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Statistical Analysis Of Wind Speed Data

Year 2005, Volume: 18 Issue: 2, 41 - 54, 31.12.2005

Veysel Yılmaz Haydar Aras H.Eray Çelik

Abstract

Wind speed is the most important parameter in the design and study of wind energy conversion devices. The energy which is obtained from wind is directly proportional with the cubic power of the wind speed. As the wind speed increases, the cost of the wind energy is reduced. In many studies in literature, it is assumed that the probability distribution related to wind speeds can be described by Weibull distribution, and it is accepted so without any statistical examination. In this study, the theoretical distributions of wind potentials fit to Weibull distribution for five different topographic situations from Turkey Wind Atlas are investigated and reported.

Keywords

Wind Energy Weibull Distribution Statistical Tests

References

  • [1] Aras H, Condition and development of the cogeneration facilities based on autoproduction investment model in Turkey. Renewable and Sustainable Energy Reviews 2003; 7(6): 553-559.
  • [2] Aras H, Wind energy status and its assessment in Turkey. Renewable Energy 2003; 28(14): 2213-2220.
  • [3] Bivona S, Burlon R, Leone C. Hourly wind speed analysis in Sicily. Renewable Energy 2003 ;28: 1371-1385.
  • [4] Chen Zhenmin. Statistical inference about the shape parameter of the Weibull Distribution. Statistics and Probabilty Letters. 1997; 36: 85-90.
  • [5] Dündar C, Canbaz M, Akgün N, Ural G. Turkey wind atlas. Published by the General Directorate of Turkish State Meteorological Service and the General Directorate of Electrical Power Resources Survey Administration, Ankara, Turkey, 2002. [in Turkish]
  • [6] Dorvlo S.S. A. Estimating wind speed distribution. Energy Conversion & Management 2002; 43: 2311-2318.
  • [7] Gupta B.K. Weibull parameters for annual and monthly wind speed distributions for five locations in India. Solar Energy 1986; 37(6): 469-471.
  • [8] AL-Hasan M, Nigmatullin R.R. Idenfication of the generalized Weibull distribution in wind speed data by the eigencoordinates method. Renewable Energy 2003; 28: 93- 110.
  • [9] Rehman S, HalawaniT.O, Husain T. Weibull parameters for wind speed distribution in Saudi Arabia. Solar Energy 1994;53(6): 473-479.
  • [10] Seguro J.V, Lambert T.W. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics 2000; 85: 75-84.
  • [11] Weisser D. A Wind energy analysis of Grenada: an estimation using the “Weibull” density function. Renewable Energy 2003; 28: 1803-1812.
  • [12] Xie M, Tang Y, Goh T.N. A modified Weibull extension with bathtub-shaped failure rate function. Reliability Engineering and System Safety 2002;76: 279-285.
  • [13] Çelik A.N. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy 2004; 29 (4):593- 604.
  • [14] Hepbasli A, Ozgener O. A review on the development of wind energy in Turkey. Renewable & Sustainable Energy Reviews 2004; 8(3): 257-276.

Statistical Analysis Of Wind Speed Data

Year 2005, Volume: 18 Issue: 2, 41 - 54, 31.12.2005

Veysel Yılmaz Haydar Aras H.Eray Çelik

Abstract

Wind speed is the most important parameter in the design and study of wind energy conversion devices. The energy which is obtained from wind is directly proportional with the cubic power of the wind speed. As the wind speed increases, the cost of the wind energy is reduced. In many studies in literature, it is assumed that the probability distribution related to wind speeds can be described by Weibull distribution, and it is accepted so without any statistical examination. In this study, the theoretical distributions of wind potentials fit to Weibull distribution for five different topographic situations from Turkey Wind Atlas are investigated and reported.

Keywords

Wind Energy Weibull Distribution Statistical Tests

References

  • [1] Aras H, Condition and development of the cogeneration facilities based on autoproduction investment model in Turkey. Renewable and Sustainable Energy Reviews 2003; 7(6): 553-559.
  • [2] Aras H, Wind energy status and its assessment in Turkey. Renewable Energy 2003; 28(14): 2213-2220.
  • [3] Bivona S, Burlon R, Leone C. Hourly wind speed analysis in Sicily. Renewable Energy 2003 ;28: 1371-1385.
  • [4] Chen Zhenmin. Statistical inference about the shape parameter of the Weibull Distribution. Statistics and Probabilty Letters. 1997; 36: 85-90.
  • [5] Dündar C, Canbaz M, Akgün N, Ural G. Turkey wind atlas. Published by the General Directorate of Turkish State Meteorological Service and the General Directorate of Electrical Power Resources Survey Administration, Ankara, Turkey, 2002. [in Turkish]
  • [6] Dorvlo S.S. A. Estimating wind speed distribution. Energy Conversion & Management 2002; 43: 2311-2318.
  • [7] Gupta B.K. Weibull parameters for annual and monthly wind speed distributions for five locations in India. Solar Energy 1986; 37(6): 469-471.
  • [8] AL-Hasan M, Nigmatullin R.R. Idenfication of the generalized Weibull distribution in wind speed data by the eigencoordinates method. Renewable Energy 2003; 28: 93- 110.
  • [9] Rehman S, HalawaniT.O, Husain T. Weibull parameters for wind speed distribution in Saudi Arabia. Solar Energy 1994;53(6): 473-479.
  • [10] Seguro J.V, Lambert T.W. Modern estimation of the parameters of the Weibull wind speed distribution for wind energy analysis. Journal of Wind Engineering and Industrial Aerodynamics 2000; 85: 75-84.
  • [11] Weisser D. A Wind energy analysis of Grenada: an estimation using the “Weibull” density function. Renewable Energy 2003; 28: 1803-1812.
  • [12] Xie M, Tang Y, Goh T.N. A modified Weibull extension with bathtub-shaped failure rate function. Reliability Engineering and System Safety 2002;76: 279-285.
  • [13] Çelik A.N. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy 2004; 29 (4):593- 604.
  • [14] Hepbasli A, Ozgener O. A review on the development of wind energy in Turkey. Renewable & Sustainable Energy Reviews 2004; 8(3): 257-276.
There are 14 citations in total.

Details

Journal Section Research Articles
Authors

Veysel Yılmaz

Haydar Aras

H.Eray Çelik

Publication Date December 31, 2005
Acceptance Date July 1, 2005
Published in Issue Year 2005 Volume: 18 Issue: 2

Cite

APA Yılmaz, V., Aras, H., & Çelik, H. (2005). Statistical Analysis Of Wind Speed Data. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 18(2), 41-54.
AMA Yılmaz V, Aras H, Çelik H. Statistical Analysis Of Wind Speed Data. ESOGÜ Müh Mim Fak Derg. December 2005;18(2):41-54.
Chicago Yılmaz, Veysel, Haydar Aras, and H.Eray Çelik. “Statistical Analysis Of Wind Speed Data”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 18, no. 2 (December 2005): 41-54.
EndNote Yılmaz V, Aras H, Çelik H (December 1, 2005) Statistical Analysis Of Wind Speed Data. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 18 2 41–54.
IEEE V. Yılmaz, H. Aras, and H. Çelik, “Statistical Analysis Of Wind Speed Data”, ESOGÜ Müh Mim Fak Derg, vol. 18, no. 2, pp. 41–54, 2005.
ISNAD Yılmaz, Veysel et al. “Statistical Analysis Of Wind Speed Data”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 18/2 (December 2005), 41-54.
JAMA Yılmaz V, Aras H, Çelik H. Statistical Analysis Of Wind Speed Data. ESOGÜ Müh Mim Fak Derg. 2005;18:41–54.
MLA Yılmaz, Veysel et al. “Statistical Analysis Of Wind Speed Data”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 18, no. 2, 2005, pp. 41-54.
Vancouver Yılmaz V, Aras H, Çelik H. Statistical Analysis Of Wind Speed Data. ESOGÜ Müh Mim Fak Derg. 2005;18(2):41-54.
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