LiDAR analysis at a site with simple terrain
Alex Clerc, Lee Cameron
Tuesday 2nd September 2014
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Contents
• Re-cap of TI correction validation study using hub height met masts*
• Description of LiDAR setup at a site with simple terrain
• Description of LiDAR data
• Conclusions and future work
*A. Clerc, “Validation Analysis - Turbulence Correction”, PCWG meeting 1/4/2014
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Summary of Previous Validation Study (April 2014 PCWG meeting)
• Data sets from 23 Power Performance tests re-analysed using PCWG TI
correction.
• It was found that overall the TI correction is very effective at removing
power curve variation, in particular at low and high wind speeds
Power [kW]
Before TI correction
After TI correction
Specific Energy
difference from
mean [MWh]
TI
Wind Speed [m/s]
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Summary of Previous Validation Study (April 2014 PCWG meeting)
• Data sets from 23 Power Performance tests re-analysed using PCWG TI
correction.
• However, at moderate wind speeds (7-10 m/s) power is lower than
average at low TI and higher than average for high TI
Power [kW]
Before TI correction
After TI correction
Specific Energy
difference from
mean [MWh]
TI
Wind Speed [m/s]
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Summary of Previous Validation Study (April 2014 PCWG meeting)
• For individual turbines, the energy errors due to power curve variation
were between +/- 1.5% before the TI correction and +/- 0.7% after the TI
correction.
Specific Energy Prediction Error
using Inner Range Curve [%]
• Code to run turbulence correction is on Github,
https://github.com/peterdougstuart/PCWG
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Description of LiDAR experiment
• A classic power performance test setup is limited due to having only a
few measurement heights, and no information above hub height
• Using a LiDAR it is hoped that Rotor Equivalent Wind Speed (REWS)* can
be quantified by accounting for:
– Horizontal wind speed measured at each height (wind speed profile)
– Wind direction measured at each height (veer)
– Vertical wind speed measured at each height (inflow angle)
*IÑAKI LEZAUN MAS (GAMESA), “Rotor Equivalent Wind Speed”, PCWG Meeting 1/4/2014
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Theoretical impact of tilt and inflow
• Site assessments and wind flow models focus on the horizontal
component of wind speed but turbine rotors are tilted
• Theoretical AEP reduction for a rotor tilt of 0°: AEP is independent of
inflow angle
Actual wind vector
Horizontal wind
Wind ┴ rotor
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Theoretical impact of tilt and inflow
• Site assessments and wind flow models focus on the horizontal
component of wind speed but turbine rotors are tilted
• Theoretical AEP reduction for a rotor tilt of 3°: up to 0.25% loss in AEP
per degree inflow
Actual wind vector
Horizontal wind
Wind ┴ rotor
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Theoretical impact of tilt and inflow
• Site assessments and wind flow models focus on the horizontal
component of wind speed but turbine rotors are tilted
• Theoretical AEP reduction for a rotor tilt of 6°: up to 0.5% loss in AEP per
degree inflow
Actual wind vector
Horizontal wind
Energy lost
when wind
blows up
Wind ┴ rotor
Energy gained
when wind
blows down
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Description of LiDAR experiment
• Six turbines, two LiDARs. LiDAR 1 (M856) is next to the power
performance mast.
Height asl
LiDAR 2
LiDAR 1
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Description of LiDAR measurements
• Concurrent LiDAR measurements from June 14 to Aug 14
• At least one measurement height per 20m from turbine lower tip to
upper tip
• Rotor equivalent wind speed (REWS) is on average lower than hub height
(HH) wind speed and has a relationship with turbulence intensity (TI)
Hub height wind speed
(3, 4]
Hub height
TI
(4, 5]
(5, 6]
(6, 7]
(7, 8]
(8, 9]
(9, 10] (10, 11] (11, 12]
(0.15, 0.2]
100.0% 98.9%
98.7%
98.3%
98.4%
98.1%
98.6%
(0.1, 0.15]
99.6%
99.5%
98.9%
98.5%
98.1%
98.2%
(0.05, 0.1]
97.6%
98.7%
99.1%
99.0%
98.8%
(0, 0.05]
97.2%
96.9%
96.4%
96.1%
98.3%
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Description of LiDAR measurements
• Rotor equivalent wind speed (REWS) is on average lower than hub height
(HH) wind speed and has a relationship with turbulence intensity (TI)
(4, 5]
98.9%
99.5%
98.7%
96.9%
(5, 6]
98.7%
98.9%
99.1%
96.4%
After TI correction
Power [kW]
Before TI correction
(9, 10] (10, 11] (11, 12]
98.6% 98.3%
Specific Energy
difference
from mean
[MWh]
Hub height
TI
(0.15, 0.2]
(0.1, 0.15]
(0.05, 0.1]
(0, 0.05]
(3, 4]
100.0%
99.6%
97.6%
97.2%
Hub height wind speed
(6, 7]
(7, 8]
(8, 9]
98.3% 98.4% 98.1%
98.5% 98.1% 98.2%
99.0% 98.8%
96.1%
Wind Speed [m/s]
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Description of LiDAR measurements
• REWS measured by each LiDAR: Table below compares AEP calculations to
a base case assumption of hub height wind speed across entire rotor, no
veer and inflow = 0 deg.
Lidar 1
Lidar 2
% AEP change from base case
Effect on AEP by including
measured wind speed profile
Effect on AEP by including
measured veer
Effect on AEP by including
measured inflow and actual turbine tilt
TOTAL
-1.3%
-1.6%
-0.4%
-0.3%
-0.3%
0.7%
-2.1%
-1.2%
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Description of LiDAR measurements
Lidar 1
Lidar 2
% AEP change from base case
Effect on AEP by including
measured inflow and actual turbine tilt
-0.3%
LiDAR 2
LiDAR 1: wind flows uphill in
predominant direction
LiDAR 2: wind flows downhill in
predominant direction
Negative
inflow
angle
Height asl
Most significant difference between
two locations is upwind terrain
0.7%
LiDAR 1
Positive
inflow
angle
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Description of LiDAR measurements
• Table below compares to a base case assumption of hub height wind
speed across entire rotor, no veer and inflow = 0 deg.
Lidar 1
Lidar 2
% AEP change from base case
Effect on AEP by including
measured wind speed profile
Effect on AEP by including
measured veer
Effect on AEP by including
measured inflow and actual turbine tilt
TOTAL
-1.3%
-1.6%
-0.4%
-0.3%
-0.3%
0.7%
-2.1%*
-1.2%**
*The turbine next to LiDAR 1 underwent a power performance test, so this is
inherent in the power performance test result
**This implies +0.9% energy available compared to the power performance
turbine
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Site Calibration
• Over the IEC valid sectors (170-230) site calibration ranges from 3% to
5% speed-up at 10m/s.
• Site calibration varies considerably with TI; applying power
performance TI and shear filters to the site calibration reduces the
overall speed-up by 1%.
• Complexity of site calibration seems to introduce considerable noise
into the analysis
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Conclusions
• REWS has a strong relationship with turbulence intensity. TI correction
should be applied when considering REWS in a power curve correction.
– Effectiveness of turbulence intensity correction has been demonstrated in
previous presentation*.
– https://github.com/peterdougstuart/PCWG
• Inflow angle could help explain energy differences between turbines, in
this case on a simple site >1% energy difference may be explainable by
considering inflow and tilt.
• In this example, site calibration seems to be the most difficult problem to
solve. Effects of TI, REWS and Inflow angle at this site are much smaller
than the effect of the site calibration (5% wind speed). More effort
needed on site calibration to improve this analysis.
*A. Clerc, “Validation Analysis - Turbulence Correction”, PCWG meeting 1/4/2014
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Future work
• Include a flexible site calibration module in the Github code
• Include veer and inflow consideration in the Github code
• Analysis of more LiDAR data will be necessary to know if REWS is
effective in correcting power curves.
• Better understanding of the assumptions behind warranted power curves
would be very helpful, in particular:
–
–
–
–
Shear above hub height
Veer across the rotor
Inflow angle (generally a range is given, e.g. +/-2deg)
For PCWG Guideline Document discussion…
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