Comparative Analysis of Wind Speed Models Using Different Weibull Distributions

Key Findings & Data:The hourly wind speed data for the period of October 2015 to 30 September 2016 is taken from Turkish State Meteorological Service.

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Conclusion & Impact:All the comparative results are given in tables.

🔬Design:Modeling & Simulation

📍Scope:2015 Verileri

⚙️Variables:Domain Parameters

Özet

A wide variety of distribution functions are used in the literature for wind speed modelling. It is the most widely used Weibull distribution (WD) function in wind speed modelling. In this paper, two-parameter WD, Rayleigh distribution (RD) which is a special form of WD, and Inverse Weibull distribution (IWD) offered for a new seasonal wind speed modelling are considered and analyzed for six different regions (Gökçeada, Bozcaada, Bandırma, Bilecik, Yalova and Sakarya regions) in the Northwest of Turkey, comparatively. The hourly wind speed data for the period of October 2015 to 30 September 2016 is taken from Turkish State Meteorological Service. As a result of the comparison, it is seen that the WD is generally suitable, although IWD has good seasonal results in some regions. All the comparative results are given in tables.

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Cite This Article

DOKUR, E., CEYHAN, S., KURBAN, M. (2019). Comparative Analysis of Wind Speed Models Using Different Weibull Distributions. Electrica, Cilt: 19 - Sayı: 1, 22–28. https://ajindex.com/articles/149927

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References (1)

H. Saleh, A.A.E.A. Aly, S. Abdel-Hady, “Assessment of different methods used to estimate Weibull distribution parameters for wind speed in Zafarana wind farm, Suez Gulf, Egypt”, Energy, vol. 44, pp. 710-719, 2012. [CrossRef] M. Gökçek, H.H. Erdem, A. Bayülken, “A techno-economical evaluation for installation of suitable wind energy plants in Western Marmara, Turkey”, Energy Exploration & Exploitation, vol. 25, pp. 407-427, 2007. [CrossRef] M. Bassyouni, S.A. Gutub, U. Javaid, M. Awais, S. Rehman, S.S. Hamid, M.H. Abdel-Aziz, A. Abouel-Kasem, H. ‘’Shafeek Assessment and analysis of wind power resource using weibull parameters’’, Energy Exploration & Exploitation, vol. 33, pp.105-122, 2015. [CrossRef] J. Yingni, Y. Xiuling, C. Xiaojun, P. Xiaoyun, “Wind potential assessment using the Weibull model at the Inner Mongolia of China” Energy Exploration and Exploitation, vol. 24, pp. 211-221, 2006. [CrossRef] H. Yue, G. Li and M. Zhou, “A probabilistic approach to small signal stability analysis of power systems with correlated wind sources”, Journal of Electrical Engineering and Technology, vol. 8, pp. 1605-1614, 2013. [CrossRef] A. Garcia, J.L. Torres, E. Prieto, A. De Francisco, “Fitting wind speed distributions: a case study”, Solar Energy, vol. 62, pp. 139-144, 1998. [CrossRef] R.E. Luna, H.W. Church, “Estimation of long-term concentrations using a “universal” wind speed distribution” Journal of Applied Meteorology, vol. 13, pp. 910-916, 1974. [CrossRef] C.G. Justus, W.R. Hargraves, A. Yalcin, “Nationwide assessment of potential output from wind-powered generators”, Journal of Applied Meteorology, vol. 15, pp. 673-678, 1976. [CrossRef] P. Kiss, I.M. Jánosi, “Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe”, Energy Conversion and Management, vol. 49, pp. 2142-2151, 2008. [CrossRef] W.E. Bardsley, “Note on the use of the inverse Gaussian distribution for wind energy applications”, Journal of Applied Meteorology”, vol. 19, pp.1126-1130, 1980. [CrossRef] R.M. Vogel, T.A. McMahon, F.H. Chiew, “Floodflow frequency model selection in Australia”, Journal of Hydrology, vol. 146, pp. 421-449, 1993. [CrossRef] N.B. Guttman, J.R.M. Hosking, J.R. Wallis, “Regional precipitation quantile values for the continental United States computed from L-moments”, Journal of Climate, vol. 6, pp. 2326-2340, 1993. [CrossRef] J.R. Stedinger, “Fitting log normal distributions to hydrologic data” Water Resources Research, vol. 16, pp. 481-490, 1980.