Adjustment of Anemometer Readings for Energy

Adjustment of Anemometer
Readings for Energy
Production Estimates
WINDPOWER – June 2008 – Houston, Texas
Matthew Filippelli, Julien Bouget, Michael Brower, and Dan Bernadett
AWS Truewind, LLC | 463 New Karner Road | Albany, NY 12205
www.awstruewind.com
Kathleen Moore
Integrated Environmental Data, LLC | 255 Fuller Road, Suite 289 | Albany, NY 12203
www.iedat.com
Headquarters: Albany, NY
• Mapping
•
•
•
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
Energy Assessment
Project Engineering
Performance Evaluation
Forecasting
Industry Leader & Consultant for ~20,000 MW in 60 countries
 Full spectrum of wind plant design, development
& evaluation services
 Consultant to several NY and US agencies & utilities for
wind & PV systems
 Established in 1983; 65 employees
Introduction
• There is growing evidence that the energy
production of many wind projects has been
overestimated. No single cause is likely to blame;
rather a number of factors probably contributed.
• One plausible source of trouble is dissimilarity
between wind resource assessment methodology
and wind turbine power performance verification.
Objective & Scope
• The following parameters were studied to characterize potential
sources of measurement error and/or energy production overestimation.
1. Effect of Turbulence Intensity
2. Effect of Off-horizontal flow
3. Effect of unusual shear and other weather anomalies
• The usefulness of new and existing models in improving wind
resource assessment and energy production was also explored.
• These results and related literature were examined to assess the
feasibility of wind speed measurement correction and to identify
potential improvements in wind resource assessment
methodology.
Power Curve Measurement
•
Procedure for power curve
measurement and certification
has been standardized
– IEC 61400-12-1
•
•
•
No standard for wind resource
assessment procedure
Best practices are generally
followed, but discrepancies
still exist in execution
These differences can
potentially lead to
measurement errors and/or
energy over-estimation
Wind Measurement Equipment
•
Resource assessment practice
generally attempts to follow IEC
recommendations on several
factors to lower uncertainty in
measured data
– Separation from tower
– Boom orientation
•
•
Risoe P2546A
•
NRG #40
One of the biggest differences
is choice in anemometer
IEC standards call for a Class 1
Anemometer (or currently: 1.7A
or 2.5B)
NRG #40 Anemometer has been
North American standard for
several years
Anemometer Choice
•
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Both instruments were chosen as
they represent the majority of
devices deployed for their
intended use
As the Max 40 is the current
standard for wind resource
assessment in North America, its
sensitivity to turbulence and offhorizontal flow will be examined
The Risoe will be used as a
reference for this work. While it is
not believed be free from all
error, its performance properties
are well documented and it meets
the IEC criteria for power
performance verification
Turbulence Assessment Database
•
•
Archived and current data from 5 resource assessment towers across
eastern North America were analyzed for this component.
Each station equipped a Risoe P2546A / NRG Max 40 pair at one
monitoring height
Tower
A
B
C
D
E
Table 1: Turbulence Analysis Case Database
Monitoring
Tower / Mount
Risoe
NRG
Height (AGL)
type
Orientation Orientation
40 m
Lattice/ Side
180° T
300° T
40 m
Lattice/ Side
285° T
165° T
40 m
Lattice/ Side
32° T
212° T
40 m
Lattice/ Side
22° T
202° T
140 m
Lattice/ Top
180° T
0° T
POR
(months)
5
30
12
3
9
Data Filtering
•
The following filtering criteria were used for each station:
– Only valid data: remove icing, obvious malfunction, etc.
– For side mount booms, use 20° direction sector bisecting sensors
– For top mount booms, use 40° direction sector bisecting sensors
from prevailing orientation
– Only speeds above 4 m/s
Table 2: Turbulence Analysis Filter Criteria and Summary Statistics
Tower
Direction
Wind
NRG
Risoe
Mean of Ratio
Data
Sector
Speeds
TI
TI
NRG/Risoe
Points
230° – 250° T
≥4 m/s
6.6%
6.9%
0.980
4200
A
225° – 245° T
≥4 m/s
9.8%
10.0%
1.003
8500
B
300° – 320° T
≥4 m/s 19.6%
21.4%
1.020
3100
C
280° – 300° T
≥4 m/s 23.5%
25.3%
1.003
600
D
250° – 290° T
≥4 m/s 11.7%
12.1%
0.982
7000
E
Methodology
•
The following parameters were calculated or used in the analysis .
–
–
–
•
P
R
TI
40 m wind speed from NRG anemometer. Designated “Primary” (m/s)
40 m wind speed from Risoe Anemometer. Designated “Redundant” (m/s)
Turbulence Intensity as defined 40 m NRG anemometer – σP/P (%)
The following procedure was used to assess the impact of horizontal
turbulence intensity on wind speed measurements.
–
–
–
–
–
–
The Risoe sensor was used as the reference anemometer, as it has been
shown to have little sensitivity to turbulence intensity
P/R and TI were calculated for each valid 10-minute data record
The P/R values were binned and averaged in 1% TI segments for the total
data set (P≥4.0 m/s), and for three separate speeds (P= 5, 8, and 12 m/s)
Frequency distributions of TI were calculated for each data set identified
above
Linear regressions were fit for each set of data at each tower
Following are the graphical representations of the results for each station
Tower A Results
Tower B Results
Tower C Results
Tower D Results
Tower E Results
Database Comparison with Reference
Reference curve is NRG data from [1]
Turbulence Intensity Results
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•
•
•
•
All towers, with the exception of Tower D, showed a strong correlation
between horizontal turbulence intensity and the NRG/Risoe (P/R)
speed ratio.
The positive slopes of the regression lines show that the NRG
anemometers indicate progressively higher relative speeds under more
turbulent conditions. This is evidence of turbulent over-speeding.
Based on these field results and the previous work, the NRG varies
from reference speed at an average rate of 0.0975% per 1% TI.
There appear to be a site-specific variations in this relationship with
wind speed.
The field and reference data indicate that this response by the NRG
anemometer to turbulent flow could induce over-estimates of wind
speed on the order of 1% to 2%+, which can translate into overestimations of 1.8% to 3.6%+ in energy estimates.
Turbulence Intensity Discussion
•
•
•
There appears good agreement between the field-measured
slopes and previously published research results; the
differences in offsets may be partially attributable to individual
sensor pair characteristics and/or other environmental factors.
Tower C’s slope is steeper than the other three towers and the
research data. This higher sensitivity to turbulence may be
attributable to the terrain at this site. The area is rugged,
heavily forested and has with a high incidence of off-horizontal
flow, the implications of which are discussed in the next
section.
Given the strong correlation between increasing relative speed
output and TI measured by the NRG anemometer, it is
envisioned that a linear correction could be implemented as a
partial treatment for these conditions.
Off-Horizontal Flow
•
•
•
In sloped terrain and complex
flow regimes, a turbine’s output
will be driven by the horizontal
component of the wind
As such, the potential for errors
in energy production estimates
grows when the resource
assessment anemometer does
not respond to off-horizontal
flows as a turbine does.
Numerous field and wind
tunnel studies have produced a
body of data on t he inclined
flow response of both the NRG
and Risoe anemometers
Risoe P2456A manufacturer specifications
Off-Horizontal Flow Database
•
•
Archived and current data from 3 resource assessment stations
across eastern North America were analyzed for this
component.
Each station is equipped with an NRG Max 40 anemometer pair
at one monitoring height supplemented with an RM Young
model 27107T vertical propeller anemometer. One of the
stations, Tower E from the previous section, also has a Risoe
P2546A anemometer installed.
Tower
E
F
G
Table 3: Off-Horizontal Flow Data Base
Tower
RM Young
Risoe
NRG
Type Height/Bearing Height/Bearing
Height/Bearing
Lat.
132 m / 0° T
140 m /180° T
140 m / 0° T
Lat.
74 m / 208° T
N/A
76 m / 88° T
76 m / 208° T
Lat.
47 m / 240° T
N/A
49 m / 173° T
49 m / 241°T
POR
(months)
9
3
3
Tower F Overview
Tower F – 80m
Tower G Overview
Tower G – 50m
Approximation Off-Horizontal Flow Angle
Vout  1  0.02
   5,5
  radians

  
 3.6 
Voutput  Vtrue 1  0.02  
  Vtrue 1 

5

/
180
5






V
Vvert
V  3.6 
sin( )  vert 
 vert 1 

Vtrue 
5 
 Vtrue 
 V

output


3
.
6



1

5 

  1
sin    
Angular response characteristics of NRG anemometer
with vector scalar wind speed definition [2]
Vvert  3.6 
1 

Vtrue 
5 
A

Vvert
Vtrue
A
3.6 A
1
5
Calculation of Over-Speeding
 3.6 
Voutput  Vtrue 1 

5 



 cos( ) 

Vhorizontal  Vtrue cos( )  Voutput 
3
.
6





 1  5  


Over / underspeeding 
Voutput  Vhorizontal
Vhorizontal
Procedure:
• The formulae presented were
applied to each 10-minute
valid data record from Towers
F and G
• Data were averaged in 10° bins
and plotted
Tower F Results
Tower G Results
Off-Horizontal Flow Discussion
•
•
•
This process illustrates that with onsite vertical speed measurement
and wind tunnel results, off-horizontal flows can be assessed with
standard wind resource assessment sensors.
The resulting upslope angle calculations are consistent with the
expected terrain-induced flow distortion, and will be useful for other
aspects of project development (e.g. turbine suitability).
The calculated speed deviations exhibit sizeable range for both towers
– Tower F deviations range from -1.19% to 2.71%. Several direction sectors
have calculated over-speeding of greater than 2.5%, which could yield
energy over-estimation on the order of 4.5%
– Tower G deviations range from -1.51% to 3.7%. Several direction sectors
have calculated over-speeding of greater than 3%, which could yield
energy over-estimation on the order of 5.4%
•
These effects can be further compounded by turbulent over speeding
of the anemometry, particularly in rugged terrain.
Vertical Turbulence Intensity
•
•
•
Previous field tests evaluating the performance of cup anemometry
indicated that the vertical turbulence intensity had a large influence in
the differences between cup anemometer measurements. It was also
shown that corrections to anemometer measurements using this
relationship could reduce variation of the wind speed ratios [3].
Tower E’s equipment configuration facilitated investigation into the
significance of this parameter on the ratio between the NRG and Risoe
anemometers.
The following procedure was employed to assess the influence of
vertical turbulence intensity on wind speed measurements.
–
–
–
–
The vertical turbulence intensity is defined as such: σW/P (also σW/U)
The Risoe sensor was again used as the reference anemometer
P/R and σW/P were calculated for each valid 10-minute data record
The P/R values were binned and averaged in 1% σW/P segments for the total
data set (P≥4.0 m/s)
– Frequency distributions of σW/P were also calculated
– A linear regression was fit to the resulting data set
– Following is a graphical representation of the results
Tower E Results
Vertical Turbulence Intensity Results
•
•
•
As shown in [2], a distinct relationship is apparent between the
vertical turbulence intensity and the wind speed ratio between
the NRG and Risoe Anemometer.
Based on these results, the NRG varies from reference speed at
an average rate of 0.218% per 1% σW/P.
The slope of this regression agrees with the earlier horizontal
turbulence studies, suggesting that the NRG instrument
indicates relatively higher wind speed values as the vertical
turbulence intensity increases.
Unusual Shear and Other Events
•
•
The effects of unusual shear
and other transient weather
events can be difficult to
capture with single point
measurements from
anemometry.
Appropriate equipment is
atmospheric profiler
– Sodar
– Lidar
•
Several types of events can
have detrimental effects on
turbine and park performance.
Directional Shear
•
•
Can manifest in several different ways
with different time scales.
Potential turbine- and park-scale
performance implications
–
–
–
Strong, transient directional shear event
in the mountains of western US [4]
•
Directional shear across the rotor plane
causes losses from aerodynamic
inefficiency, 2% to 3% losses estimated
for shears of 10° to 18°
In extreme cases, can cause excessive
loading and down time
On a park scale, performance can
degrade if optimized to the wrong wind
rose (typically measured below hub
height and not adjusted for increased
elevation).
Commonly difficult to detect on a
resource assessment tower with only
two vanes.
Other Meteorologically Driven Shear Conditions
•
•
•
Examples include time-varying horizontal shear, low level jets, frontal
passages, stability, and local circulations.
Can have a potentially significant negative impact on energy production
to a slight positive effect depending upon phenomena
Certain conditions may be difficult to identify with standard
meteorological towers, but may be more visible with high-sensitivity
sensors (e.g. 3D sonics, ΔT, etc.)
[4]
[5]
Terrain and Monitoring Condition Driven Shear
•
•
•
•
Conditions driven by slope steepness,
complexity, land cover, exposure, etc.
Here, anemometer scrutiny and
potential correction may be useful
Over-speeding on mast anemometers
may lead to significant errors in
absolute speed measurements and
shear calculations
Aside from complementary remotesensing campaigns, resource
assessment in complex terrain would
also benefit from vertical wind speed
measurements on the masts
Usefulness of Models
•
•
•
A High-resolution, CFD
model was sought to help
describe the wind regime
through the complex
terrain of Towers F and
G’s project area.
Meteodyn WT was run to
assess its usefulness in
providing 3D flow field
information, including offhorizontal wind data and
turbulence.
The results were checked
against the measurements
taken at Towers F and G.
Tower F Overview
Tower F
Tower G Overview
Tower G
Tower F Results
•
Inclined flow output from model
compared to measurement at 80 m
Tower G Results
•
Inclined flow output from model
compared at 80 m compared to
measured data at 50 m.
Conclusions
•
•
•
•
There are clear correlations between turbulence intensity and
the wind speed ratios of equally exposed NRG and Risoe
anemometers. The evidence resembles turbulent overspeeding, and appears to be treatable through linear
corrections.
Off-horizontal flow angles and the resulting speed deviations
can be calculated and treated through the analysis of standard
resource assessment anemometry and vertical speed
measurements.
Anemometer adjustments and careful resource assessment
practices may help mitigate errors in shear estimates due to
speed measurement inaccuracy.
The use of high-resolution, CFD-based wind models show
promise for assisting with the characterization of flow through
project sites, particularly in complex terrain.
References
[1] Papadopoulos, K. H. ‘Effects of Turbulence and Flow inclination on the
Performance of Cup anemometers in the Field,’ Boundary Layer Meteorology,
101: 77-107, 2001.
[2] Pedersen, T. F., ‘Power Curve Measurements under Influence of Skew Airflow
and Turbulence,’ 2002.
[3] Albers, A., ‘Open Field Cup Anemometry,’DEWI Magazine, No. 19, August 2001.
[4] Moore, K. and B. Bailey, ‘Classifying Rotor Span Shear Profile Variability and
Improving Wind Turbine Production Prediction,’
[5] Banta, R. M., et al., “Nocturnal Low-level Jet Characteristics observed during
CASES-99,’