Assessment of wind energy potential for small scale water pumping systems in the north region of Cameroon

  • Authors

    • Dieudonné Kidmo Higher Institute of the Sahel, University of Maroua, Cameroun
    • Noël Djongyang Department of Renewable Energy, Higher Institute of the Sahel, University of Maroua, PO Box 46 Maroua, Cameroon
    • Serges Doka
    • Danwe Raidandi
    2014-02-22
    https://doi.org/10.14419/ijbas.v3i1.1769

  • Abstract

    Based on the wind data recorded over a six year period (2007 to 2012) as observed in the main meteorological station of the Garoua International airport, an assessment of the wind potential has been performed by means of the Weibull Probability Density Function (PDF) with two parameters. The maximum likelihood estimation method (MLE) was used to estimate the dimensionless Weibull shape parameter k, and the Weibull scale parameter C. The maximum wind power density extracted by the blades as well as the useful average hydraulic power output and the daily water production of the hypothetic windmill were determined in order to forecast applications in the north region of Cameroon such as providing domestic water, watering farm animals and small scale irrigation.

     

    Keywords: Hydraulic Power Output, Power Density, Weibull Distribution, Weibull Parameters, Wind Speed.

  • References

    1. Abdeen Mustafa Omer. Wind mechanical engineering: energy for water pumping in rural areas in sudan, Technical Proceedings of the 2011 Clean Technology Conference and Trade Show.
    2. Rn Tchinda, Ernest Kaptouom. Wind energy in Adamaoua and North Cameroon provinces, Energy Conversion and Management 44 (2003) 845857.
    3. D.A. Fadare. A Statistical Analysis of Wind Energy Potential in Ibadan, Nigeria, Based on Weibull Distribution Function, The Pacific Journal of Science and Technology Volume 9. Number 1. May-June 2008, Springer.
    4. Mukund R. Patel. Wind and Solar Power Systems, U.S. Merchant Marine Academy Kings Point, New York.
    5. C.G. Justus, W.R. Hargraves, Amir mikail and denise grabber. Methods for estimating speed frequency distributions, Journal of applied meteorology, volume 17, November 1977.
    6. Ajayi, O., Fagbenle, R., Katende. Wind Profile Characteristics and Econometrics Analysis of Wind Power Generation of a Site in Sokoto State, Nigeria, Energy science and technology vol. 1, N 2, 2011 pp. 54-66.
    7. Salahaddin A. Ahmed. Comparative study of four methods for estimating Weibull parameters for Halabja, Iraq, International Journal of Physical Sciences Vol. 8(5), pp. 186-192, 9 February, 2013.
    8. Jamil, M. Wind Power Statistics and Evaluation of Wind Energy Density, Wind Engineering, 18, NOS, 227-240, 1994.
    9. D. Deligiorgi, D. Kolokotsa, T. Papakostas, E. Mantou. Analysis of the Wind Field at the Broader Area of Chania, Crete Proc. of the 3rd IASME/WSEAS Int. Conf. on Energy, Environment, Ecosystems and Sustainable Development, Agios Nikolaos, Greece, July 24-26, 2007.
    10. M.L. Ray, A.L. Rogers, and J.G. McGowan. Analysis of wind shear models and trends in different terrains, Renewable Energy Research Laboratory, Department of Mechanical & Industrial Engineering, University of Massachusetts, Amherst MA 01003.
    11. Olayinka S Ohunakin1, Olanrewaju M Oyewola and Muyiwa S Adaramola. Economic analysis of wind energy conversion systems using levelized cost of electricity and present value cost methods in Nigeria, International Journal of Energy and Environmental Engineering, 2013 4:2.
    12. J.F. Manwell, J.G. McGowan and A.L. Rogers. Wind energy explained: Theory, design and application, John Wiley and Sons Ltd. 2002.
    13. B. Ozerdem, H.M. Turkeli. Wind energy potential estimation and micrositting on Izmir Institute of Technology Campus, Turkey, Renewable Energy 30 (2005) 16231633.
    14. Rn Tchinda, Joseph Kendjioa, Ernest Kaptouom, Donatien Njomo. Estimation of mean wind energy available in far north Cameroon, Energy Conversion & Management 41 (2000) 19171929.
    15. W. palz, W. schnell. Solar Energy R&D in the European Community. Series G Wind Energy Volume 1, Proceedings of the EC Contractors' Meeting held in Brussels, 23-24 November 1982.
    16. Mark L. Morrissey, werner E. cook, J. Scott greene. An Improved Method for Estimating the Wind Power Density Distribution Function, Journal of atmospheric and oceanic technology, volume 27, July 2010.
    17. P. A. Costa Rocha, R. Coelho de Sousa, C. Freitas de Andrade, M. Vieira da Silva. Comparison of seven numerical methods for determining Weibull parameters for wind energy generation in the northeast region of Brazil, Applied Energy 89 (2012) 395400.
    18. Clarence SEMASSOU. Aide la dcision pour le choix de sites et systmes nergtiques adapts aux besoins du bnin, PhD Thesis, Ecole Doctorale de lUniversit Bordeaux 1 ED 209, N dordre : 4450, 15 dcembre 2011.
    19. P.T. Smulders. Wind water pumping: the forgotten option, Energy for Sustainable Development, Volume II No. 5 l January 1996.
    20. M.J. Stevens and P.T. Smulders., The estimation of parameters of the Weibull wind speed distribution for wind energy utilization purposes, Wind Eng. 3 (2), 132-145, 1979.
    21. Cossi NA. Estimation of the mean wind energy in Benin (Ex Dahomey), Renew Energy 1991;1(5/6):84553.
    22. Petersen EL et al. Wind atlas for Denmark Ris /-R-428, 1985. p. 229.
    23. C.G. Justus, W.R. Hargraves, and Ali Yalcin. Nationwide assessment of potential output from wind-powered generators, Journal of applied meteorology, volume 5, N 7 July 1976.
    24. Joop van Meel and Paul Smulders. Wind pumping, a Handbook, World Bank Technical Paper No. 101 (Industry and Engineering Series), Washington D.C, July 1989.

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  • How to Cite

    Kidmo, D., Djongyang, N., Doka, S., & Raidandi, D. (2014). Assessment of wind energy potential for small scale water pumping systems in the north region of Cameroon. International Journal of Basic and Applied Sciences, 3(1), 38-46. https://doi.org/10.14419/ijbas.v3i1.1769

    Received date: 2014-01-17

    Accepted date: 2014-02-13

    Published date: 2014-02-22