2014 Wind Turbine Blade Workshop- Johansen

1. Vortex Generator Performance Measurement Challenges and Solutions
Nick Johansen
Commercial Operations System Engineer
Sandia Blade Reliability Workshop
August 28, 2014

2. 2
Presentation Outline
1Experimental Set-up
2Performance Assessment Toolset
•Site Wind Climatology and Test/Control Period Definitions
3Performance Assessment Method 1: Power Curve
4Performance Assessment Method 2: Active Power Relationships
5Conclusions/Next Steps

3. 3
Experimental Test Set-up
•Mean Site Altitude: 1446m (~4750’) -> 80m Annual Avg. Air Density 1.04 kg/m3
•Annual Average Wind Speed: 8.7 m/s
•Turbulence Intensity less than IEC specification for all operational wind speeds

4. 4
Performance Assessment Toolset

5. 5
Site Wind Climatology/Test Period Definitions

6. 6
0 0.2 0.4 0.6 0.8 1 1.2
0
0.2
0.4
0.6
0.8
1
Wind Speed (normalized)
Power (normalized)
Test Period (subset) Power Curves
vg
no vg
• Ambient turbulence intensity inflow conditions from
test and control periods similar for wind speeds
with largest power curve difference
• Change in energy to be calculated per wind speed
bin and a function of control turbine performance
• Test period subsets used to illustrate sensitivity of
energy change to inflow conditions
Performance Assessment: Power Curve
0 0.2 0.4 0.6 0.8 1 1.2
0
0.2
0.4
0.6
0.8
1
Wind Speed (normalized)
Power (normalized)
Control Period Power Curves
vg
no vg
0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
Hub Height Wind Speed (normalizwed)
Turbulence Intensity
Ambient Turbulence Intensity Comparison
Control
Test
Ti
A
=17.9504
Ti
B
=15.7066
Ti
C
=13.4628
Control Test
With VG
w/o VG

7. 7
• Active power from VG turbine is plotted as
function of active power from control
turbine (control and test periods)
• Variations in relationship attributed to vg
install
• Requires proper definition of
control and test period
• No reference wind speed is used (nacelle
or free-stream)
• Breaking test periods into subsets -> yields
more independent estimates using all 9
pairs
• Distribution of energy capture differences
established
• Time of year/inflow conditions
sensitivity identified
• Distribution significantly cleaned up with
use of more representative control periods
Performance Assessment: Active Power
Relationships
What conditions caused these estimates?

8. 8
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
0
0.2
0.4
0.6
0.8
1
Power Output (w/o VG) (normalized)
Power Output (w VG) (normalized)
Active Power v Active Power Relationship
Control AVG
Control AVG-STD
Control AVG+STD
Test AVG
Test AVG-STD
Test AVG+STD
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
10
1
10
2
Turbine with Vortex Generator Power Standard Deviation Sensitivity to Generation Level
Power Output (w/o VG) (normalized)
Power STD as Percentage of Power Output (%)
Control Period
Test Period
• Active power relationship produces
clear signal w/o use of wind speed
• Reduction in power scatter on vortex
generator turbine
• Cleaner loads?
• Spike in power fit standard deviation
associated with region of largest
difference between VG and control
turbine
• VG turbine outperforms control
turbine
• Results associated with specific
inflow conditions
• Repeat for other test period subsets
• Matrix of results to inflow conditions
is produced
Active Power Relationships (cont’d)
Variable Speed Fixed Speed
Pitch>0

9. 9
•Inflow conditions in control and test periods to be as close as possible
•Balance between proximity in time or proximity in inflow conditions
•Active power relationship methodology yields multiple estimates of energy capture improvements
•Seasonal/inflow sensitivity identified
•Nacelle wind speed independent
•Power curve approach useful although yields fewer estimates of energy capture change
•Upwind wind resource required can limit useable sectors
•Performance of vortex generators understood at test site(s)
•Performance assessment method designed such that results can be applied to other sites
Conclusions

10. 10
•Instead of just matching seasons for control and test periods, match exact inflow conditions
•Wind direction, wind speed and directional shear, turbulence intensity, atmospheric stability, inflow angle
•VG energy capture improvement of 1-2% estimate is often determined through careful filtering of inflow conditions (non-wake scenarios)
•For sites that have significant energy capture with close to mid- distance waked inflow, how does the 1-2% change
•Do VGs impact performance at low wind speeds and high turbulence where high turbulence is known to improve energy capture already?
Next Steps