WIND RESOURCE OF MICROREGIONS IN SOUTH AND NOTHEAST OF BRAZIL: AN EVALUATION OF METEROLOGICAL DATA AND COMPUTACIONAL TOOL

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1 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium WIN RESOURCE OF MICROREGIONS IN SOUTH AN NOTHEAST OF BRAZIL: AN EVALUATION OF METEROLOGICAL ATA AN COMPUTACIONAL TOOL Jorge Antonio Villar Alé¹, Cássia Pederiva de Oliveira¹, avi 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) Av. Ipiranga, 661 Prédio 3, Sala 12 - CEP: ; Tel: ² University of Coimbra, Coimbra, Portugal ABSTRACT: The paper show the wind characterization of two microregions with the purpose of evaluating the wind resource. ata 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 7 22' " S and longitude 36 31'3" W with an altitude of 67 m above sea level, the second tower is located at latitude 29 26' 3" S a nd longitude 3 9' 23" W with an altitude of 9 m. First of all, statistical treatment was performed of data obtained from two propeller anemometers located at 2 m and 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. INTROUCTION 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 AN METEOROLOGICAL INFORMATIONS The meteorological data studied refers to two meteorological stations of the National Organization System of Environment ata (SONA 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 (2 m and m).

2 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium (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. Table 1: Information of meteorological towers. Station Name Latitude Longitude Altitude (m) Height of Measurement sensors (m) period São João de Cariri (SJC) 7 22 S W 6 2 e 2 São Martinho da Serra (SMS) S W 9 2 e 2 Altogether, the SONA network collected meteorological data in the São João do Cariri Meteorological Station during the months from January to ecember of 2, while in the São Martinho da Serra Meteorological Station were collected 3136 data during the same period of 2. The amount of collected data, % 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 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 2 m and m. The frequency of sampling was 1 minutes which corresponds to speed and direction of the winds and temperature. 3. STATISTICAL WIN ATA 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; ) Weibull analysis using the energy pattern factor method [2]; ) estimation of Weibull distribution at 1 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 1 m with the power curve characteristically of a particular turbine AVERAGE WIN SPEES The Fig. 2 shows the seasonal and annual average speeds for the two stations studied in heights of 2 and meters. For height of m, we obtained the higher average speed to São João do Cariri Meteorological Station during the spring with a value of 6.7 m/s whereas lower was 3.7 m/s in the fall.

3 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium Already the São Martinho da Serra Meteorological Station had its higher average speed record during the winter, corresponding to 6.7 m/s, in the summer had its record the lowest value, which was 6.1 m/s. / s) 6 (m E S P IN W2 ANEMOMETER (2 m) ANEMOMETER ( m),,3 3,7 3,17,27, 6,7,77,2,7 / s) 6 (m E S P IN W2 ANEMOMETER (2 m) ANEMOMETER ( m) 6,7 6,1,3,3 6,7 6,3 6,,6,1,3 SUMMER FALL WINTER SPRING ANNUAL AVERAGE SEASONS OF THE YEAR AN ANNUAL AVERAGE (SJC) SUMMER FALL WINTER SPRING ANNUAL AVERAGE SEASON OF THE YEAR AN ANNUAL AVERAGE (SMS) Figure 2: Seasonal and annual average wind speeds at 2 m and m PREOMINANT IRECTIONS OF THE WIN 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. WEST NW NORTH % 3% 3% 2% 2% 1% 1% % % NE EAST WEST NW NORTH 2% 2% 1% 1% % % NE EAST SW SE SW SE SOUTH SOUTH Figure 3: Annual wind roses at m AILY PATTERN WINS Fig. (a) shows the daily pattern of winds of average hour speed for SJC Station in 2. We can observe a decrease of velocity from 22:h until 9:h. After this time the speed starts to increase until 12:h, remaining stable until 1:h. After this time have a slight increase reaching a maximum at 22:h and it s subsequently decline of daily cycle.

4 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium Fig. (b) shows the daily pattern of winds of average hour speed for SMS Station in 2 where we can observe a increase of velocity from 1:h reaching its maximum at approximately 3:h, remaining constant until about 11:h. After this time have a slight decrease until close its daily cycle. 7 Average (m) Average (2m) 7 Average (m) Average (2m) 6 3 WIN SPEE ([m/s) : 3: 6: 9: 12: 1: 1: 21: TIME 2 : 3: 6: 9: 12: 1: 1: 21: TIME 3.. WEIBULL ISTRIBUTION ANALISYS Figure : aily regime of winds of average hour speed. Fig. shows the frequency histograms of wind speeds, the annual Weibull curves at meters to SJC Station and SMS, respectively. FREQUENCIY (%) 2% 1% 1% % k=2,3 c=,91 m/s FREQUENCY (%) 2% 1% 1% % k=2,1 C=7,3 m/s % % Figure : Annual wind speeds distribution ( m). 3.. ESTIMATIVE OF WEIBULL ISTRIBUTION AT 1 m The estimative of Weibull distribution at 1 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. FREQUENCY (%) 2% 1% 1% % SMS Station (k=3.3; c=.7m/s) SJC Station (k=2.62; c=6.77m/s) % Figure 6: Estimative of Weibull distribution (1 m).

5 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium 3.6. WIN 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. 1, 1,2 ) (m,9 E S N H G,6 U O R,3,,99,7 1,21 1,1, SÃO JOÃO O CARIRI -PB SÃO MARTINHO A SERRA -RS,1,63,21 1, POWER EXPONENT,,3,2,1,2,17,3,3,2,23 SÃO JOÃO O CARIRI -PB SÃO MARTINHO A SERRA -RS,2,2,2,17 SUMMER FALL WINTER SPRING ANNUAL SUMMER FALL WINTER SPRING ANNUAL SEASONS OF THE YEAR AN ANUAL SEASONS OF YEAR AN ANNUAL (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 1 m. The Fig. 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. m/s at 1 m while SMS Station shown a wind speed of approximately. m/s at the same height. HEIGHT (m) Logarithmical Law Z=,21m Exponential Law α=,2 Tower data (m ) T H E IG H Logarithmical Law Z=1,m Exponential Law a=,2 Tower data Station Station Figure : Annual wind speed profile.

6 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium 3.7. WIN RESOURCE MAPS We conducted a quantitative analysis of wind power of the two sites correlating the Weibull curves at 1 m with the power curve characteristically of a particular turbine with a nominal power of 2.3MW. The Fig (a) and Fig (b) shown the seasonal and annual estimative of wind power generated and the capacity factor to both stations. 7 ) h W 6 (M N T IO C U O R P 3 E R W O 2 P IN 1 W SÃO JOÃO O CARIRI -PB SÃO MARTINHO A SERRA -RS SUMMER FALL WINTER SRPING ANNUAL SEASONS OF YEAR AN ANNUAL 6 ) (% R T O C F A 3 Y IT C A P 2 A C 1 SÃO JOÃO O CARIRI -PB SÃO MARTINHO A SERRA -RS 17,29 2,17 7,1 3,73 17,22 3,3 2,1 3,6 16, 33,3 SUMMER FALL WINTER SRPING ANNUAL SEASONS OF YEAR AN ANNUAL (a) Power generated (MWh) (b) Capacity factor (%) Figure 9: Estimation of power generated in both stations.. COMPUTATIONAL ANALISYS WINSTATION Using the data analyzed, we generated a velocity field in both regions using the WindStation software [] which allows the realization of a numerical simulation of turbulent flow in flat and complex terrains, determining a speed field in 3 mesh to evaluation of wind resources []. The Fig. 1(a) and Fig. 1(b) shown the terrain elevation and the microsite details around the tower with a computational domain to SJC Station and SMS. (SJC) (SMS) Figure 1: 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.2 m and in clean locations the roughness used was.1m while to SMS Station the standard roughness used was. m and in location with forest the value used was 1. m. Specific tables of roughness [3] shown that a roughness of 1. 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.

7 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium.1. VELOCITY FIEL The Fig. 11 and Fig. 12 shown a wind map in areas around the SJS Station and SMS at 1 m The red points in Fig. 11 represent the higher speed, which correspond to 6.66 m/s to SJC Station and.9 m/s to SMS Station; In the same image the blank points correspond to the lower speeds which are.36 m/s and.1 m/s to SJC Station and SMS respectively. The velocity obtained where are located the stations SJC and SMS are.6 m/s and.7 m/s respectively at 1 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: Lower speed São João do Cariri Higher speed Speed (m/s) Microregion Wind Map Tower São João do Cariri 1 meters Figure 11: Wind map - São João do Cariri (1 m).

8 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium Location: Lower speed São Martinho da Serra Speed (m/s) Microregion Wind Map Higher speed Tower Torre São Matinho da Serra 1 meters Figure 12: Wind map - São Martinho da Serra (1 m)..2. COMPARISON OF WIN 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. m/s. After this velocity a slight deviation of speed exist showing that the profile generated by WindStation presents speeds slightly higher until m. After m the both profiles approach considerably until 7 m and after this height a slight divergence between the profiles exist. In general, the percentage differences of wind speeds are between 2% at %. 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 1 m both wind speed profiles are approximately. m/s.

9 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium HEIGHT (m) Exponential Law α=,2 WindStation HEIGHT (m) 1 9 Exponential Law α=,2 9 WindStation BRAZILIAN WIN POWER MAP Figure 13: Comparison of wind shear. The seasonal wind speeds showed to m, according with CRESCESB [6], in the Brazilian Wind Power Map are compared with wind speed ( m) coming from statistical analysis of both stations. The comparison is shown in Fig SÃO JOÃO O CARIRI -PB CRESESB 6 SÃO MARTINHO A SERRA - RS CRESESB WINF SPEE (m/s) 2 2 SUMMER FALL WINTER SPRING SUMMER FALL WINTER SPRING SEASONS OF YEAR SEASONS OF YEAR Figure 1: Comparison of wind seasonal speed with Brazilian Wind Power Map.

10 EWEA Europe s Premier Wind Energy Event 1-17 March 211, Brussels, Belgium 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 2 and 2 respectively, with aim of evaluating the wind resource of both locations. Higher average wind speeds occurred between October (6. m/s) and August (7. m/s) at m for SJC Station and SMS respectively. The seasonal and annual wind roses at 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 1 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=.7 m/s and k=3.3. About the extrapolation of wind shear until 1 m the SJC Station showed highest speeds in spring (7.27 m/s) and SMS Station in winter (.3 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. % to SJC Station and 33.3 % 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. m/s to 6. m/s while the SMS Station the speeds achieved was between 6. m/s and. 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 (.2 m/s) of presents by CRESESB (.2 m/s). As for SMS Station a contrast was observed where all seasons presents higher values for measurement data, staying annual average of 6. m/s while the CRESESB presents a annual average wind speed of.7 m/s. 7. REFERENCES [1] SONA. Sistema nacional de dados ambientais. Ministério da Ciência e Tecnologia. Available in: < Access at 3 April. 29. [2] ALÉ, J. A. V.; BÚRIGO, V. C.; SIMIONI, G. C. da S.. Wind Resource escription Evaluating A New Method Of etermining The Weibull Parameters Near A Forestry Are European Wind Energy Conference & Exhibition 21:. Varsóvia, Polônia. April de 21. [3] Principios de Conversion de la Energia Eolica, Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas Ed. CIEMAT. Madrid, 21. [] LOPES, A. M. G. WindStation. User s Manual. Version Available in: < Access at: 21 ecember 21. [] LOPES, A. M. G. WindStation - A Software For The Simulation Of Atmospheric Flows Over Complex Topography, Journal of Environmental Modeling & Software, Vol.1, N.1, pp. 1-6, 23. [6] BRITO, S. S. B. Centro de Referência para Energia Solar e Eólica CRESESB. Atlas do Potencial Eólico Brasileiro. Available in: < Access at 21 ecember 21.