EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
WIND RESOURCE OF MICROREGIONS IN SOUTH AND NOTHEAST OF BRAZIL: AN EVALUATION
OF METEROLOGICAL DATA AND COMPUTACIONAL TOOL
Jorge Antonio Villar Alé¹, Cássia Pederiva de Oliveira¹,
Davi Ezequiel François¹, Antonio Manuel Gameiro Lopes²
¹ Pontifical Catholic University of Rio Grande do Sul, Porto Alegre, Brazil
Faculty of Engineering - Wind Energy Center (CE-EÓLICA) – www.pucrs.br/ce-eolica
Av. Ipiranga, 6681– Prédio 30, Sala 120 - CEP: 90619-900; Tel: +55 51 3353 4438
² University of Coimbra, Coimbra, Portugal
ABSTRACT: The paper show the wind characterization of two microregions with the purpose of
evaluating the wind resource. Data from two meteorological towers was used: one in São João do Cariri,
State of Paraíba, and the other in São Martinho da Serra, State of Rio Grande do Sul. The first tower is
located at latitude 07° 22' 54" S and longitude 36° 31'38" W with an altitude of 467 m above sea level, the
second tower is located at latitude 29° 26' 34" S a nd longitude 53° 49' 23" W with an altitude of 489 m.
First of all, statistical treatment was performed of data obtained from two propeller anemometers located
at 25 m and 50 m tall. Subsequently, the information of the processed data were used to generate a
velocity field in regions with the aid of specific software (WindStation), which allowed evaluate the wind
resource to generate a velocity field of study sites. The methodology developed can be used to evaluate
wind power energy, as well as choice the location for the implementation of future wind sites, in order to
placement the generators in relation of the land topography. The study allowed to determine
computationally the velocity field in the local and reproduce the wind shear compared with the wind profile
obtained with the statistical treatment of data from the towers. Furthermore, it was possible to compare
the information’s between themselves, as well as correlating them with the Brazilian Wind Power Map.
1. INTRODUCTION
To estimate the generating capacity of wind farms is necessary a thorough analyses of a selected
area. So, meteorological tower are installed colleting at least one year of data for subsequent statistical
analyses and specific software analyses. This paper show the statistical analyses of meteorological data
from two distinct regions of Brazil, one located at northeast of country, in São João do Cariri (SJC) and
other located at south, in São Martinho da Serra (SMS). After the data processing we performed a
WindStation simulation, which allows the numerical simulation of turbulent flow, may perform analyses in
flat and complex terrains.
2. LOCAL AND METEOROLOGICAL INFORMATIONS
The meteorological data studied refers to two meteorological stations of the National Organization
System of Environment Data (SONDA network). This network is a conception of the National Institute for
Space Research (INPE) with aid from the Ministry of Science and Technology (MCT) which aims to raise
and improve the solar and wind energy resources database in Brazil [1].
The first station studied, called São João do Cariri Meteorological Station (SJC Station) is located
in the northeast region of Brazil, state of Paraíba, in a city called São João do Cariri; the second station is
located in the south region of the country, state of Rio Grande do Sul, in the city of São Martinho da
Serra, thus this station is called São Martinho da Serra Meteorological Station (SMS Station). The Fig. 1
shows the location of the meteorological towers in their respective states and also the height of sensors
for data collection (25 m and 50 m).
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
(b) State of Paraíba (SJC)
(a) Brazilian map
(c) State of Rio Grande do Sul (SMS)
(d) Height of the sensors
Figure 1: (a) Map of Brazil and (b,c) states where meteorological tower are installed; (d) height of
the sensors.
The Tab. 1 shows the exact location of the two meteorological towers with the location
coordinates, altitude, height of sensors and measurement period.
Station Name
São João de Cariri
(SJC)
São Martinho da
Serra (SMS)
Table 1: Information of meteorological towers.
Height of
Latitude
Longitude
Altitude (m)
sensors (m)
Measurement
period
07°22’54”S
36°31’38”W
486
25 e 50
2008
29°26’34”S
53°49’23”W
489
25 e 50
2005
Altogether, the SONDA network collected 316194 meteorological data in the São João do Cariri
Meteorological Station during the months from January to December of 2008, while in the São Martinho
da Serra Meteorological Station were collected 315360 data during the same period of 2005. The amount
of collected data, 5% were considered unfit for use in the SJC Station analysis by have wrong values or
be physically impossible, in the SMS Station only negative wind speeds were changed to 0 m/s, the rest
of data were used without any modification.
In both cases, for data collection was used two propeller anemometers and one temperature
sensor installed in each tower at 25 m and 50 m. The frequency of sampling was 10 minutes which
corresponds to speed and direction of the winds and temperature.
3. STATISTICAL WIND DATA ANALYSIS
Based on data of speed and direction of the wind and temperature, we performed the statistical
analyses with the aim of determining the behavior of the wind to further evaluation of the wind power.
The statistical analysis of data was performed using a data sheet. This way, we obtained: 1)
average wind speeds; 2) predominant directions; 3) daily pattern of wind speed; 4) Weibull analysis using
the energy pattern factor method [2]; 5) estimation of Weibull distribution at 100 m using the extrapolation
equations [3] and; 6) the wind shear using the exponential law and logarithmic law.
After the statistical analysis we performed a quantitative analysis of wind power for the two
regions, correlating the Weibull curves at 100 m with the power curve characteristically of a particular
turbine.
3.1. AVERAGE WIND SPEEDS
The Fig. 2 shows the seasonal and annual average speeds for the two stations studied in heights
of 25 and 50 meters. For height of 50 m, we obtained the higher average speed to São João do Cariri
Meteorological Station during the spring with a value of 6.47 m/s whereas lower was 3.75 m/s in the fall.
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
Already the São Martinho da Serra Meteorological Station had its higher average speed record during the
winter, corresponding to 6.87 m/s, in the summer had its record the lowest value, which was 6.10 m/s.
ANEMOMETER (25 m)
8
s)/ 6
m
(
D
EE 4
P
S
D
N
I
W2
ANEMOMETER (25 m)
ANEMOMETER (50 m)
6,57
6,10
5,35
5,03
8
ANEMOMETER (50 m)
6,47
5,44
5,77
5,27
4,83
5,24
4,57
4,48
3,75
3,17
0
s)/ 6
m
(
D
EE 4
P
S
D
N
I
W2
6,87
5,60
6,50
6,43
5,41
5,35
0
SUMMER
FALL
WINTER
SPRING
ANNUAL
AVERAGE
SUMMER
SEASONS OF THE YEAR AND ANNUAL AVERAGE (SJC)
FALL
WINTER
SPRING
SEASON OF THE YEAR AND ANNUAL AVERAGE (SMS)
(a) São João do Cariri
ANNUAL
AVERAGE
(b) São Martinho da Serra
Figure 2: Seasonal and annual average wind speeds at 25 m and 50 m.
3.2. PREDOMINANT DIRECTIONS OF THE WIND
The annual wind roses are shown in Fig. 3 where we can observe the predominant direction was
south-southeast to the SJC Station and southeast to the SMS Station. Also it is possible observe the
occurrence of strong winds in SMS Station if compared with SJC Station.
NORTH
40%
35%
30%
25%
20%
15%
10%
5%
0%
NW
WEST
NORTH
25%
NE
20%
NW
10%
5%
EAST
SW
WEST
SE
2-4
4-6
6-8
EAST
0%
SW
SE
SOUTH
SOUTH
0-2
NE
15%
8 - 10
10 - 12
12 - 14
0- 2
2- 4
4- 6
6- 8
8 - 10
10 - 12
12 - 14
14 - 16
WIND SPEED (m/s)
WIND SPEED (m/s)
(a) São João do Cariri
(b) São Martinho da Serra
Figure 3: Annual wind roses at 50 m.
3.3. DAILY PATTERN WINDS
Fig. 4(a) shows the daily pattern of winds of average hour speed for SJC Station in 2008. We can
observe a decrease of velocity from 22:00h until 09:00h. After this time the speed starts to increase until
12:00h, remaining stable until 18:00h. After this time have a slight increase reaching a maximum at
22:00h and it’s subsequently decline of daily cycle.
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
Fig. 4(b) shows the daily pattern of winds of average hour speed for SMS Station in 2005 where
we can observe a increase of velocity from 18:00h reaching its maximum at approximately 03:00h,
remaining constant until about 11:00h. After this time have a slight decrease until close its daily cycle.
8
8
7
WIND SPEED ([m/s)
7
WIND SPEED (m/s)
Average (50m)
Average (50m)
Average (25m)
6
5
4
3
Average (25m)
6
5
4
3
2
2
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00
TIME
TIME
(a) São João do Cariri
(b) São Martinho da Serra
Figure 4: Daily regime of winds of average hour speed.
3.4. WEIBULL DISTRIBUTION ANALISYS
Fig. 5 shows the frequency histograms of wind speeds, the annual Weibull curves at 50 meters to
SJC Station and SMS, respectively.
20%
20%
k=2,43
c=5,91 m/s
FREQUENCY (%)
FREQUENCIY (%)
15%
10%
5%
k=2,81
C=7,30 m/s
15%
10%
0%
5%
0%
1
3
5
7
9
11
13
15
17
1
3
5
WIND SPEED (m/s)
7
9
11
13
15
17
WIND SPEED (m/s)
(a) São João do Cariri
(b) São Martinho da Serra
Figure 5: Annual wind speeds distribution (50 m).
3.5. ESTIMATIVE OF WEIBULL DISTRIBUTION AT 100 m
The estimative of Weibull distribution at 100 m are shown in Fig. 6, correlating both locations. We
can observe the Weibull function of SMS Station presents a characteristically curve sparser, showing
higher wind speeds, the SJC Station presents condensed and low wind speeds.
20%
SMS Station (k=3.03; c=8.87m/s)
FREQUENCY (%)
SJC Station (k=2.62; c=6.77m/s)
15%
10%
5%
0%
1
3
5
7
9
11
13
15
17
WIND SPEED (m/s)
Figure 6: Estimative of Weibull distribution (100 m).
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
3.6. WIND SHEAR PROFILE
Using the exponential law and the logarithmical law was possible determinate the seasonal and
annual values of surface roughness and the power exponent to SJC Station and SMS which are shown in
the Fig. 7. We can observe a lower roughness to SJC Station, feature of flat terrains, while SMS shows a
higher roughness, which may be due of significant obstacles in wind route like forest or constructions
near to the data collection for example.
0,4
1,21
SÃO JOÃO DO CARIRI -PB
SÃO MARTINHO DA SERRA -RS
1,18
1,2
1,00
0,99
)
(m 0,9
SS
E
N
H
G 0,6
U
O
R
0,63
0,57
0,44
0,30
0,28
0,3
0,24
SÃO JOÃO DO CARIRI -PB
SÃO MARTINHO DA SERRA -RS
0,28
0,25
0,30
0,23
0,20
0,2
0,17
0,17
0,1
0,21
0,3
POWER EXPONENT
1,5
0,10
0,08
0
0
SUMMER
FALL
WINTER
SPRING
SUMMER
ANNUAL
FALL
WINTER
SPRING
ANNUAL
SEASONS OF YEAR AND ANNUAL
SEASONS OF THE YEAR AND ANUAL
(a) Surface roughness
(b) Power exponent
Figure 7: Roughness and empirical constant.
Using the surface roughness and the power exponent we extrapolated the wind profile until 100
m. The Fig. 8 shown the annual wind profile where we can observe higher velocities in SMS Station if
compared with SJC, this last station present a wind speed of about 6.0 m/s at 100 m while SMS Station
shown a wind speed of approximately 8.0 m/s at the same height.
100
100
95
90
HEIGHT (m)
85
Logarithmical Law Z0=0,21m
95
Logarithmical Law Z0=1,00m
Exponential Law α=0,20
90
Exponential Law a=0,28
Tower data
85
Tower data
80
80
75
75
70
70
65
65
60
40
60
)
m
( 55
T
H 50
G
IE
H 45
40
35
35
30
30
25
25
20
20
15
15
10
10
5
5
55
50
45
0
0
0
3
6
9
0
3
WIND SPEED (m/s)
(a) São João do Cariri Station
6
WIND SPEED (m/s)
(b) São Martinho da Serra Station
Figure 8: Annual wind speed profile.
9
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
3.7. WIND RESOURCE MAPS
We conducted a quantitative analysis of wind power of the two sites correlating the Weibull
curves at 100 m with the power curve characteristically of a particular turbine with a nominal power of
2.3MW. The Fig 5(a) and Fig 5(b) shown the seasonal and annual estimative of wind power generated
and the capacity factor to both stations.
7000
)
h
W 6000
M
(
N 5000
O
IT
C
U
D 4000
O
R
P 3000
ER
W
O 2000
P
D
N
I 1000
W
0
6684
SÃO JOÃO DO CARIRI -PB
SÃO JOÃO DO CARIRI -PB
SÃO MARTINHO DA SERRA -RS
3356
1999
1771
1406
1269
872
1503
879
50
)
%
(
R 40
O
T
C
A
F 30
Y
TI
C
A 20
P
A
C
SÃO MARTINHO DA SERRA -RS
38,35
34,73
FALL
WINTER
SRPING
ANNUAL
SEASONS OF YEAR AND ANNUAL
33,03
30,46
28,17
25,14
17,29
17,22
16,54
7,01
10
358
SUMMER
60
0
SUMMER
FALL
WINTER
SRPING
ANNUAL
SEASONS OF YEAR AND ANNUAL
(a) Power generated (MWh)
(b) Capacity factor (%)
Figure 9: Estimation of power generated in both stations.
4. COMPUTATIONAL ANALISYS – WINDSTATION
Using the data analyzed, we generated a velocity field in both regions using the WindStation
software [4] which allows the realization of a numerical simulation of turbulent flow in flat and complex
terrains, determining a speed field in 3D mesh to evaluation of wind resources [5]. The Fig. 10(a) and
Fig. 10(b) shown the terrain elevation and the microsite details around the tower with a computational
domain to SJC Station and SMS.
(a) São João do Cariri (SJC)
(b) São Martinho da Serra (SMS)
Figure 10: Terrain elevation and microsite detail around the towers.
About roughness, a map was generated to both location with aid of satellites images, the map
contain values of roughness found through statistical analysis and specifics tables. To SJC Station the
standard roughness used was 0.2 m and in clean locations the roughness used was 0.1m while to SMS
Station the standard roughness used was 0.8 m and in location with forest the value used was 1.0 m.
Specific tables of roughness [3] shown that a roughness of 1.0 m is high corresponding a urban areas
and not a rural area where as is installed the SMS Station, in this way should be conduct a specific study
to verify the reason for the high roughness at the site.
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
4.1. VELOCITY FIELD
The Fig. 11 and Fig. 12 shown a wind map in areas around the SJS Station and SMS at 100 m
The red points in Fig. 11 represent the higher speed, which correspond to 6.66 m/s to SJC Station and
8.89 m/s to SMS Station; In the same image the blank points correspond to the lower speeds which are
5.36 m/s and 5.18 m/s to SJC Station and SMS respectively. The velocity obtained where are located the
stations SJC and SMS are 5.86 m/s and 8.07 m/s respectively at 100 m.
The turbulence intensity found to both station are 9% to SJC and 7% to SMS with a direction
south-southeast to SJC and southeast to SMS.
Location:
São João
do Cariri
Lower speed
Higher speed
Microregion Wind Map
Speed (m/s)
Tower
São João do Cariri
100 meters
Figure 11: Wind map - São João do Cariri (100 m).
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
Location:
Lower speed
São Martinho
da Serra
Microregion Wind Map
Speed (m/s)
Tower
Torre
São Matinho da Serra
Higher speed
100 meters
Figure 12: Wind map - São Martinho da Serra (100 m).
4.2. COMPARISON OF WIND SHEAR
The Fig. 13 shown the wind shear generated by WindStation and the wind shear obtained with
the information of meteorological towers. In Fig. 13(a) we can observe that have a good concordance of
lower velocities until 3.0 m/s. After this velocity a slight deviation of speed exist showing that the profile
generated by WindStation presents speeds slightly higher until 40 m. After 40 m the both profiles
approach considerably until 70 m and after this height a slight divergence between the profiles exist.
In general, the percentage differences of wind speeds are between 2% at 5%. However Fig.
13(b) the wind profile shown by WindStation is higher that the wind shear by exponential law, we can also
observe that at 100 m both wind speed profiles are approximately 8.0 m/s.
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
100
100
95
90
Exponential Law α=0,28
90
WindStation
85
85
80
80
75
75
70
70
65
65
60
60
55
55
HEIGHT (m)
HEIGHT (m)
95
Exponential Law α=0,20
50
45
50
45
40
40
35
35
30
30
25
WindStation
25
20
20
15
15
10
10
5
5
0
0
2
4
6
8
0
10
0
WIND SPEED (m/s)
2
4
6
8
10
WIND SPEED (m/s)
(a) São João do Cariri
(b) São Martinho da Serra
Figure 13: Comparison of wind shear.
5. BRAZILIAN WIND POWER MAP
The seasonal wind speeds showed to 50 m, according with CRESCESB [6], in the Brazilian Wind
Power Map are compared with wind speed (50 m) coming from statistical analysis of both stations. The
comparison is shown in Fig. 14.
8
8
SÃO MARTINHO DA SERRA - RS
CRESESB
SÃO JOÃO DO CARIRI -PB
CRESESB
6
WIND SPEED (m/s)
WINF SPEED (m/s)
6
4
2
4
2
0
0
SUMMER
FALL
WINTER
SEASONS OF YEAR
(a) São João do Cariri
SPRING
SUMMER
FALL
WINTER
SEASONS OF YEAR
(b) São Martinho da Serra
Figure 14: Comparison of wind seasonal speed with Brazilian Wind Power Map.
SPRING
EWEA 2011 - Europe’s Premier Wind Energy Event
14-17 March 2011, Brussels, Belgium
Poster - PO 142
6. CONCLUSIONS
We conducted the statistical analysis of meteorological data from São João do Cariri
Meteorological Station and São Martinho da Serra Meteorological Station for the years 2008 and 2005
respectively, with aim of evaluating the wind resource of both locations.
Higher average wind speeds occurred between October (6.85 m/s) and August (7.45 m/s) at
50 m for SJC Station and SMS respectively. The seasonal and annual wind roses at 50 m shown a
annual south-southeast predominance to SJC Station and Southeast to SMS Station, the wind roses also
confirmed the seasonal average wind speed.
Weibull analysis showed an estimation of wind speed at 100 m with a occurrence profile in lowers
speeds to São João do Cariri Station with c=6.77 m/s and k=2.62. São Martinho da Serra Station showed
great dispersion with a c=8.87 m/s and k=3.03.
About the extrapolation of wind shear until 100 m the SJC Station showed highest speeds in
spring (7.27 m/s) and SMS Station in winter (8.43 m/s), we observed also a higher roughness in São
Martinho da Serra if compared with São João do Cariri, the same behavior was observed to power
exponent, which influence directly in the wind shear.
At last we estimated the wind energy production to a turbine of 2.3 MW, which presents a yield of
16.54 % to SJC Station and 33.03 % to SMS Station. Thus, it is possible conclude that São João do Cariri
shown wind power results that can be use to small wind turbines, but unattractive to a large enterprise
because it have a low yield. However, São Martinho da Serra Station presents better results for large
wind turbine, since it have a better yield.
The WindStation simulation showed a velocity field to both meteorological station, which to SJC
Station the wind speeds was around 5.5 m/s to 6.5 m/s while the SMS Station the speeds achieved was
between 6.5 m/s and 8.5 m/s, which present higher values and with a sparse speed interval. About the
wind shear of SJC Station, it shown closer to exponential law and velocity measurements at two heights
of the tower. To SMS Station the wind shear of WindStation shown higher speeds if compared with
exponential law and average speeds in the tower, so, should be performed a more refined study with new
simulations checking the roughness effect at local.
In comparison with the Brazilian Wind Power Map we observed that just in spring the
measurements data of SJC Station presents a higher average, while the annual average was lower
(5.24 m/s) of presents by CRESESB (5.52 m/s). As for SMS Station a contrast was observed where all
seasons presents higher values for measurement data, staying annual average of 6.50 m/s while the
CRESESB presents a annual average wind speed of 5.47 m/s.
7. REFERENCES
[1] SONDA. Sistema nacional de dados ambientais. Ministério da Ciência e Tecnologia. Available in:
<http://sonda.cptec.inpe.br/basedados/sjcariri.html> Access at 03 April. 2009.
[2] ALÉ, J. A. V.; BÚRIGO, V. C.; SIMIONI, G. C. da S.. Wind Resource Description Evaluating A
New Method Of Determining The Weibull Parameters Near A Forestry Are European Wind Energy
Conference & Exhibition 2010:. Varsóvia, Polônia. April de 2010.
[3] Principios de Conversion de la Energia Eolica, Centro de Investigaciones Energéticas,
Medioambientales y Tecnológicas Ed. CIEMAT. Madrid, 2001.
[4] LOPES, A. M. G. WindStation. User’s Manual. Version 2.0.2. 2009.
Available in:
<http://www.easycfd.net/windstation/default_files/Page551.htm> Access at: 21 December 2010.
[5] LOPES, A. M. G. WindStation - A Software For The Simulation Of Atmospheric Flows Over
Complex Topography, Journal of Environmental Modeling & Software, Vol.18, N.1, pp. 81-86, 2003.
[6] BRITO, S. S. B. Centro de Referência para Energia Solar e Eólica – CRESESB. Atlas do Potencial
Eólico Brasileiro. Available in: <http://www.cresesb.cepel.br/>. Access at 21 December 2010.