Schroeder 2016 snl_blade_workshop_final

1. The Impact of Atmospheric Stability on Wind
Structure and Turbine Wakes as Revealed by
Radar Measurements
National Wind Institute
TEXAS TECH UNIVERSITY
John L. Schroeder1, James B. Duncan2, Brian D. Hirth2,
W. Scott Gunter3, Jerry G. Guynes2
1Geosciences Department, Texas Tech University
2National Wind Institute, Texas Tech University
3Department of Earth and Space Science, Columbus State University
2016 Sandia Blade Workshop, 31 August 2016

2. National Wind Institute
TEXAS TECH UNIVERSITY
Conflict of Interest Disclaimer
The co-authors of this presentation conduct research in the area of documenting
wind plant complex flows. This research is supported by the US Department of
Energy, Sandia National Laboratories, the National Science Foundation, and
private industry. Several co-authors of this presentation have equity ownership in
SmartWind Technologies LLC, which is developing products and services related
to the research being reported. The terms of this arrangement are in accordance
with Texas Tech University’s conflict of interest policies.

3. National Wind Institute
TEXAS TECH UNIVERSITY
Previous Work: Doppler Radar Complex Flows Documentation
• Wake structure and tracking
• Turbine-to-turbine interaction
• Array edge effects
• Terrain impacts
• Wind ramp evolution
• Atmospheric stability!

4. National Wind Institute
TEXAS TECH UNIVERSITY
Technology Advancement
New DOE-X Radar, Reese Technology Center
TTUKa Mobile Research Radars
OBJECTIVE: Enhance Clear Air
Sensitivity/Data Availability

5. National Wind Institute
TEXAS TECH UNIVERSITY
Initial DOE-X Radar Measurements
Highlight the Impact of Atmospheric Stability on Wind Structure
• Data availability greatly
increased with the DOE-X
relative to the TTUKa radars.
• Measurements reveal the
impact of atmospheric stability
on wind structure:
• Unstable “cellular” structure
from late morning to evening
• Stable “laminar” structure
overnight
• A “streaky” structure appears in
between
• Rapid transitions between
these different structures
• Significant wind speed and
directional shear overnight

6. National Wind Institute
TEXAS TECH UNIVERSITY
(1) (2) (3)
DOE-X Radial Velocity and TTU 200-m Tower Temperature
(3) VR (m s-1
) – 05/1500 UTC(2) VR (m s-1
) – 05/0800 UTC(1) VR (m s-1
) – 04/2200 UTC
∆θV= - 1.0K
L = -2.7 m
Iu = 0.35
∆θV = 9.8K
L = 8.3 m
Iu = 0.04
∆θV = -0.8K
L = -32.1 m
Iu = 0.15

7. National Wind Institute
TEXAS TECH UNIVERSITY
• Single Doppler sectors
• 1.0° elevation tilt
• Data collected in “clear air”
• Instrumented tower nearby
• Isolated turbine on edge of farm
• Period captures evening
boundary layer transition
• 2,821 individual scans collected
during 3 hours 36 minutes
• ~4.7 second sector revisit time
Single-Doppler Deployment with an Instrumented Tower
Deployment Details:

8. National Wind Institute
TEXAS TECH UNIVERSITY
Single-Doppler Radial Velocity and Tower Temperature

9. National Wind Institute
TEXAS TECH UNIVERSITY
(1) (2) (3)
Single-Doppler Radial Velocity and Tower Temperature
(3) VR (m s-1
) – 0100 UTC(2) VR (m s-1
) – 2345 UTC(1) VR (m s-1
) – 2250 UTC
∆θV = - 0.7K
L = -64.3 m
Iu = 0.1069
∆θV = 0.5K
L = 26.0 m
Iu = 0.0715
∆θV = 4.3K
L = 0.5 m
Iu = 0.0177

10. National Wind Institute
TEXAS TECH UNIVERSITY
• Define initial search 150m behind
turbine’s location.
• Wake center is determined to be
the location of the minimum
radial velocity along the cross-
section.
• Utilizing the initial wake center,
the downstream bounds are
defined to search for subsequent
wake positions.
• Process is iterated out to 3200m
at intervals of 25m.
How to define the end of the wake?
Simple Wake Detection Algorithm

11. National Wind Institute
TEXAS TECH UNIVERSITY
• In order for the downstream radial
velocity field to no longer be
considered part of the wake, we
required:
1. A lateral shift in the wake
center point from the
previous location exceeds
50m.
2. The magnitude of the
velocity deficit relative to the
inflow (200-400m upstream)
is less than 30%.
Determination of Wake Length
Not perfect!

12. National Wind Institute
TEXAS TECH UNIVERSITY
Stability Classification Average Wake Length (m) Number of SWPs
Unstable 1265m 242
Stable 1792m 915
~42% Increase in Wake Length During Stable Period
Stability is defined according to:
• Monin-ObukhovLength (L)
• Gradient in Virtual Potential
Temperature (ΔθV).
Stable Conditions
• 0 < L < 600 & ΔθV>0
Unstable Conditions
• -600 < L< 0 & ΔθV<0
Defining Stability

13. National Wind Institute
TEXAS TECH UNIVERSITY
To denote wake meandering: 1. Fit a linear model to the derived wake
centers. 2. Use the variability about this wake center line to quantify
wake meandering.
Determination of Wake Meandering

14. National Wind Institute
TEXAS TECH UNIVERSITY
Wake Meandering

15. National Wind Institute
TEXAS TECH UNIVERSITY
Determining Wake Width
• Use the wake tracking
algorithm to find the
centerline.
• The wake edge is
defined as the location
of an inflection point
along the radial velocity
cross-section or where
the average of the end
points exceed 85% of
the freestream flow.

16. National Wind Institute
TEXAS TECH UNIVERSITY
Wake Width
Wake Width
• Unstable/Convective
• Mean wind speed of
8.40 m s-1
• Mean TI of 0.10
• Stable
• Mean wind speed of
7.35 m s-1
• Mean TI of 0.04

17. National Wind Institute
TEXAS TECH UNIVERSITY
Summary
§ Advanced Doppler radar technologies and meteorological towers (with thermodynamic
measurements) are useful to explore changes in boundary layer wind structure and
turbine wake characteristics with changing atmospheric stability.
§Unstable regimes:
§ Flow is dominated by gusts and lulls, rapid changes in wind speed and direction
§ Turbine wake lengths are shorter
§ Turbine meandering is more substantial
§Stable regimes:
§ Boundary layer gusts and lulls subside, flow becomes remarkably smooth
§ Shear and veer become more substantial with height
§ Turbine wake lengths can become VERY long (e.g. >100 D)
§ Wake meandering is minimal
§ Turbine-turbine interaction is enhanced
§Stable atmospheric conditions might be an easier target for proactive control ideas
focused on minimizing turbine-to-turbine interaction.

18. National Wind Institute
TEXAS TECH UNIVERSITY
Acknowledgements
Financial Support:
Single-Doppler dataset collected with funding
provided by Sandia National Laboratories via the US
Department of Energy Wind and Water Power
Technologies Office
DOE-X radar development and initial data collection
funded by the US Department of Energy
(DE-EE0006804)
Analysis of the single-Doppler wake measurements
was completed using support provided by a National
Science Foundation Grant CBET award (1336935)