Comparative Analysis of Wind Speed Models Using Different Weibull Distributions

Yıl 2019, Cilt: 19 Sayı: 1, 22 - 28, 01.01.2019

Emrah Dokur , Salim Ceyhan , Mehmet Kurban

Öz

DOI: 10.26650/electrica.2018.28091

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. 

Cite this article as: Dokur E, Ceyhan S,
Kurban M. Comparative Analysis of Wind Speed Models Using Different Weibull
Distributions. Electrica, 2019; 19(1): 22-28.

Anahtar Kelimeler

Wind speed modeling, weibull distribution, rayleigh distribution, inverse weibull distribution

Kaynakça

• [1] H. Saleh, A.A.E.A. Aly and 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, 2012,pp.710-719.
• [2] M. Gökçek, H.H. Erdem and A. Bayülken, “A techno-economical evaluation for installation of suitable wind energy plants in Western Marmara, Turkey”, Energy Exploration & Exploitation, vol.25, 2007, pp. 407-427.
• [3] M. Bassyouni, S.A. Gutub, U. Javaid, M. Awais, S. Rehman, S.S. Hamid, M.H. Abdel-Aziz, A. Abouel-Kasem and H. Shafeek Assessment and analysis of wind power resource using weibull parameters. Energy Exploration & Exploitation. vol. 33, 2015, pp.105-122.
• [4] J. Yingni, Y. Xiuling, C. Xiaojun and P. Xiaoyun “Wind potential assessment using the Weibull model at the Inner Mongolia of China” Energy Exploration and Exploitation, 2006, vol.24, pp.211-221.
• [5] 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 , 2013, pp. 1605-1614.
• [6] A. Garcia, J.L. Torres, E. Prieto and A. De Francisco “Fitting wind speed distributions: a case study”, Solar Energy vol.62, 1998, pp. 139-144.
• [7] R.E. Luna and H.W. Church “Estimation of long-term concentrations using a “universal” wind speed distribution” Journal of Applied Meteorology, vol.13, 1974, pp.910-916.
• [8] C.G. Justus, W.R. Hargraves and A. Yalcin “Nationwide assessment of potential output from wind-powered generators”, Journal of Applied Meteorology, vol.15, 1976, pp. 673-678.
• [9] P. Kiss and I.M. Jánosi “Comprehensive empirical analysis of ERA-40 surface wind speed distribution over Europe”, Energy Conversion and Management, vol.49, 2008, pp.2142-2151.
• [10] W.E. Bardsley, “Note on the use of the inverse Gaussian distribution for wind energy applications”, Journal of Applied Meteorology”, vol.19,1980, pp.1126-1130.
• [11] R.M. Vogel, T.A. McMahon and F.H. Chiew, “Floodflow frequency model selection in Australia”, Journal of Hydrology, vol.146, 1993, pp.421-449.
• [12] N.B. Guttman, J.R.M. Hosking and J.R. Wallis, “Regional precipitation quantile values for the continental United States computed from L-moments”, Journal of Climate, vol. 6, 1993, pp.2326-2340.
• [13] J.R. Stedinger, “Fitting log normal distributions to hydrologic data” Water Resources Research, vol.16, 1980, pp.481-490.
• [14] F.C. Kaminsky “Four probability densities/log-normal, gamma, Weibull, and Rayleigh/and their application to modelling average hourly wind speed”, In International Solar Energy Society Annual Meeting, vol.19, 1977,pp.6-10.
• [15] R.H. Sherlock, “Analyzing winds for frequency and duration” In on Atmospheric Pollution . American Meteorological Society, 1951, pp. 42-49.
• [16] E.C. Morgan, M. Lackner, R.M. Vogel and L.G. Baise, “Probability distributions for offshore wind speeds”, Energy Conversion and Management, vol. 52, 2011, pp.15-26.
• [17] E. S. Takle and J. M. Brown, “Note on the use of Weibull statistics to characterize wind-speed data”, Journal of Applied Meteorology ,vol.17, 1978, pp.556-559.
• [18] O.A. Jaramillo and M.A. Borja, “Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case”, Renewable Energy, vol.29, 2004, pp.1613-1630.
• [19] E. Dokur, S. Ceyhan, M. Kurban, “Finsler Geometry for Two-Parameter Weibull Distribution Function”, Mathematical Problems in Engineering, vol. 2017, 2017, pp. 1-6.
• [20] J.A. Carta, P. Ramirez and S. Velazquez, “A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands”, Renewable and Sustainable Energy Reviews, 2009, pp.13, pp.933-955.
• [21] L. Van der Auwera, F. De Meyer and L.M. Malet “The use of the Weibull three-parameter model for estimating mean wind power densities”, Journal of Applied Meteorology, vol.19, 1980, pp. 819-825.
• [22] F.G. Akgül, B. Şenoğlu and T. Arslan, “An alternative distribution to Weibull for modeling the wind speed data: Inverse Weibull distribution”, Energy Conversion and Management, vol.114, 2016, pp.234-240.
• [23] M. Carrasco-Díaz, D. Rivas, M. Orozco-Contreras and O. Sánchez-Montante, “An assessment of wind power potential along the coast of Tamaulipas, northeastern Mexico”, Renewable Energy vol.78, 2015, pp.295-305.
• [24] J.F. Manwell, J.G. McGowan and A.L. Rogers “Wind energy explained: theory, design and application” John Wiley & Sons, 2010.
• [25] S. Mathew “Wind energy: fundamentals, resource analysis and economics”, Heidelberg: Springer, vol. 1 2006.
• [26] C.G. Justus, W.R. Hargraves, A. Mikhail and D. Graber, “Methods for estimating wind speed frequency distributions”, Journal of Applied Meteorology, vol.17, 1978, pp.350-353.
• [27] S.A. Akdag and A. Dinler, “A new method to estimate Weibull parameters for wind energy applications”, Energy Conversion and Management, vol.50, 2009, pp.1761-1766.
• [28] M. Kurban, E. Dokur, and S. Ceyhan. "A novel information geometry method for estimating parameters of the Weibull wind speed distribution." Proceedings of the Estonian Academy of Sciences vol.67, 2018, pp. 39-49.
• [29] E.H. Lysen “Introduction to wind energy.”, Consultancy services wind energy developing countries (CWD), 1982.
• [30] M.J.M. Stevens and P.T. Smulders, “The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes”, Wind engineering vol.3,1979,pp.132-145.