rotor diameters and the relative waked direction of minimum distance

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Fig. 2

Wind direction rose at turbine T01, measured from the SCADA data

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Fig. 3

Turbulence intensity rose at turbine T01, measured from the SCADA data

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Fig. 4

Average wind turbines power curve. SCADA data are averaged on wind speed interval having 0.5 m/s of amplitude.

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Fig. 5

Wind farm efficiency from SCADA data: data filtered on wind intensity ≤10 m/s

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Fig. 6

Wind farm efficiency from time resolved FW series

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Fig. 7

Wind farm efficiency form time resolved MW series

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Fig. 8

Distribution of power ratios (PT01/PT03): time-resolved versus SCADA, FW time series

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Fig. 9

Distribution of power ratios (PT03/PT02): time-resolved versus SCADA, MW time series

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Fig. 10

PSD of nacelle position: upstream T03 and downstream T01 wind turbines, FW time series

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Fig. 11

PSD of power production: upstream T03 and downstream T01 wind turbines, FW time series

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Fig. 12

PSD of nacelle position: upstream T02 and downstream T03 wind turbines, MW time series

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Fig. 13

PSD of power production: upstream T02 and downstream T03 wind turbines, MW time series

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