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.

References

1.
GWEC
,
2014
, “
Global Wind Report, Annual Market Update 2014
,”
Global Wind Energy Council
, Brussels, Belgium.
2.
Barthelmie
,
R. J.
,
Jensen
,
L. E.
,
Frandsen
,
S. T.
,
Nielsen
,
M. N.
,
Pryor
,
S. C.
,
Rethore
,
P. E.
, and
Jorgensen
,
H. E.
,
2007
, “
Modelling and Measurements of Power Losses and Turbulence Intensity in Wind Turbine Wakes at Middelgrunden Offshore Wind Farm
,”
Wind Energy
,
10
(
6
), pp.
517
528
.
3.
Sanderse
,
B.
,
2009
, “
Aerodynamics of Wind Turbine Wakes
,”
ECN
Wind Energy, Petten, The Netherlands, Technical Report No. ECN-E-09-016.
4.
Jafari
,
S.
,
Chokani
,
N.
, and
Abhari
,
R. S.
,
2014
, “
Simulation of Wake Interactions in Wind Farms Using an Immersed Wind Turbine Model
,”
ASME J. Turbomach.
,
136
(
6
), p.
061018
.
5.
Lanzafame
,
R.
,
Mauro
,
S.
, and
Messina
,
M.
,
2013
, “
Wind Turbine CFD Modeling Using a Correlation-Based Transitional Model
,”
Renewable Energy
,
52
, pp.
31
39
.
6.
Stevens
,
R. J. A. M.
,
Graham
,
J.
, and
Meneveau
,
C.
,
2014
, “
A Concurrent Precursor Inflow Method for Large Eddy Simulation and Applications to Finite Length Wind Farms
,”
Renewable Energy
,
68
, pp.
46
50
.
7.
Singh
,
A.
,
Giannoulakis
,
S.
,
Chokani
,
N.
, and
Abhari
,
R. S.
,
2013
, “
Large-Area Identification of Wind Farms and Optimization of Turbine Layout
,”
European Wind Energy Conference (EWEA2013)
,
Vienna, Austria
, Feb. 4–7, Paper No. 527.
8.
Gonzalez
,
J. S.
,
Rodrigues
,
A. G. G.
,
Mora
,
J. C.
,
Santos
,
J. R.
, and
Payan
,
M. B.
,
2010
, “
Optimization of Wind Farm Turbines Layout Using an Evolutive Algorithm
,”
Renewable Energy
,
35
(
8
), pp.
1671
1681
.
9.
Eroglu
,
Y.
, and
Seckiner
,
S. U.
,
2013
, “
Wind Farm Layout Optimization Using Particle Filtering Approach
,”
Renewable Energy
,
58
, pp.
95
107
.
10.
Perez
,
B.
,
Minguez
,
R.
, and
Guanche
,
R.
,
2013
, “
Offshore Wind Farm Layout Optimization Using Mathematical Programming Techniques
,”
Renewable Energy
,
53
, pp.
389
399
.
11.
Mikkelsen
,
T.
,
Knudsen
,
S.
,
Sjoholm
,
M.
,
Angelou
,
N.
, and
Tegrmeier
,
A.
,
2012
, “
WindScanner.eu—A New Remote Sensing Research Infrastructure for On- and Offshore Wind Energy
,”
International Conference on Wind Energy: Materials
,
Engineering and Policies (WEMEP2012), Hyderabad
,
India
, Nov. 22–23, Paper No. KNL-3.
12.
Mann
,
J.
,
Cariou
,
J. P.
,
Courtney
,
M. S.
,
Parmentier
,
R.
,
Mikkelsen
,
T.
,
Wagner
,
R.
,
Lindeloew
,
P.
,
Sjoeholm
,
M.
, and
Enevoldsen
,
K.
,
2008
, “
Comparison of 3D Turbulence Measurements Using Three Staring Wind LIDARs and a Sonic Anemometer
,”
14th International Symposium for the Advancement of Boundary Layer Remote Sensing
(ISARS 2008),
Roskilde
,
Denmark
, June 23–25, Paper No. 012012.
13.
Iungo
,
G. V.
,
Wu
,
Y. T.
, and
Porte-Agel
,
F.
,
2012
, “
Field Measurements of Wind Turbine Wakes With LIDARs
,”
J. Atmos. Oceanic Technol.
,
30
(
2
), pp.
274
287
.
14.
Drechsel
,
S.
,
Chong
,
M.
,
Mayr
,
G. J.
,
Weismann
,
M.
,
Calhoun
,
R.
, and
Dornbrack
,
A.
,
2009
, “
Three-Dimensional Wind Retrieval: Application of MUSCAT to Dual-Doppler LIDAR
,”
J. Atmos. Oceanic Technol.
,
26
(
3
), pp.
635
646
.
15.
Hirth
,
B. D.
, and
Schroeder
,
J. L.
,
2012
, “
Documenting Wind Speed and Power Deficits Behind a Utility-Scale Wind Turbine
,”
J. Appl. Meteorol. Climatol.
,
52
(
1
), pp.
39
46
.
16.
Fredriksson
,
K.
,
Galle
,
B.
,
Nystroem
,
K.
, and
Svanberg
,
S.
,
1981
, “
Mobile LIDAR System for Environmental Probing
,”
Appl. Opt.
,
20
(
24
), pp.
4181
4189
.
17.
Uchino
,
O.
, and
Tabata
,
I.
,
1991
, “
Mobile LIDAR for Simultaneous Measurements of Ozone Aerosols, and Temperature in the Stratosphere
,”
Appl. Opt.
,
30
(
15
), pp.
2005
2012
.
18.
Weibring
,
P.
,
Edner
,
H.
, and
Svanberg
,
S.
,
2003
, “
Versatile Mobile LIDAR System for Environmental Monitoring
,”
Appl. Opt.
,
42
(
18
), pp.
3583
3594
.
19.
Haala
,
N.
,
Peter
,
M.
,
Kremer
,
J.
, and
Hunter
,
G.
,
2008
, “
Mobile LIDAR Mapping for 3D Point Cloud Collection in Urban Areas—A Performance Test
,”
21st International Society for Photogrammetry and Remote Sensing
(
ISPRS
) Congress,
Beijing, China
, July 3–11, pp.
1119
1124
.
20.
USGS, 2015, “
LP DAAC Global Data Explorer
,” U.S. Department of the Interior, Washington, DC, gdex.cr.usgs.gov
21.
Bingoel
,
F.
,
Mann
,
J.
, and
Foussekis
,
D.
,
2009
, “
Modelling Conically Scanning LiDAR Error in Complex Terrain With WAsP Engineering
,”
Meteorol. Z.
,
18
(
2
), pp.
189
195
.
22.
Behr
,
T.
,
Kalfas
,
A.
, and
Abhari
,
R. S.
,
2006
, “
A Probabilistic Uncertainty Evaluation Method for Turbomachinery Probe Measurements
,”
18th Symposium on Measuring Techniques in Transonic and Supersonic Flows in Cascades and Turbomachines
,
Thessaloniki, Greece
, Sept. 21–22, Paper No. 18MTT08.
23.
Van der Hoven
,
I.
,
1957
, “
Power Spectrum of Horizontal Wind Speeds in the Frequency Range From 0.007 to 900 Cycles Per Hour
,”
J. Meteorol.
,
14
(
2
), pp.
160
164
.
24.
German Weather Service, 2015, “
Lindenberger Säule,” German Weather Service, Frankfurter, Germany, http://www.dwd.de/mol
25.
Kelley
,
N. D.
,
Jonkman
,
B. J.
,
Scott
,
G. N.
, and
Pichugina
,
Y. L.
,
2007
, “
Comparing Pulsed Doppler LIDAR With SODAR and Direct Measurements for Wind Assessment
,”
American Wind Energy Association WindPower Conference and Exhibition
, Los Angeles, CA, June 3–7, Paper No. NREL/CP-500-41792.
26.
Kocer
,
G.
,
Chokani
,
N.
, and
Abhari
,
R. S.
,
2012
, “
Wake Structure of a 2MW Wind Turbine Measured Using an Instrumented UAV
,”
AIAA
Paper No. 2012-0231.
27.
Micallef
,
D.
,
Ferreira
,
C. S.
,
Sant
,
T.
, and
van Bussel
,
G.
,
2010
, “
An Analytical Model of Wake Deflection Due to Shear Flow
,”
Science of Making Torque from Wind
(
Torque 2010
),
Crete, Greek
, June 28–30.
28.
Madsen
,
H. Aa.
,
Riziotis
,
V.
,
Zahle
,
F.
,
Hansen
,
M. O. L.
,
Snel
,
H.
,
Grasso
,
F.
, and
Larsen
,
T. J.
,
2011
, “
Blade Element Momentum Modeling of Inflow With Shear in Comparison With Advanced Model Results
,”
Wind Energy
,
15
(
1
), pp.
63
81
.
29.
Grasso
,
F.
,
2010
, “
AWSM—Ground and Wind Shear Effects in Aerodynamic Calculations
,”
ECN
Wind Energy, Petten, The Netherlands, Technical Report No. ECN-E–10-016.
30.
Sezer-Uzol
,
N.
, and
Uzol
,
O.
,
2013
, “
Effect of Steady and Transient Wind Shear on the Wake Structure and Performance of a Horizontal Axis Wind Turbine Rotor
,”
Wind Energy
,
16
(
1
), pp.
1
17
.
31.
Baldacchino
,
D.
,
2012
, “
Horizontal Axis Wind Turbine Wake Stability Investigations; Insights Through a Vortex-Ring Modelling Approach
,” Master thesis, Delft University of Technology, Delft, The Netherlands.
32.
Helmis
,
C. G.
,
Papadopoulos
,
K. H.
,
Asimakopoulos
,
D. N.
,
Papageorgas
,
P. G.
, and
Soilemes
,
A. T.
,
1995
, “
An Experimental Study of the Near-Wake Structure of a Wind Turbine Operating Over Complex Terrain
,”
Sol. Energy
,
54
(
6
), pp.
413
428
.
33.
Kress
,
C.
,
Barber
,
S.
,
Chokani
,
N.
, and
Abhari
,
R. S.
,
2011
, “
Improved Modeling of Wakes: Experimental Study and Experimentally-Anchored Model
,”
European Wind Energy Association (EWEA) Offshore 2011
,
Amsterdam, Nov. 29-Dec. 1
.
34.
Subramanian
,
B.
,
Chokani
,
N.
, and
Abhari
,
R. S.
,
2016
, “
Aerodynamics of Wind Turbine Wakes in Flat and Complex Terrain
,”
Renewable Energy
,
85
, pp.
454
463
.
You do not currently have access to this content.