A New theoretical model for modeling the wind speed frequency distribution

Fathi Ben Amar, Mustapha Elamouri


The probabilistic speed-frequency distribution of the wind is essential for evaluation of the wind potential. Although wind commonly evaluated with weibul distribution, by assigning null value to calm winds it cannot envisage the existence of calm winds. Since for the sites with significant calm wind frequency Weibull distribution is uncertain, in this paper we present a new theoretical approach which employs Maximum Entropy Principle (MEP). The model is improvement of previous proposed MEP which applied to the synoptic sites distributed inside the Tunisian territory with significant calm winds frequency. The obtained results appropriately describe the distribution of measured wind speed data particularly calm speed over the MEP and Weibull models.

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T. P. Chang, Estimation of wind energy potential using different probability density functions. Applied Energy 88 (2011) 1848–1856.

R.J. O'Doherty, B. W. Roberts, The Application of U.S. Upper Wind Data in One Design of Tethered Wind Energy Systems. Solar Energy Research Institute Report SERI/TR-211-1400, February 1982

V. Lo Brano, A. Orioli, G. Ciulla, S. Culotta, Quality of wind speed fitting distributions for the urban area of Palermo, Italy. Renewable Energy 36 (2011) 1026-1039.

B. Safari, Modeling wind speed and wind power distributions in Rwanda. Renewable and Sustainable Energy Reviews 15 (2011) 925–935.

J.A. Carta, P. Ramirez, S. Velaiquez, A review of wind speed probability distributions used in wind energy analysis Case studies in the Canary Islands. Renewable and Sustainable Energy Reviews 13 (2009) 933–955.

Y.Q. Xiao, Q.S. Lia, Z.N. Li, Y.W. Chow, G.Q. Li, Probability distributions of extreme wind speed and its occurrence interval. Engineering Structures 28 (2006) 1173–1181.

J. Zhou, E. Erdem, G. Li, J. Shi. Comprehensive evaluation of wind speed distribution models: A case study for North Dakota sites. Energy Conversion and Management 51 (2010) 1449–1458.

M. Elamouria, F. Ben Amar, Wind energy potential in Tunisia, Renewable Energy 33 (2008) 758–768.

S. Akpinar, E. K. Akpinar, Estimation of wind energy potential using finite mixture distribution models. Energy Conversion and Management 50 (2009) 877–884.

T. P. Chang, Wind Speed and Power Density Analyses Based on Mixture Weibull and Maximum Entropy Distributions. International Journal of Applied Science and Engineering 2010. 8, 1: 39-46.

A. Shamilov, Y. M. Kantar, I. Usta, Use of MinMaxEnt distributions defined on basis of MaxEnt method in wind power study. Energy Conversion and Management 49 (2008) 660–677.

Y. M. Kantar, I. Usta, Analysis of wind speed distributions: Wind distribution function derived from minimum cross entropy principles as better alternative to Weibull function. Energy Conversion and Management 49 (2008) 962–973.

F. Ben Amar, M.Elamouri, R. Dhifaoui, Energy assessment of the first wind farm section of Sidi Daoud, Tunisia, Renewable Energy 33 (2008) 2311-2321.

S. Akpinar, E. K. Akpinar, Wind energy analysis based on maximum entropy principle (MEP)-type distribution function. Energy Conversion and Management 48 (2007) 1140–1149.

P. Ramirez, J. A. Carta, 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 (2006) 2564–2577.

M. Li, X. Li, Investigation of wind characteristics and assessment of wind energy potential for Waterloo region, Canada, Energy Conversion and Management 46 (2005) 3014–3033.

M. Li, X. Li, MEP-type distribution function: a better alternative to Weibull function for wind speed distributions. Renewable Energy 30 (2005) 1221–1240.

M. Li, X. Li, On the probabilistic distribution of wind speeds: theoretical development and comparison with data. Int. J. Exergy, Vol. 1, No. 2, 2004.


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