Research Papers

Volumetric Three-Dimensional Wind Measurement Using a Single Mobile-Based LiDAR

[+] Author and Article Information
M. Zendehbad

Laboratory for Energy Conversion,
Department of Mechanical
and Process Engineering,
ETH Zürich,
Zürich CH-8092, Switzerland
e-mail: zendehbad@lec.mavt.ethz.ch

N. Chokani, R. S. Abhari

Laboratory for Energy Conversion,
Department of Mechanical
and Process Engineering,
ETH Zürich,
Zürich CH-8092, Switzerland

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 November 26, 2014; final manuscript received October 7, 2015; published online November 25, 2015. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 138(1), 011003 (Nov 25, 2015) (10 pages) Paper No: SOL-14-1354; doi: 10.1115/1.4031946 History: Received November 26, 2014; Revised October 07, 2015

A novel approach to measure the wind flow field in a utility-scale wind farm is described. The measurement technique uses a mobile, three-dimensional scanning LiDAR system to make successive measurements of the line-of-sight (LOS) wind speed from three different positions; from these measurements, the time-averaged three-dimensional wind velocity vectors are reconstructed. The scanning LiDAR system is installed in a custom-built vehicle in order to enable measurements of the three-dimensional wind flow field over a footprint that is larger than with a stationary scanning LiDAR system. At a given location, multiple series of plan position indicator (PPI) and velocity azimuthal display scans are made to average out turbulent fluctuations; this series is repeated at different locations across the wind farm. The limited duration of the total measurement time period yields measurements of the three-dimensional wind flow field that are unaffected by diurnal events. The approach of this novel measurement technique is first validated by comparisons to a meteorological mast and SODAR at a meteorological observatory. Then, the measurement technique is used to characterize the wake flows in two utility-scale wind farms: one in complex terrain and the other in flat terrain. The three-dimensional characteristics of the wakes are described in the measurements, and it is observed that in complex terrain the wake has a shorter downstream extent than in flat terrain. A maximum deficit in the wind speed of 20–25% is observed in the wake. The location of the maximum deficit migrates upward as the wake evolves; this upward migration is associated with an upward pitching of the wake flow. A comparison of the measurements to a semi-empirical wake model illustrates how the measurements, at full-scale Reynolds numbers, can support further development of wake models.

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Fig. 1

The mobile laboratory, windRoverII is equipped with the three-dimensional scanning LiDAR system

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Fig. 2

29 MW wind farm located in complex terrain in western Switzerland. Map of (a) land elevation, the wind direction during the measurements is shown by the arrowed line, and (b) land cover [20]. In the figure, the dashed rectangle outlines the area shown in Fig. 5.

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Fig. 3

26 MW wind farm in flat terrain in northern Germany. Map of (a) land elevation and (b) land cover [20]. The dashed white line shows the area shown in Fig. 4.

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Fig. 4

The volumetric, time-averaged measurements of the three-dimensional wind velocity are made at turbine WTG10. The dashed line shows the planform of the scanned volume. The bottom and top heights of the measurement volume are 50 m AGL and 170 m AGL, respectively. The vertical profile of wind speed shown in Fig. 12 is measured at the location of filled triangle symbol (▲).

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Fig. 5

Reference wind condition at the three measurement points that are used for the volumetric time-averaged measurements of the three-dimensional wind velocity vector

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Fig. 6

The LiDAR measurements while moving are made at the wind farm in flat terrain. During measurements, the mobile laboratory is driven along the path indicated by the dashed line.

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Fig. 7

Flowchart diagram of uncertainty propagation considering all sources of uncertainty in the measured Cartesian velocity vector

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Fig. 8

Spectrum of atmospheric fluctuations of wind speed

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Fig. 9

Effect of number of PPI scans on the RMS error in the LOS component of wind speed

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Fig. 10

Validation measurements are performed at Lindenberg Meteorological Observatory (Falkenberg)

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Fig. 11

Comparison of the LOS wind velocity measured with the mobile LiDAR to simultaneous measurements by SODAR and instrumented meteorological mast

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Fig. 12

Comparison of (a) wind speed and (b) wind direction from LiDAR 3D wind measurement to simultaneous SODAR measurement. The rms differences are 0.3 m/s and 3.4 deg, respectively.

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Fig. 13

Comparison of LiDAR measured vertical profile of axial wind speed to power law profile

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Fig. 14

Three-dimensional evolution of axial wind speed in wake

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Fig. 15

Isosurface of wind speed and streamlines in wake. Isosurface shows 95% of reference wind speed. Streamlines are released from perimeter of a circle downstream of the rotor.

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Fig. 16

Measured flow pitch angle in vertical plane at rotor centerline (lateral distance y/D = 0)

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Fig. 17

Evolution of hub-height, axial wind speed of wake, in complex terrain

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Fig. 18

Vertical profiles of axial wind speed at three positions, 1.5D, 2.5D, and 3.5D, in the wake

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Fig. 19

Comparison of measured profiles of axial wind in the wake of the ETH wake model. (a) Vertical profiles at 1.5D, (b) horizontal profiles at 1.5D, and (c) horizontal profiles at 2.5D.

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Fig. 20

Four different snapshots of LIDAR measurements, made while moving. The position of the mobile-LIDAR is shown by the filled white diamond symbol.

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Fig. 21

Evolution of LOS wind velocity of wake in flat terrain

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Fig. 22

Time histories of SCADA measurements at upstream turbine (WEA3) and downstream turbine (WEA1). The two upper lines show the hub-height wind speed and the two lower lines show the produced power.




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