COLORADO ANEMOMETER LOAN PROGRAM
 

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BRANSON - 7/21/2009 through 11/13/2010

LOCATION DETAILS
Latitude:
N 37° 9.549’ or N 37° 9’ 33"
Longitude:
W 103° 46.722’ or W 106° 46’ 43"
Township:
33 S
Range:
57 W
Section:
22
Elevation (ft.):
5,690
Datum:
WGS 84
Tower Type:
NRG Tilt-Up
Tower Height:
30 m (98.4 feet)
Vane Offset (deg):
+354°
Direction Basis:
Magnetic North
Mag. Declination:
8° 17' E, changing by 7' W/yr
Wind Explorer S/N:
0662
Site No.:
3703

 CSU ALP Install Team (from left): Derrick Benallie, Jake Renquist, Eric Rasbach, Nick Wagner, Mike Kostrzewa, Doug Hopper, and Daniel Fink.

 

DATA DETAILS

July 21, 2009 through November 13, 2010:

The anemometer tower was installed on July 21, 2009 and removed on November 13, 2010. The site is located just south of U. S. Highway 160 in Las Animas County east of Trinidad and about 11.4 miles NE of Branson. The site is flat in all directions with no obstructions within at least 1/2 mile.

All data was collected using an NRG #40 anemometer and NRG #200 Wind Vane mounted on a tilt-up tower located at a height of 30m. This equipment fed into an NRG Wind Explorer data logger. All data plugs were sent to the Colorado ALP at Colorado State University for analysis. The data plug files and text versions of these files are given below.

Raw Wind Data Files
NRG Data Plug Files
Txt Files

Highest 2 sec Gust mph

Gust Date/Time
57
8/18/09 14:48
Branson_3703_2009_1010_0117.A09 Branson_3703_2009_1010_0117.txt
51
12/29/09 11:28
Branson_3703_2009_0117_0410.A10 Branson_3703_2009_0117_0410.txt
71
1/22/10 5:29
Branson_3703_2010_0410_0727.A10 Branson_3703_2010_0410_0727.txt
69
4/12/10 21:56
Branson_3703_2010_0727_1113.A10 Branson_3703_2010_0727_1113.txt
66
10/25/10 11:43

It is important to note that these are the raw files without any compensation for offset. It is also important to note that the temperature was not recorded during this period.

Using this data, an analysis of the wind resource report was developed using Windographer 1.45. For this data an offset of +354° was applied to the wind vane data. For this report, a data quality analysis was performed on the data. This data was filtered two ways:

  1. Any wind speed data where the wind speed was less than 1 mph for 3 hours or more was deleted.
  2. Any wind direction data where the wind direction varied by less than 3 degrees over 6 hours was deleted

Windographer was then used to add in synthetic data to these intervals with suspect data. The summary report, the combined data files (with and without the data quality analysis), and the Windographer files (with and without the data quality analysis) are given below:

Final Wind Resource Summary

Highlights of the wind resource for the entire recording period from July 21, 2009 through November 13, 2010 are given below:

Data Properties
Variable
Data Set Starts:
7/21/2009 11:30 MST
Height above ground (m)
30
Data Set Ends:
11/13/2010 12:30
Mean wind speed (mph)
14.257
Data Set Duration:
16 months
Median wind speed (mph)
13.240
Length of Time Step:
10 minutes
Min wind speed (mph)
0.42
Elevation (ft.):
5,690
Max wind speed (mph)
57.3
Mean air density (kg/m³):
1.035
Mean power density (W/m²)
296
Wind Power Coefficients
Mean energy content (kWh/m²/yr)
2,593
Power Density at 50m:
375 W/m²
Energy pattern factor
2.211
Wind Power Class:
3 (Fair)
Weibull k
1.702
Wind Shear Coefficients
Weibull c (mph)
15.946
Power Law Exponent:
0.121
1-hr autocorrelation coefficient
0.826
Surface Roughness:
0.01 m
Diurnal pattern strength
0.183
Roughness Class:
0.780
Hour of peak wind speed
20
Roughness Description:
Rough Pasture
Mean turbulence intensity
0.17378
Note: The wind power density and wind power class at 50m are projections of the data from 30m. A surface roughness of 0.01 meters was assumed for this projection. This is equal to that of a rough pasture. This value was then used this to calculate the roughness class and the power law exponent shown above.
Standard deviation (mph)
8.492
Total data elements
160,176
Suspect/missing elements
3,938
Data completeness (%)
97.8

 

Probability Distribution Function at 30m: Frequency (%) vs. Wind Speed (mph)

 

Vertical Wind Shear, Height (m) vs Mean Wind Speed (mph)

 

Wind Frequency Rose at 30 meters

 

Wind Energy Rose at 30 meters

 

Daily Wind Speed Profile at 30m, Hourly Mean Wind Speed (mph) vs. Hour of the Day

 

Seasonal Wind Speed Profile at 30m, Monthly Mean Wind Speed (mph) vs. Month

 

Windographer was used to match up the wind at this site with the performance curves of some common turbines of various sizes and various heights. The table below shows the results. For the larger turbines, the tower height was increased to account for the larger turbine blades - the wind resource was extrapolated to these higher heights. Keep in mind that the larger and the higher the turbine, the better the wind and the greater the output. But of course, as the tower heights and turbine sizes increase so does the cost.

Turbine
Rotor
Diameter
meters
Rotor
Power
kW
Hub
Height
meters
Hub
Height
Wind
Speed
mph
Time
At
Zero
Output
percent
Time
At
Rated
Output
percent
Average
Net
Power
Output
kW
Average
Net
Energy
Output
kWh/yr
Average
Net
Capacity
Factor
%
Bergey Excel-R
6.7
7.5
30 14.26 22.63 7.31 2.3 20,100 30.7
Bergey Excel-S
6.7
10
30 14.26 13.28 4.06 2.5 22,300 25.5
Bergey XL.1
2.5
1
30 14.26 5.56 10.36 0.3 3,000 34.7
Southwest Skystream 3.7
3.7
1.8
30 14.26 20.67 0.00 0.6 5,200 33.3
Southwest Whisper 500
4.5
3
30 14.26 22.51 8.92 1.1 9,500 36.1
Northern Power NW 100/21
20.7
100
37 14.62 20.05 0.00 26.2 229,900 26.2
Vestas V47 - 660 kW
47
660
65 15.66 19.43 1.20 198.0 1,734,000 30.0
GE 1.5s
70.5
1,500
80.5 16.07 23.62 7.54 420.1 3,680,200 28.0
Vestas V80 - 2.0 MW
80
2,000
100 16.50 22.77 4.00 650.1 5,695,100 32.5
GE 2.5xl
100
2,500
110 16.69 18.35 8.85 899.6 7,880,600 36.0

IMPORTANT: No turbine losses are included in the power, energy, and capacity factor values in the table. Typically, turbine losses can be 5-20% to account for maintenance downtime, icing/soiling and losses from other turbines in a wind farm. Users wanting to be conservative in the performance projections should multiply the power, energy, and capacity values by (1- % losses) to account for these losses.


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Last updated: June 2009
Email questions & comments to: michael@engr.colostate.edu
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