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CNS Meteorological System
• Upgraded 100-meter tower in 2004 to include a dual elevator on the same tower face
• Dual monitoring systems with independence from sensor to Plant Computer
• Wind Sensors have Cups/Vanes on one side and Sonic on the other
Systems A and B� 10, 60, and 100 meter wind speed and direction� 3 Delta-ts (60m-10m, 100m-10m, 100m-60m)� 10, 60, and 100 meter temperatures
System A only� 10 meter dew point� Station Pressure� Precipitation
Meteorological Parameters
System A � Climatronics F460 Wind speed and Direction Sensors� Climatronics Temperature Sensors� Tower Systems Elevator� Climatronics Dew Point Sensor� Climatronics Tipping Bucket Rain gauge with Wind Shield�Campbell Scientific 23X Micro Dataloggers�Climatronics Pressure Sensor
Meteorological Equipment
System B � Met One 50.5 Sonic Wind speed and Direction Sensors� Climatronics Temperature Sensors� Tower Systems Elevator� Campbell Scientific 23X Micro Dataloggers
Meteorological Equipment
Purpose
• Independently verify wind data collected from both systems are not statistically different
• Data from System A (cup/vane) can be interchanged with data from System B (sonic)
• Demonstrate the impact of the tower structure on meteorological data
Data Set
• One year of onsite validated hourly meteorological data (October 31, 2004 –October 30, 2005)
• 8784 possible hourly values for each parameter for both Systems A and B on the 100-meter tower
Methodology
• Remove bad data from System A and System B files including calibrations, frozen sensors, failed sensors, bad data spikes, etc
• Remove wind directions when wind speeds less than 3 mph and/or wind directions are through tower
• Remove wind speeds when wind directions are through tower
Table 3–1 Invalid Data for CNS Onsite Meteorological ProgramOctober 31, 2004 – October 30, 2005
Bad Data-Spike3(B) 1/21 1900 – 210060 Meter Wind Direction
Bad Data-Spike9(B) 1/12 0800 – 1600100 Meter Wind Direction
Frozen Sensor151(A)1/3 0700 – 1/9 130010 Meter Wind Speed
Frozen Sensor/Sensor Failure
Bad Data-Spike
4153
3
(A)1/3 0700 – 1/9/ 13004/2 0600 – 8/13 16009/28 1900 – 10/31 2400(B) 1/21 1900 – 2100
60 Meter Wind Speed
Frozen Sensor
Bad Data-Spike
221
9
(A) 1/3 0700 – 1/9 13002/6 2300 – 2/9 2100(B) 1/12 0800 – 1600
100 Meter Wind Speed
Fall Calibration819/26 0900 – 09/29 1700All Parameters (A&B)
Troubleshoot – All 3 levels down
258/3 0800 – 8/4 0800All Parameters(A System Only)
Spring Calibration1033/29 1400 – 4/1 12004/4 0800 – 4/5 1500
All Parameters (A&B)
ProblemHoursMissing/Bad DataParameter
• Wind Directions from 195-245 degrees blow through tower
• Window is 25 degrees for vane and cup sensors and 30 degrees for sonic sensor
Data Availability
• 100- meter wind speed 82%• 60-meter wind speed 87%• 10-meter wind speed 88%• 100-meter wind direction 84%• 60-meter wind direction 45%• 10-meter wind direction 70%
Results
Unobstructed with no tower influence
Wind Speed Averages
0.90.97.07.8775410-MWS
1.61.510.311.8763760-MWS
1.41.412.813.97433100-MWS
Abs.Diff.B Avg.A Avg.Hours
Wind Speed Correlation
0.990.621.010.9775410-MWS
0.990.841.061.5763760-MWS
0.980.951.021.47433100-MWS
Corr.Y-int.SlopeDiff.Hours
Figure 1: 100-Meter Wind Speed Regression
y = 1.0159x + 0.9484
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
50.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0 45.0
100-Meter B (Sonic) Wind Speed
100-
Met
er A
(Cup
) Win
d S
peed
Observations: 7183
Correlation: 0.98
Figure 2: 60-Meter Wind Speed Regression
y = 1.0621x + 0.8437
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0 40.0
60-Meter B (Sonic) Wind Speed
60-M
eter
A (C
up) W
ind
Spe
ed
Observations: 7637
Correlation: 0.99
Figure 3: 10-Meter Wind Speed Regression
y = 1.0141x + 0.6284
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0
10-Meter B (Sonic) Wind Speed
10-M
eter
A (C
up) W
ind
Spe
ed
Observations: 7754
Correlation: 0.99
Wind Direction Averages
7.06.7189195614810-MWD
4.03.6180184397060-MWD
3.02.61821847375100-MWD
Abs.Diff.B Avg.A Avg.Hours
Wind Direction Correlation
0.993.081.026.7614810-MWD
0.991.301.033.6397060-MWD
0.990.871.022.67375100-MWD
Corr.Y-int.SlopeDiff.Hours
Figure 4: 100-Meter Wind Direction Regression
y = 1.0191x - 0.8727
-50
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
100-Meter B (Sonic) Wind Direction
100-
Met
er A
(Van
e) W
ind
Dir
ectio
n
Observations: 7375
Tower interference
Correlation: 0.99
Figure 5: 60-Meter Wind Direction Regression
y = 1.0271x - 1.2974
-50
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
60-Meter B (Sonic) Wind Direction
60-M
eter
A (V
ane)
Win
d D
irec
tion
Observations: 3970
Tower interference
Correlation: 0.99
Figure 6: 10-Meter Wind Direction Regression
y = 1.0191x + 3.0831
0
50
100
150
200
250
300
350
400
0 50 100 150 200 250 300 350 400
10-Meter B (Sonic) Wind Direction
10-M
eter
A (V
ane)
Win
d D
irec
tion
Observations: 6148
Tower interference
Correlation: 0.99
Tower Impacts
Wind Speed and Direction
• Wind Directions from 195-245 degrees blow through tower
• Window is 25 degrees for vane and cup sensors and 30 degrees for sonic sensor
100-m Wind Speed AveragesTower Impact
6.16.17.313.4266100-MWS-B
3.0-2.712.710.0409100-MWS-A
Abs.Diff.B Avg.A Avg.Hours
100-m Wind Speed Correlation Tower Impact
0.891.111.686.1266100-MWS-B
0.921.860.64-2.7409100-MWS-A
Corr.Y-int.SlopeDiff.Hours
Figure 10: 100-meter Wind Speed Regression When Tower Impacts System A (Cups)
y = 0.6409x + 1.8626
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.0100-Meter B (Sonic) Wind Speed
100-
Met
er A
(Cup
) Win
d S
peed
Observations: 409
Correlation: 0.92
Figure 11: 100-Meter Wind Speed Regression When Tower Impacts System B (Sonic)
y = 1.681x + 1.1097
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
40.0
45.0
0.0 5.0 10.0 15.0 20.0 25.0100-Meter B (Sonic) Wind Speed
100-
Met
er S
yste
m A
(Cup
) Win
d S
peed
Observations: 268
Correlation: 0.89
60-m Wind Speed AveragesTower Impact
3.93.96.610.528560-MWS-B
1.5-0.510.39.836460-MWS-A
Abs.Diff.B Avg.A Avg.Hours
60-m Wind Speed CorrelationTower Impact
0.97-0.021.603.928560-MWS-B
0.941.680.79-0.536460-MWS-A
Corr.Y-int.SlopeDiff.Hours
Figure 12: 60-Meter Wind Speed Regression When Tower Impacts System A (Cups)
y = 0.7889x + 1.6774
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.0 5.0 10.0 15.0 20.0 25.0 30.0 35.060-Meter B (Sonic) Wind Speed
60-M
eter
A (C
up) W
ind
Spe
ed
Observations: 364
Correlation: 0.94
Figure 13: 60-Meter Wind Speed Regression When Tower Impacts System B (Sonic)
y = 1.6029x - 0.024
0.0
5.0
10.0
15.0
20.0
25.0
30.0
35.0
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.0 18.0 20.060-MeterA (Cup) Wind Speed
60-M
eter
B (S
onic
) Win
d S
peed
Observations: 285
Correlation: 0.97
10-m Wind Speed AveragesTower Impact
2.82.84.37.122510-MWS-B
1.2-0.48.17.739410-MWS-A
Abs.Diff.B Avg.A Avg.Hours
10-m Wind Speed CorrelationTower Impact
0.95-0.121.682.822510-MWS-B
0.971.190.80-0.439410-MWS-A
Corr.Y-int.SlopeDiff.Hours
Figure 14: 10-Meter Wind Speed Regression When Tower Impacts System A (Cups)
y = 0.801x + 1.1866
0.0
5.0
10.0
15.0
20.0
25.0
0.0 5.0 10.0 15.0 20.0 25.0 30.010-Meter B (Sonic) Wind Speed
10-M
eter
A (C
up) W
ind
Spe
ed
Observations: 394
Correlation: 0.97
Figure 15: 10-Meter Wind Speed Regression When Tower Impacts System B (Sonic)
y = 1.6833x - 0.1175
0.0
5.0
10.0
15.0
20.0
25.0
30.0
0.0 2.0 4.0 6.0 8.0 10.0 12.0 14.0 16.010-Meter B (Sonic) Wind Speed
10-M
eter
A (C
up) W
ind
Spe
ed
Observations: 225
Correlation: 0.95
100-m Wind Direction AveragesTower Impact
55235240145100-MWD-B
44199203451100-MWD-A
Abs.Diff.B Avg.A Avg.Hours
100-m Wind Direction CorrelationTower Impact
0.9428.90.905145100-MWD-B
0.9712.90.964451100-MWD-A
Corr.Y-int.SlopeDiff.Hours
Figure 16: 100-Meter Wind Direction Regression When Tower Impacts System A (Vane)
190
195
200
205
210
215
220
185 190 195 200 205 210 215
100-Meter B (Sonic) Wind Direction
100-
Met
er A
(Van
e) W
ind
Dir
ectio
n
y = 0.9561x + 12.894
Observations: 451
Correlation: 0.97
Figure 17: 100-Meter Wind Direction Regression When Tower Impacts System B (Sonic)
230
232
234
236
238
240
242
244
246
248
250
228 230 232 234 236 238 240 242 244
100-Meter B (Sonic) Wind Direction
100-
Met
er A
(Van
e) W
ind
Dir
ectio
n
y = 0.8958x + 28.931
Observations: 145
Correlation: 0.94
10-m Wind Direction AveragesTower Impact
772362438010-MWD-B
6619720340210-MWD-A
Abs.Diff.B Avg.A Avg.Hours
10-m Wind Direction CorrelationTower Impact
0.954.01.0178010-MWD-B
0.9911.20.98640210-MWD-A
Corr.Y-int.SlopeDiff.Hours
Figure 18: 10-Meter Wind Direction Regression When Tower Impacts System A (Vane)
y = 0.9757x + 11.2
190
195
200
205
210
215
180 185 190 195 200 205 21010-Meter B (Sonic) Wind Direction
10-M
eter
A (V
ane)
Win
d D
irec
tion
Observations: 402
Correlation: 0.99
Figure 19: 10-Meter Wind Direction Regression When Tower Impacts System B (Sonic)
y = 1.0125x + 3.9992
232
234
236
238
240
242
244
246
248
250
252
228 230 232 234 236 238 240 242 24410-Meter B (Sonic) Wind Direction
10-M
eter
A (V
ane)
Win
d D
irec
tion
Observations: 80
Correlation: 0.95
Conclusions
• Outside of Tower wake impacts, Systems A and B are statistically the same for WS/WD.
• Outside of Tower wake impacts, all differences are small.
• WD small bias likely due to alignment errors during calibration.
Conclusions (cont’d)
• Cup anemometer records wind speed on average 1mph higher than sonic – likely due to overspeeding.
• Tower wake has greatest impact on wind speed. Differences up to 10 mph seen at wind speeds above 25 mph.
• Appears the wind speed tower impact is largest on sonic sensors – but is it?
• Data from either system are interchangeable
Conclusions (cont’d)
• Tower wake has little to no impact on wind direction on either vane or sonic sensors.
• Data from either System A (cups/vanes) or System B (sonic) are interchangeable outside of tower wake. Within wake, data scrutiny is needed either manually or with software.