A statistical analysis of wind speed distribution models in the Aburrá Valley, Colombia
Keywords:
Power density, Probability density function, Goodness of fit test, Wind speed
Abstract
The probability density functions, that model the wind speed behavior in five urban places and one rural of the Aburrá Valley in Antioquia, Colombia, are presented in this paper. The probability density functions are used to calculate the wind power density for each location, which are used to recommend several applications that could take advantage of such potential powers. Wind speeds are recorded at five monitoring stations located in the urban area of the valley and at one nearby rural station. Four probability density functions, namely, Weibull, Rayleigh, Gamma, and Lognormal, are used to represent the wind speed data histograms of each location and to select the probability density function that best fit the variability of the wind speed data. Also, four goodness of fit test are calculated. The wind power density is calculated with the probability density function that best represents the wind speed distribution to evaluate the wind power availability. The power densities reported for the five urban stations ranged from 1.38 to 4.54W/m2, and that reported for the rural station was 911.1W/m2. Taking into account the power density of each station, several applications are suggested.
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Akda, S. A. & Güler, Ö. (2010). Evaluation of wind energy investment interest and electricity generation cost analysis for Turkey. Appl. Energy, 87(8), 2574-2580.
https://doi.org/10.1016/j.apenergy.2010.03.015
Akpinar, S. A. & Akpinar, E. K. (2007). Wind energy analysis based on Maximum Entropy Principle (MEP)-type distribution function. Energy Convers. Manag., 48(4), 1140-1149.
https://doi.org/10.1016/j.enconman.2006.10.004
Carta, J. A. & Ramírez, P. (2007). Use of finite mixture distribution models in the analysis of wind energy in the Canarian Archipelago. Energy Convers. Manag., 48(1), 281-291.
https://doi.org/10.1016/j.enconman.2006.04.004
Celik, A. N. (2004). A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renew. Energy, 29(4), 593-604.
https://doi.org/10.1016/j.renene.2003.07.002
Chang, T. P. (2011). Estimation of wind energy potential using different probability density functions. Appl. Energy, 88(5), 1848-1856.
https://doi.org/10.1016/j.apenergy.2010.11.010
Dincer, F. (2011). The analysis on wind energy electricity generation status, potential and policies in the world. Renew. Sustain. Energy Rev., 15(9), 5135-5142.
https://doi.org/10.1016/j.rser.2011.07.042
Elliott, D. L. & Schwartz, M. N. (1993). Wind energy potential in the United States. International Academy of Science Project Energy, Kansas City, USA.
Fyrippis, I., Axaopoulos, P. J. & Panayiotou, G. (2010). Wind energy potential assessment in Naxos Island, Greece. Appl. Energy, 87(2), 577-586.
https://doi.org/10.1016/j.apenergy.2009.05.031
Georgakis, C. & Santamouris, M. (2008). On the estimation of wind speed in urban canyons for ventilation purposes-Part 1: Coupling between the undisturbed wind speed and the canyon wind. Build. Environ., 43(8), 1404-1410.
https://doi.org/10.1016/j.buildenv.2007.01.041
Hernández, Q., Espinosa, F., Saldaña, R. & Rivera, C. (2012). Evaluación del potencial eólico para la generación de energía eléctrica en el estado de Veracruz, México. Dyna, 171: 215-221.
Johnson, G. L. (1985). Wind energy systems. Englewood Cliffs: Prentice-Hall.
Li, D., Wang, S. & Yuan, P. (2010). A review of micro wind turbines in the built environment. Power and Energy Engineering Conference (APPEEC), Chengdu, Asia.
https://doi.org/10.1109/APPEEC.2010.5448223
Li, M. & Li, X. (2005a). Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada. Energy Convers. Manag., 46(18), 3014-3033.
https://doi.org/10.1016/j.enconman.2005.02.011
Li, M. & Li, X. (2005b). MEP-type distribution function: a better alternative to Weibull function for wind speed distributions. Renew. Energy, 30(8), 1221-1240.
https://doi.org/10.1016/j.renene.2004.10.003
Lo Brano, V., Orioli, A., Ciulla, G. & Culotta, S. (2011). Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renew. Energy, 36(3), 1026-1039.
https://doi.org/10.1016/j.renene.2010.09.009
Massey, Jr, F. J. (1951). The Kolmogorov-Smirnov test for goodness of fit. J. Amer. Statis. Assoc., 46(253), 68-78.
https://doi.org/10.1080/01621459.1951.10500769
Picard, A., Davis, R., Gläser, M., & Fujii, K. (2008). Revised formula for the density of moist air (CIPM-2007). Metrologia, 45(2), 149-155.
https://doi.org/10.1088/0026-1394/45/2/004
Ramírez, P., & Carta, J. A. (2005). Influence of the data sampling interval in the estimation of the parameters of the Weibull wind speed probability density distribution: a case study. Energy Convers. Manag., 46(15-16), 2419-2438.
https://doi.org/10.1016/j.enconman.2004.11.004
Safari, B. (2011). Modeling wind speed and wind power distributions in Rwanda. Renew. Sustain. Energy Rev., 15(2), 925-935.
https://doi.org/10.1016/j.rser.2010.11.001
Safari, B. & Gasore, J. (2010). A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renew. Energy, 35(12), 2874-2880.
https://doi.org/10.1016/j.renene.2010.04.032
Salameh, Z. & Nandu, C. V. (2010). Overview of building integrated wind energy conversion systems. IEEE Power and Energy Society General Meeting, Minneapolis, USA.
https://doi.org/10.1109/PES.2010.5590054
Seguro, J. & Lambert, T. (2000). Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. J. Wind Eng. Ind. Aerodyn., 85(1), 75-84.
https://doi.org/10.1016/S0167-6105(99)00122-1
Zhou, J., Erdem, E., Li, G., & Shi, J. (2010). Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites. Energy Convers. Manag., 51(7),1449-1458.
https://doi.org/10.1016/j.enconman.2010.01.020
How to Cite
Saavedra Montes, A. J., Amaya Martínez, P. A., & Arango Zuluaga, E. I. (2014). A statistical analysis of wind speed distribution models in the Aburrá Valley, Colombia. CT&F - Ciencia, Tecnología Y Futuro, 5(5), 121–136. https://doi.org/10.29047/01225383.36
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Published
2014-12-15
Section
Scientific and Technological Research Articles
