Wind and Structures

  • 제21권2호
  • /
  • Pages.203-221
  • /
  • 2015
  • /
  • 1226-6116(pISSN)
  • /
  • 1598-6225(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

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)
  • 투고 : 2014.12.13
  • 심사 : 2015.06.18
  • 발행 : 2015.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

참고문헌

  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. https://doi.org/10.1016/j.enconman.2009.03.020
  2. Chang, T.P. (2011a), "Estimation of wind energy potential using different probability density functions", Appl. nerg., 88(5), 1848-1856. https://doi.org/10.1016/j.apenergy.2010.11.010
  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. https://doi.org/10.1016/j.apenergy.2010.06.018
  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. https://doi.org/10.1016/j.apenergy.2011.08.003
  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. https://doi.org/10.1016/S0167-6105(97)00013-5
  7. Dorvlo, A.S. (2002), "Estimating wind speed distribution", Energ. Convers. Manag., 43(17), 2311-2318. https://doi.org/10.1016/S0196-8904(01)00182-0
  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. https://doi.org/10.15680/IJIRSET.2014.0301032
  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. https://doi.org/10.1016/j.apenergy.2008.08.006
  14. 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. https://doi.org/10.1016/j.cageo.2008.04.004
  15. Kwon, S.D. (2010), "Uncertainty analysis of wind energy potential assessment", Appl. Energ., 87(3), 856-865. https://doi.org/10.1016/j.apenergy.2009.08.038
  16. Manwell, J. F., McGowan, J. G. and Rogers, A. L. (2002), Wind energy explained, Wiley.
  17. Mathew, S. and Philip, G.S. (2011), Advances in wind energy and conversion technology, Springer.
  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. https://doi.org/10.1016/j.rser.2013.11.016
  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. https://doi.org/10.1016/S0167-6105(99)00122-1
  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.

피인용 문헌

  1. Mathematical modeling of wind power estimation using multiple parameter Weibull distribution vol.23, pp.4, 2016, https://doi.org/10.12989/was.2016.23.4.351
  2. A Mathematical model to estimate the wind power using three parameter Weibull distribution vol.22, pp.4, 2016, https://doi.org/10.12989/was.2016.22.4.393
  3. An integrator based wind speed estimator for wind turbine control vol.21, pp.4, 2015, https://doi.org/10.12989/was.2015.21.4.443
  4. Alternative Moment Method for wind energy potential and turbine energy output estimation vol.120, 2018, https://doi.org/10.1016/j.renene.2017.12.072
  5. The new odd-burr rayleigh distribution for wind speed characterization vol.28, pp.6, 2019, https://doi.org/10.12989/was.2019.28.6.369
  6. Wind Characteristics in the Taiwan Strait: A Case Study of the First Offshore Wind Farm in Taiwan vol.13, pp.24, 2015, https://doi.org/10.3390/en13246492
  7. An Analysis of Wind Power Generation on the Electric Transmission System of Southern Vietnam vol.16, pp.None, 2015, https://doi.org/10.37394/232016.2021.16.21
  8. Mixture Weibull probabilistic model in Wind Turbine Power Analysis vol.1916, pp.1, 2021, https://doi.org/10.1088/1742-6596/1916/1/012109
  9. Enhancement of wind energy resources assessment using Multi-Objective Genetic algorithm: A case study at Gabal Al-Zayt wind farm in Egypt vol.18, pp.14, 2015, https://doi.org/10.1080/15435075.2021.1904949