Location wise comparison of mixture distributions for assessment of wind power potential: A parametric study

References

• Akdağ, S. A., H. S. Bagiorgas, and G. Mihalakakou. 2010. Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern Mediterranean. Applied Energy 87:2566–73. doi:10.1016/j.apenergy.2010.02.033.
   Web of Science ®Google Scholar
• Akpinar, S., and E. K. Akpinar. 2007. Wind energy analysis based on maximum entropy principle (MEP)-type distribution function. Energy Conversion and Management 48:1140–49. doi:10.1016/j.enconman.2006.10.004.
   Web of Science ®Google Scholar
• Akpinar, S., and E. K. Akpinar. 2009. Estimation of wind energy potential using finite mixture distribution models. Energy Conversion and Management 50:877–84. doi:10.1016/j.enconman.2009.01.007.
   Web of Science ®Google Scholar
• Banerjee, A., I. S. Dhillon, J. Ghosh, and S. Sra. 2005. Clustering on the unit hypersphere using von Mises-Fisher distributions. Journal of Machine Learning Research 6:1345–82.
   Web of Science ®Google Scholar
• Bivona, S., R. Burlon, and C. Leone. 2003. Hourly wind speed analysis in Sicily. Renewable Energy 28:1371–85. doi:10.1016/S0960-1481(02)00230-6.
   Web of Science ®Google Scholar
• Carta, J. A., C. Bueno, and P. Ramírez. 2008. Statistical modelling of directional wind speeds using mixtures of von Mises distributions: Case study. Energy Conversion and Management 49:897–907. doi:10.1016/j.enconman.2007.10.017.
   Web of Science ®Google Scholar
• Carta, J. A., and P. Ramírez. 2007a. Analysis of two-component mixture Weibull statistics for estimation of wind speed distributions. Renewable Energy 32:518–31. doi:10.1016/j.renene.2006.05.005.
   Web of Science ®Google Scholar
• Carta, J. A., and P. Ramírez. 2007b. Use of finite mixture distribution models in the analysis of wind energy in the Canarian archipelago. Energy Conversion and Management 48:281–91. doi:10.1016/j.enconman.2006.04.004.
   Web of Science ®Google Scholar
• Carta, J. A., P. Ramírez, and C. Bueno. 2008. A joint probability density function of wind speed and direction for wind energy analysis. Energy Conversion and Management 49:1309–20. doi:10.1016/j.enconman.2008.01.010.
   Web of Science ®Google Scholar
• Celik, A. N. 2004. On the distributional parameters used in assessment of the suitability of wind speed probability density functions. Energy Conversion and Management 45:1735–47. doi:10.1016/j.enconman.2003.09.027.
   Web of Science ®Google Scholar
• Chang, T. P. 2010. Wind speed and power density analyses based on mixture Weibull and maximum entropy distributions. International Journal of Applied Science and Engineering 1:39–46.
   Google Scholar
• Chang, T. P. 2011a. Estimation of wind energy potential using different probability density functions. Applied Energy 88:1848–56. doi:10.1016/j.apenergy.2010.11.010.
   Web of Science ®Google Scholar
• Chang, T. P. 2011b. Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application. Applied Energy 88:272–82. doi:10.1016/j.apenergy.2010.06.018.
   Web of Science ®Google Scholar
• Chellali, F., A. Khellaf, A. Belouchrani, and R. Khanniche. 2012. A comparison between wind speed distributions derived from the maximum entropy principle and Weibull distribution. Case of study; six regions of Algeria. Renewable and Sustainable Energy Reviews 16:379–85. doi:10.1016/j.rser.2011.08.002.
   Web of Science ®Google Scholar
• Coelingh, J. P., A. J. M. van Wijk, and A. A. M. Holtslag. 1996. Analysis of wind speed observations over the North Sea. Journal of Wind Engineering and Industrial Aerodynamics 61:51–69. doi:10.1016/0167-6105(96)00043-8.
   Web of Science ®Google Scholar
• Cook, N. J. 2001. “Discussion on modern estimation of the parameters of the Weibull wind speed distribution for wind speed energy analysis” by J.V. Seguro, T.W. Lambert. Journal of Wind Engineering and Industrial Aerodynamics 89:867–69. doi:10.1016/S0167-6105(00)00088-X.
   Web of Science ®Google Scholar
• Datta, D., S. Singh, B. E. Johnson, and H. S. Kushwaha. 2008. Maximum likelihood estimates of mean and variance of occupation radiation doses subjected to minimum detection levels. Radiation Protection Dosimetry 129:411–18. doi:10.1093/rpd/ncm483.
   PubMed Web of Science ®Google Scholar
• Davis, M. W. 1977. A new thermal rating approach: The real time thermal rating system for strategic overhead conductor transmission lines–part i: general description and justification of the real time thermal rating system. IEEE Transactions on Power Apparatus and Systems 96:803–09. doi:10.1109/T-PAS.1977.32393.
   Web of Science ®Google Scholar
• Deaves, D. M., and I. G. Lines. 1997. On the fitting of low mean windspeed data to the Weibull distribution. Journal of Wind Engineering and Industrial Aerodynamics 66:169–78. doi:10.1016/S0167-6105(97)00013-5.
   Web of Science ®Google Scholar
• Douglass, D. A. 1988. Weather-dependent versus static thermal line ratings. IEEE Transactions on Power Delivery 3:742–53. doi:10.1109/61.4313.
   Web of Science ®Google Scholar
• Garcia, A., J. L. Torres, E. Prieto, and A. de Francisco. 1998. Fitting wind speed distributions: A case study. Solar Energy 62:139–44. doi:10.1016/S0038-092X(97)00116-3.
   Web of Science ®Google Scholar
• Gupta, B. K. 1986. Weibull parameters for annual and monthly wind speed distributions for five locations in India. Solar Energy 37:469–71. doi:10.1016/0038-092X(86)90039-3.
   Web of Science ®Google Scholar
• Harris, R. I., and N. J. Cook. 2014. the parent wind speed distribution: Why weibull? Journal of Wind Engineering and Industrial Aerodynamics 131:72–87. doi:10.1016/j.jweia.2014.05.005.
   Web of Science ®Google Scholar
• Hussin, A. G., J. F. Jalaludin, and I. Mohamed. 2006. Analysis of Malaysian wind direction data using AXIS. Journal of Applied Sciences Research 2:1019–21.
   Google Scholar
• Jaramillo, O. A., and M. A. Borja. 2004. Wind speed analysis in La Ventosa, Mexico: A bimodal probability distribution case. Renewable Energy 29:1613–30. doi:10.1016/j.renene.2004.02.001.
   Web of Science ®Google Scholar
• Jaynes, E. T. 1957. Information theory and statistical mechanics. Physical Review 106:620–30. doi:10.1103/PhysRev.106.620.
   Web of Science ®Google Scholar
• Jones, T. A., and W. R. James. 1969. Analysis of bimodal orientation data. Journal of the International Association for Mathematical Geology 1:129–35. doi:10.1007/BF02048557.
   Google Scholar
• Kapur, J. N. 1989. Maximum entropy models in science and engineering. New Delhi, India: New Age International Publishers.
   Google Scholar
• Kasperski, M. 2010. Estimation of design wind speed. In 7th international advance school on wind engineering. New Delhi: International Association for Wind Engineering Venue.
   Google Scholar
• Li, M., and X. Li. 2004. On the probabilistic distribution of wind speeds: Theoretical development and comparison with data. International Journal of Exergy 1:237–55. doi:10.1504/IJEX.2004.005096.
   Google Scholar
• Li, M., and X. Li. 2005. MEP-type distribution function: A better alternative to Weibull function for wind speed distributions. Renewable Energy 30:1221–40. doi:10.1016/j.renene.2004.10.003.
   Web of Science ®Google Scholar
• Lun, I. Y. F., and J. C. Lam. 2000. A study of Weibull parameters using long-term wind observations. Renewable Energy 20:145–53. doi:10.1016/S0960-1481(99)00103-2.
   Web of Science ®Google Scholar
• Mardia, K. V., and P. E. Jupp. 2000. Directional analysis. New York, NY: Wiley.
   Google Scholar
• Masseran, N., A. M. Razali, K. Ibrahim, and M. T. Latif. 2013. Fitting a mixture of von Mises distributions in order to model data on wind direction in Peninsular Malaysia. Energy Conversion and Management 72:94–102. doi:10.1016/j.enconman.2012.11.025.
   Web of Science ®Google Scholar
• Qin, X., S. J. Zhang, and D. X. Yan. 2010. A new circular distribution and its application to wind data. Journal of Mathematics Research 2:12–17. doi:10.5539/jmr.v2n3p12.
   Google Scholar
• Ramírez, P., and J. A. Carta. 2006. The use of wind probability distributions derived from the maximum entropy principle in the analysis of wind energy. a case study. Energy Conversion and Management 47:2564–77. doi:10.1016/j.enconman.2005.10.027.
   Web of Science ®Google Scholar
• Razali, A. M., A. Ahmad, A. Zaharim, and K. Sopian. (2008). Statistical analysis of wind directional data. In The 3rd International Conference on Mathematics and Statistics, Indonesia.
   Google Scholar
• Rehman, S., T. O. Halawani, and T. Husain. 1994. Weibull parameters for wind speed distribution in Saudi Arabia. Solar Energy 53:473–79. doi:10.1016/0038-092X(94)90126-M.
   Web of Science ®Google Scholar
• Rocha, P. A. C., R. C. de Sousa, C. F. de Andrade, and M. E. V. da Silva. 2012. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil. Applied Energy 89:395–400. doi:10.1016/j.apenergy.2011.08.003.
   Web of Science ®Google Scholar
• Seguro, J. V., and T. W. Lambert. 2000. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics 85:75–84. doi:10.1016/S0167-6105(99)00122-1.
   Web of Science ®Google Scholar
• Shannon, C. E. 1948. A mathematical theory of communication. Bell System Technical Journal 27:379–423. doi:10.1002/bltj.1948.27.issue-3.
   Web of Science ®Google Scholar
• Sulaiman, M. Y., A. M. Akaak, M. A. Wahab, A. Zakaria, Z. A. Sulaiman, and J. Suradi. 2002. Wind characteristics of Oman. Energy 27:35–46. doi:10.1016/S0360-5442(01)00055-X.
   Web of Science ®Google Scholar
• Titterington, D. M., A. F. M. Smith, and U. E. Makov. 1985. Statistical analysis of finite mixture distributions. New York, NY: Wiley Publication.
   Google Scholar
• Venkataraman, P. 2002. Applied optimization with matlab programming. New York: Wiley.
   Google Scholar
• Zaharim, A., A. M. Razali, R. Z. Abidin, and K. Sopian. 2009. Fitting of statistical distributions to wind speed data in Malaysia. Europian Journal of Scientific Research 26:6–12.
   Google Scholar
• Zhang, H., Y. J. Yu, and Z. Y. Liu. 2014. Study on the maximum entropy principle applied to the annual wind speed probability distribution: A case study for observations of intertidal zone anemometer towers of Rudong in East China Sea. Applied Energy 114:931–38. doi:10.1016/j.apenergy.2013.07.040.
   Web of Science ®Google Scholar
• Zhou, J., E. Erdem, G. Li, and J. Shi. 2010. Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites. Energy Conversion and Management 51:1449–58. doi:10.1016/j.enconman.2010.01.020.
   Web of Science ®Google Scholar