Statistical wind speed studies and wind energy potential resource analysis of abong mbang, Cameroon: a case study

  • Authors

    • Yemele David University of Dschang, Cameroon
    • Bawe Gerard Nfor, Jr
    • Talla Pierre Kisito
    • Ghogomu Patrick Ndinakie
    2015-11-16
    https://doi.org/10.14419/ijbas.v4i4.5198
  • Wind Energy Resource, Power Density, Gamma, Weibull, Cameroon.

  • Accurate analysis of wind characteristics for a particular site is the first step towards wind energy resource installation. In this study, the onus is to determine the wind energy potential characteristics, and the best representative probability density function, for the Abong Mbang weather station and its immediate environ. The Chi square, coefficient of determination and root mean square error were used as the discriminating goodness of fit tests. Results show that the gamma distribution is the best representative of the wind speed regime, closely followed by the Weibull distribution. We equally study the feasibility of the installation of wind turbine systems at this site based on the Weibull and the Rayleigh models. It is observed that Abong Mbang is characterized by very low wind speeds, higher shape parameters than the scale parameters and consequently very low power density values. Abong Mbang is not technically feasible for the installation of small wind turbine.

  • References

    1. [1] W. Zhou, C. Lou, Z. Li, L. Lu and H. Yang, Current status of research on optimum sizing of stand-alone hybrid solar-wind power generation systems, Applied Energy, 87(2) (2010) 380–9 http://dx.doi.org/10.1016/j.apenergy.2009.08.012.

      [2] H. ARAS, V. YILMAZ and H.E. ÇELİK, Estimation of Monthly Wind Speeds of Eskişehir, Turkey. The First International Exergy, Energy and Environment Symposium, Hotel Princess, Izmir, Turkey, (July 2003) 13-17.

      [3] V. YILMAZ and H. E. ÇELİK, Doğuş Üniversitesi Dergisi, A Statistical Approach to Estimate the Wind Speed Distribution: The Case of Gelibolu Region, 9 (1), (2008) 122-132.

      [4] R. Tchinda and E. Kaptouom, Wind energy in Adamaoua and North Cameroon provinces, Int J Energy Conver Manag (44) (2003) 845–57

      [5] R. Tchinda, J. Kendjio, E. Kaptouom and D. Njomo, Estimation of mean wind energy available in far north Cameroon, Energy Conversion and Management, Pergamon, (41) (2000) 1917-1929.

      [6] Z. O. Olaofe and K. A. Folly, Statistical Analysis of the Wind Resources at Darling for Energy Production, International Journal of Renewable Energy Research, (2012) Vol.2, No.2.

      [7] K. Abbas, Alamgir, Sajjad Ahmad Khan, Amjad Ali, Dost Muhammad Khan and Umair Khalil, Statistical Analysis of Wind Speed Data in Pakistan, World Applied Sciences Journal, 18 (11) (2012) 1533-1539, ISSN 1818-4952.

      [8] S. Mathew, Wind Energy: Fundamentals, Resource Analysis and Economics, Springer-Verlag, Berlin Heidelberg 2006. http://dx.doi.org/10.1007/3-540-30906-3.

      [9] J. A. Carta and P. Ramirez, Analysis of two-component mixture Weibull statistics for estimation of wind speed distribution. Renew. Energ. (32) (2007) 518-531.

      [10] V. L. Brano, A. Orioli, G. Ciulla and S. Culotta, Quality of wind speed fitting for urban area of Palermo, Italy, Renew. Energ. (36) (2011) 1026-1039.

      [11] O. A. Jaramillo and M. A. Borja, Wind speed analysis in La Ventosa, Mexico: a bimodal probability distribution case, Renew. Energ. (29) (2004) 1613-1630.

      [12] B. Safari, “Modeling wind speed and wind power distribution in Rwanda. Renew. Sust. Energy Rev. (15) (2011) 925-935.

      [13] E. K. Akpinar and S. Akpinar, An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics, Energy Conversion and Management (46) (2005) 1848–1867 http://dx.doi.org/10.1016/j.enconman.2004.08.012.

      [14] I. A. Perez, M. A. Garcia, M. L. Sanchez and Torre de B., Analysis of height variations of sodar-derived wind speeds in Northern Spain, Journal of Wind Engineering and Industrial Aerodynamics (92) (2004) 875–894.

      [15] G. Johnson, Wind Energy Systems, Kansas State University, Manhattan, KS, USA, 2006.

      [16] P. Bhattaracharya and R. Bhattacharjee, “Journal of Applied Quantitative Methods†Vol 5 No. 2 (Summer 2010).

      [17] 1. A. K. Azad, M. G. Rasul and T. Yusaf; Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications, Energies (7) (2014) 3056-3085.

      [18] N. Masseran, A. Mahir Razali, K. Ibrahim, A. Zaharim and K. Sopian, The Probability Distribution Model of Wind Speed over East Malaysia, Research Journal of Applied Sciences, Engineering and Technology 6(10) (2013) 1774-1779

      [19] R. Gupta and A. Biswas, Wind analysis of silchar (Assam, India) by Rayleigh’s and Weibull methods, Journal of Mechanical Engineering Research Vol. 2(1) (February 2010) 010-024.

      [20] E. L. Silva and P. Lisboa, “Analysis of the characteristic features of the density functions for gamma, Weibull and log-normal distributions through RBF network pruning with QLPâ€, Proceedings of the 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, Corfu Island, Greece (February 2007)16-19.

      [21] H. Aksoy, Use of gamma distribution in Hydrological analysis, Turk J Engin Environ Sci (24) (2000) 419-428.

      [22] D. P. Wiens, J. Cheng and N. C. Beaulieu, “Pak. J. Statist†Vol.19 (1) (2003) 129-141.

      [23] P-H. Huang and T-Y. Hwang, “On New Moment Estimation of Parameters of the Generalized Gamma Distribution using its Characterizationâ€, Taiwanese Journal Of Mathematics, Vol. 10 No. 4 (June 2006) 1083-1093.

      [24] C. Forbes, M. Evans, N. Hastings and B. Peacock, Statistical Distributions, John Wiley and Sons, Inc, (2011) 4th Edition.

      [25] T. Y. Hwang and P. H. Huang, On new moment estimation of parameters of the gamma distribution using its characterization, Annals of the Institute of Statistics Mathematics (54) (2002) 840-847, Japan http://dx.doi.org/10.1023/A:1022471620446.

      [26] M. A. Hussain, M. J. Iqbal and S. Soomro, Urban Wind Speed Analysis in Global Climate Perspective: Karachi as a Case Study, Internal Journal of Geosciences (3) (2012) 1000-1009.

      [27] Y. Hou, Y. Peng, A. L. Johnson and J. Shi, Empirical Analysis of Wind Power Potential at Multiple Heights for North Dakota Wind Observation Sites, CS Canada, Energy Science and TechnologyVol4 No. 1 (2012) 1-9.

      [28] R. Kollu1, S. R. Rayapudi, S.V.L. Narasimham and K. M. Pakkurthi, Mixture probability distribution functions to model wind speed distributions, International Journal of Energy and Environmental Engineering, (2012) 3-27.

      [29] C. Carillo, J. Cidras, E. Diaz-Dorado and A. F. Obando-Montano, An approach to determine the Weibull Parameters for wind Energy Analysis: The Case of Galicia (Spain), Energies (7) (2014) 2676-2700.

      [30] O. O. Ajayi, R.O. Fagbenle, J. Katende, O.A. Omotosho and Samson Aasa, Analytical Predictive Model for Wind Energy Potential Estimation: A Model for Pre-assessment Study, Journal of Applied Sciences 12(5) (2012) 450-458 http://dx.doi.org/10.3923/jas.2012.450.458.

      [31] K. Abbas, Alamgir, S. A. Khan, A. Ali, D. M. Khan and U. Khalil, Statistical Analysis of Wind Speed Data in Pakistan, World Applied Sciences Journal 18 (11) (2012) 1533-1539.

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    David, Y., Nfor, Jr, B. G., Kisito, T. P., & Ndinakie, G. P. (2015). Statistical wind speed studies and wind energy potential resource analysis of abong mbang, Cameroon: a case study. International Journal of Basic and Applied Sciences, 4(4), 466-474. https://doi.org/10.14419/ijbas.v4i4.5198