A Study of Weibull Distribution to Analyze the Wind Speed at Jogimatti in India
International Journal of Emerging Trends in Science and Technology,
Vol. 1 No. 02 (2014),
12 April 2014
Abstract
Wind is an available resource in nature that could be utilized by mechanically converting wind power into electricity using wind turbines. Wind energy is an indirect form of solar energy. Various probability distribution models were used for the statistical analysis of recorded wind speeds. This paper investigates the probability distributions of wind speed based on wind speed data recorded at JOGIMATTI station in INDIA. The  Weibull distribution and Weibull-Weibull probability distribution function, Mixture Gamma and Weibull distribution, Mixture Normal and Weibull distribution and Maximum Endrophy distribution are adopted in this study to fit the wind speed data. It is found from the hypothesis test that Weibull distribution is more appropriate than the other distribution. This best fit probability distribution can be used to calculate the power density.A case study is given to discuss the probability analysis results.
Keywords: Wind speed , Moment method , Weibull Distribution , statistical analysis
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References
[2] Davenport AG. The application of statistical concepts to the wind loading of structure. Proceedings of Institution of Civil Engineering 1962;19: 449–71.
[3] Deaves DM, Lines IG. On the fitting of low mean wind speed data to the Weibull distribution. Journal of Wind Engineering and Industrial Aerodynamics 1997;66(3):169–78.
[4] Sayigh A. Renewable energy—the way forward. App. Energy 1999;64:15–30.
[5] Islam, MR, Saidur, R, Rahim, NA: Assessment of wind energy potentiality at kudat and Labuan, Malaysia using weibull distribution function. Energy 36(2), 985–992 (2011)
[6] Celik, AN: Energy output estimation for small-scale wind power generators using Weibull-representative wind data. J Wind Eng Ind Aerodyn 91(5), 693–707(2003)
[7] Celik, AN: A statistical analysis of wind power density based on the weibull and Rayleigh models at the southern region of turkey.Renew Energy 29, 593–604 (2003)
[8] AkdaÄŸ, SA, Bagiorgas, HS, Mihalakou, G: Use of two-component Weibull mixtures in the analysis of wind speed in the Eastern
Mediterranean. Applied Energy 87(8), 2566–2573 (2010)
[9] Carta, JA, Ramı’rez, P: Analysis of two-component mixture weibull statistics for estimation of wind speed distributions. Renew Energy 32(3), 518–531 (2007)
[10] Kiss, P, Jánosi, IM: Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe. Energy Conversion and Management 49,2142–2151 (2008)
[11]. Jaramillo, OA, Borja, MA: Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case. Renew Energy 29(10), 613–630 (2004)
[12]. Akpinar, S, Akpinar, EK: Estimation of wind energy potential using f inite mixture distribution models. Energy Conversion Management 50(4), 877–884 (2009)
[13]. Tian Pau, C: Estimation of wind energy potential using different Probability density functions. Applied Energy 88(5), 1848–1856 (2011)
[14]. Kececioglu, D: Reliability engineering handbook, vol.1 and 2. DestechPubblications, Pennsylva Disnia (2002)
[15] Stacy E.W(1962) “A Generalisation of the Gamma Distribution â€The Annals of mathematical statistics,33,1187-1192
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