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http://dx.doi.org/10.12989/was.2015.21.2.203

Estimation of weibull parameters for wind energy application in Iran's cities  

Sedghi, Majid (Department of Mechanical Engineering, Isfahan University of Technology)
Hannani, Siamak K. (School of Mechanical Engineering, Sharif University of Technology)
Boroushaki, Mehrdad (Department of Energy Engineering, Sharif University of Technology)
Publication Information
Wind and Structures / v.21, no.2, 2015 , pp. 203-221 More about this Journal
Abstract
Wind speed is the most important parameter in the design and study of wind energy conversion systems. The weibull distribution is commonly used for wind energy analysis as it can represent the wind variations with an acceptable level of accuracy. In this study, the wind data for 11 cities in Iran have been analysed over a period of one year. The Goodness of fit test is used for testing data fit to weibull distribution. The results show that this data fit to weibull function very well. The scale and shape factors are two parameters of the weibull distribution that depend on the area under study. The kinds of numerical methods commonly used for estimating weibull parameters are reviewed. Their performance for the cities under study was compared according to root mean square and wind energy errors. The result of the study reveals the empirical, modified maximum likelihood estimate of wind speed with minimum error. Also, that the moment and modified maximum likelihood are the best methods for estimating the energy production of wind turbines.
Keywords
weibull distribution; numerical methods; Iran's cities; wind energy;
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  • Reference
1 Akdag, S.A. and Dinler, A. (2009), "A new method to estimate Weibull parameters for wind energy applications", Energy. Convers. Manag., 50(7), 1761-1766.   DOI   ScienceOn
2 Chang, T.P. (2011a), "Estimation of wind energy potential using different probability density functions", Appl. nerg., 88(5), 1848-1856.   DOI
3 Chang, T.P. (2011b), "Performance comparison of six numerical methods in estimating Weibull parameters for wind energy application", Appl. Energ., 88(1), 272-282.   DOI
4 Costa Rocha, P.A., de Sousa, R.C., de Andrade, C.F. and da Silva, M.E.V. (2012), "Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil", Appl. Energ., 89(1), 395-400.   DOI
5 D'Agostino, R.B. (1986), Goodness-of-fit-techniques, Dekker.
6 Deaves, D. and Lines, I. (1997), "On the fitting of low mean windspeed data to the Weibull distribution", J. Wind Eng. Ind. Aerod., 66(3), 169-178.   DOI
7 Dorvlo, A.S. (2002), "Estimating wind speed distribution", Energ. Convers. Manag., 43(17), 2311-2318.   DOI
8 Focken, U. and Lange, M. (2006), Physical approach to short-term wind power prediction, Springer.
9 Indhumathy, D., Seshaiah, C.V. and Sukkiramathi, K. (2014), "Estimation of Weibull parameters for wind speed calculation at Kanyakumari in India", Int. J. Innov. Res. Sci., Eng. Technol., 3(1), 8340-8345.   DOI
10 IREO, Wind atlas of Iran, from http://www.suna.org.ir/.
11 Jamdade, P.G. and Jamdade, S.G. (2012), "Analysis of wind speed data for four locations in ireland based on weibull distribution's linear regression model", Int. J. Renew. Energ. Res., 2(3), 451-455.
12 Johnson, G.L. (2006), Wind energy systems, Manhattan University.
13 Jowder, F.A. (2009), "Wind power analysis and site matching of wind turbine generators in Kingdom of Bahrain", Appl. Energ., 86(4), 538-545.   DOI
14 Mathew, S. and Philip, G.S. (2011), Advances in wind energy and conversion technology, Springer.
15 Kantar, Y.M. and Senoglu, B. (2008), "A comparative study for the location and scale parameters of the Weibull distribution with given shape parameter", Comput. Geosci., 34(12), 1900-1909.   DOI
16 Kwon, S.D. (2010), "Uncertainty analysis of wind energy potential assessment", Appl. Energ., 87(3), 856-865.   DOI   ScienceOn
17 Manwell, J. F., McGowan, J. G. and Rogers, A. L. (2002), Wind energy explained, Wiley.
18 Mostafaeipour, A., Jadidi, M., Mohammadi, K. and Sedaghat, A. (2014), "An analysis of wind energy potential and economic evaluation in Zahedan", Iran. Renewable and Sustainable Energy Reviews, 30, 641-650.   DOI
19 Sathyajith, M. (2006), Wind energy: fundamentals, resource analysis and economics, Springer.
20 Seguro, J. and Lambert, T. (2000), "Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis", J. Wind Eng. Ind. Aerod., 85(1), 75-84.   DOI
21 Vestas V47-660 kW wind turbine catalog, Denmark, http://www.vestas.com.
22 Yildirim, U., Kaya, F. and Gungor, A. (2012), "Comparison of moment and energy trend factor methods on calculating wind energy potentia", Proceeding of the 16th International Research/Expert Conference, Dubai.