Feng-Jiao Liu1, Hong-Hsi Ko2, Shyi-Shiun Kuo2, Ying-Hsin Liang2 and Tian-Pau Chang This email address is being protected from spambots. You need JavaScript enabled to view it.2

1Department of Electrical and Information Technology, Nankai University of Technology, Nantou, Taiwan 542, R.O.C.
2Department of Multimedia Animation and Applications, Nankai University of Technology, Nantou, Taiwan 542, R.O.C.


 

Received: November 22, 2013
Accepted: August 14, 2014
Publication Date: September 1, 2014

Download Citation: ||https://doi.org/10.6180/jase.2014.17.3.09  


ABSTRACT


Some wind speed distributions in Taiwan have been found deviating from conventional Weibull distribution. In this paper the mixture Weibull distribution was adopted to analyze the wind data observed at three wind sites having different climatic environments. The Kolmogorov-Smirnov test and wind potential energy were considered as indicators to show how the mixture Weibull function characterizes wind speed distribution. Relevant mathematical expressions are derived originally for wind energy assessment. The results show that the mixture Weibull function performs quite better than a conventional Weibull function particularly for a region where the wind speed distribution reveals two humps on it. The similar result is obtained also when wind power density is considered. The maximum errors of cumulative distribution function between observation data and mixture Weibull function are always below the critical value of 95% confidence level in Kolmogorov-Smirnov test. The relative percentage errors of wind potential energy between time-series data and theoretical values from mixture Weibull function never exceed 0.1%. It is found that the distribution pattern of wind speed would affect a lot to the electrical energy generated by an ideal turbine.


Keywords: Wind Characteristics, Wind Speed, Wind Power, Weibull Distribution, Mixture Weibull Distribution


REFERENCES


  1. [1] Carta, J. A., Ramirez, P. and Velazquez, S., “A Review of Wind Speed Probability Distributions Used in Wind Energy Analysis Case Studies in the Canary Islands,” Renewable and Sustainable Energy Reviews, Vol. 13, pp. 933955 (2009). doi: 10.1016/j.rser.2008.05.005
  2. [2] Celik, A. N., Makkawi, A. and Muneer, T., “Critical Evaluation of Wind Speed Frequency Distribution Functions,” J. Renewable and Sustainable Energy, Vol. 013102, pp. 116 (2010). doi: 10.1063/1.329 4127
  3. [3] Ucar, A. and Balo, F., “Investigation of Wind Characteristics and Assessment of Wind-Generation Potentiality in Uludag-Bursa, Turkey,” Applied Energy, Vol. 86, pp. 333339 (2009). doi: 10.1016/j.apenergy. 2008.05.001
  4. [4] Akdag, S. A. and Dinler, A., “A New Method to Estimate Weibull Parameters for Wind Energy Applications,” Energy Conversion and Management, Vol. 50, pp. 17611766 (2009). doi: 10.1016/j.enconman. 2009.03.020
  5. [5] Jowder, F. A. L., “Wind Power Analysis and Site Matching of Wind Turbine Generators in Kingdom of BAHRAIN,” Applied Energy, Vol. 86, pp. 538545 (2009). doi: 10.1016/j.apenergy.2008.08.006
  6. [6] Raichle, B. W. and Carson, W. R., “Wind Resource Assessment of the Southern Appalachian Ridges in the Southeastern United States,” Renewable and Sustainable Energy Reviews, Vol. 13, pp. 11041110 (2009). doi: 10.1016/j.rser.2007.12.005
  7. [7] Kwon, S. D., “Uncertainty Analysis of Wind Energy Potential Assessment,” Applied Energy, Vol. 87, pp. 856865 (2010). doi: 10.1016/j.apenergy.2009.08.038
  8. [8] Zhou, W., Yang, H. X. and Fang, Z. H., “Wind Power Potential and Characteristic Analysis of the Pearl River Delta Region, China,” Renewable Energy, Vol. 31, pp. 739753 (2006). doi: 10.1016/j.renene.2005. 05.006
  9. [9] Seguro, J. V. and Lambert, T. W., “Modern Estimation of the Parameters of the Weibull Wind Speed Distribution for Wind Energy Analysis,” J Wind Eng Indus Aerod, Vol. 85, pp. 7584 (2000). doi: 10.1016/ S0167-6105(99)00122-1
  10. [10] Akpinar, E. K. and Akpinar, S., “Determination of the Wind Energy Potential for Maden-Elazig, Turkey,” Energy Conversion and Management, Vol. 45, pp. 29012914 (2004). doi: 10.1016/j.enconman.2003. 12.016
  11. [11] Celik, A. N., “A Statistical Analysis of Wind Power Density Based on the Weibull and Rayleigh Models at the Southern Region of Turkey,” Renew Energy, Vol. 29, pp. 593604 (2003). doi: 10.1016/j.renene.2003. 07.002
  12. [12] Sulaiman, M. Y., Akaak, A. M., Wahab, M. A., Zakaria, A., Sulaiman, Z. A. and Suradi, J., “Wind Characteristics of Oman,” Energy, Vol. 27, pp. 3546 (2002). doi: 10.1016/S0360-5442(01)00055-X
  13. [13] Yang, H. X., Lu, L. and Burnett, J., “Weather Data and Probability Analysis of Hybrid Photovoltaic-Wind Power Generation Systems in Hong Kong,” Renewable Energy, Vol. 28, pp. 18131824 (2003). doi: 10.1016/S0960-1481(03)00015-6
  14. [14] Chang, T. J., Wu, Y. T., Hsu, H. Y., Chu, C. R. and Liao, C. M., “Assessment of Wind Characteristics and Wind Turbine Characteristics in Taiwan,” Renewable Energy, Vol. 28, pp. 851871 (2003). doi: 10.1016/ S0960-1481(02)00184-2
  15. [15] Dorvlo, A. S. S., “Estimating Wind Speed Distribution,” Energy Conversion and Management, Vol. 43, pp. 23112318 (2002). doi: 10.1016/S0196-8904(01) 00182-0
  16. [16] Akpinar, S. and Akpinar, E. K., “Wind Energy Analysis Based on Maximum Entropy Principle (MEP)- Type Distribution Function,” Energy Conversion and Management, Vol. 48, pp. 11401149 (2007). doi: 10.1016/j.enconman.2006.10.004
  17. [17] Shamilov, A., Kantar, Y. M. and Usta, I., “Use of MinMaxEnt Distributions Defined on Basis of MaxEnt Method in Wind Power Study,” Energy Conversion and Management, Vol. 49, pp. 660677 (2008). doi: 10.1016/j.enconman.2007.07.045
  18. [18] Carta, J. A. and Mentado, D., “A Continuous Bivariate Model for Wind Power Density and Wind Turbine Energy Output Estimations,” Energy Conversion and Management, Vol. 48, pp. 420432 (2007). doi: 10. 1016/j.enconman.2006.06.019
  19. [19] Jeromel, M., Malacic, V. and Rakovec, J., “Weibull Distribution of Bora and Sirocco Winds in the Northern Adriatic Sea,” Geophysical, Vol. 26, pp. 85100 (2009).
  20. [20] Carta, J. A. and Ramirez, P., “Analysis of Two-Component Mixture Weibull Statistics for Estimation of Wind Speed Distributions,” Renewable Energy, Vol. 32, pp. 518531 (2007). doi: 10.1016/j.renene.2006. 05.005
  21. [21] Razali, A. M. and Salih, A. A., “Combing Two Weibull Distributions Using a Mixing Parameter,” European Journal of Scientific Research, Vol. 31, pp. 296305 (2009).
  22. [22] Jiang, S. Y. and Kececioglu, D., “Maximum Likelihood Estimation from Censored-Data Mixed Weibull Distribution,” IEEE Transactions on Reliability, Vol. 41, pp. 248255 (1992). doi: 10.1109/24.257791
  23. [23] Chang, T. P., “Performance Comparison of Six Numerical Methods in Estimating Weibull Parameters for Wind Energy Application,” Applied Energy, Vol. 88, pp. 272282 (2011). doi: 10.1016/j.apenergy.2010. 06.018
  24. [24] Carta, J. A. and Ramirez, P., “Use of Finite Mixture Distribution Models in the Analysis of Wind Energy in the Canarian Archipelago,” Energy Conversion and Management, Vol. 48, pp. 281291 (2007). doi: 10. 1016/j.enconman.2006.04.004
  25. [25] Jaramillo, O. A. and Borja, M. A., “Wind Speed Analysis in La Ventosa, Mexico, a Bimodal Probability Distribution Case,” Renewable Energy, Vol. 29, pp. 16131630 (2004). doi: 10.1016/j.renene.2004.02. 001
  26. [26] Jamil, M., Parsa, S. and Majidi, M., “Wind Power Statistics and an Evaluation of Wind Energy Density,” Renewable Energy, Vol. 6, pp. 623628 (1995). doi: 10.1016/0960-1481(95)00041-H