Investigating seasonal wind energy potential in Vredendal, South Africa

  • Thapelo Cornelius Mosetlhe Tshwane University of Technology http://orcid.org/0000-0002-3752-8576
  • Adedayo Ademola Yusuff University of South Africa
  • Yskandar Hamam Tshwane University of Technology

Abstract

Global warming and the energy crisis have necessitated an urgent exploitation and utilisation of renewable energy. Wind energy has gained popularity over the years because of vast availability of its resource. A study was carried out to investigate the stochastic characteristics of the available wind energy at installation sites. Data for a ten-minute interval wind speed collected over a period of five years and measured at a height of 10, 40 and 62 m in Vredendal was considered. Wind speed data was arranged in seasonal format and its statistical distribution investigated based on Weibull, lognormal and gamma distributions. The Anderson-Darling test and Akaike information criterion were used to evaluate the goodness of fit. The results showed that wind power at different heights and time stamps exhibited different statistical distribution. It was found that wind turbines in Vredendal must be installed as high as possible to harness wind power effectively. During summer and spring, there was a high potential for wind power availability compared with that of winter.

Author Biographies

Thapelo Cornelius Mosetlhe, Tshwane University of Technology
Post Graduate Student
Adedayo Ademola Yusuff, University of South Africa
Deparment of Electrical and Mining Engineering, Associate Professor
Yskandar Hamam, Tshwane University of Technology
Deparment of Electrical Engineering, Professor

References

Abdulkarim, A., Abdelkader, S. M. & John Morrow, D. 2015. Statistical analyses of wind and solar energy resources for the development of hybrid microgrid. In: Oral A., Bahsi Oral Z., Ozer M. (eds) 2nd Interna-tional Congress on Energy Efficiency and Energy Related Materials (ENEFM2014), Springer Proceed-ings in Energy, Switzerland, 2015: 9-14.
https://doi.org/10.1007/978-3-319-16901-9_2
Akaike, H. 2011. Akaike's information criterion. In: International Encyclopedia of Statistical Science. Berlin, Heidelberg: Springer Berlin Heidelberg: 25-25.
https://doi.org/10.1007/978-3-642-04898-2_110
Alberto, L.-G. 2008. Probability, statistics, and random processes for electrical engineering. Upper Saddle River, NJ: Pearson/Prentice Hall.
Anderson, T. W. & Darling, D. A. 1952. Asymptotic the-ory of certain ‘goodness of fit’ criteria based on sto-chastic processes. The Annals of Mathematical Statis-tics 23: 193-212.
https://doi.org/10.1214/aoms/1177729437
Ayele, N. L., Akshay, K. S. & Rudiren, P. C. 2018. Char-acterisation of wind speed series and power in Dur-ban. Journal of Energy in Southern Africa 28(3): 66-78.
Ayodele, T. R., Jimoh, A. A., and Munda, J. L. & and Agee, J. T. 2013. A statistical analysis of wind distri-bution and wind power potential in the coastal re-gion of South Africa. International Journal of Green Energy 10(8): 814-834.
https://doi.org/10.1080/15435075.2012.727112
Ayodele, T. R., Jimoh, A. A., Munda, J. L. & Agee, J. T. 2012. Statistical analysis of wind speed and wind power potential of Port Elizabeth using Weibull pa-rameters. Journal of Energy in Southern Africa 23(2): 30-38.
Celik, A. N. 2004. A statistical analysis of wind power density based on the Weibull and Rayleigh models at the southern region of Turkey. Renewable Energy 29(4): 593-604.
https://doi.org/10.1016/j.renene.2003.07.002
Dombaycı, Ö. A. & Gölcü, M. 2009. Daily means ambi-ent temperature prediction using artificial neural network method: A case study of Turkey. Renewable Energy 34: 1158-1161.
https://doi.org/10.1016/j.renene.2008.07.007
Gokcek, M., Bayulken, A. & Bekdemir, S. 2007. Investi-gation of wind characteristics and wind energy po-tential in Kirklareli, Turkey. Renewable Energy 32(10): 1739-1752.
https://doi.org/10.1016/j.renene.2006.11.017
Hsu, H. P. 1997. Theory and problems of probability, random variables, and random processes. Schaums Outline Series.
Keyhani, A., Ghasemi-Varnamkhasti, M., Khanali, M. & Abbaszadeh, R. 2010. An assessment of wind ener-gy potential as a power generation source in the cap-ital of Iran, Tehran. Energy 35(1): 188-201.
https://doi.org/10.1016/j.energy.2009.09.009
Liptser, R. & Shiryaev, A. N. 2013. Statistics of random processes: I. General theory. Springer Science & Business Media.
Mosetlhe, T. C., Yusuff, A. A. & Hamam, Y. 2017. As-sessment of small signal stability of power systems with wind energy conversion unit. Proceedings of IEEE Africon, Cape Town, South Africa, 2017: 1089-1094.
https://doi.org/10.1109/AFRCON.2017.8095634
Mosetlhe, T. C., Yusuff, A. A., Hamam, Y. & Jimoh, A. A. 2016. Estimation of wind speed statistical distribu-tion at Vredendal, South Africa. Proceeding of Inter-national Association of Science and Technology for Development (IASTED), Gaborone, Botswana, 2016: 344-349.
Mostafaeipour, A., Jadidi, M., Mohammadi, K. & Seda-ghat, A. 2014. An analysis of wind energy potential and economic evaluation in Zahedan, Iran. Renew-able and Sustainable Energy Reviews 30(1): 641-650.
https://doi.org/10.1016/j.rser.2013.11.016
Mulaudzi, S. T., Sankaran, V. & Lysko, M. D. 2013. So-lar radiation analysis and regression coefficients for the Vhembe Region, Limpopo Province, South Afri-ca. Journal of Energy in Southern Africa 24(3): 1-7.
Nogay, S. H., Akinci, T. C. & Eidukeviciute, M. 2012. Application of artificial neural networks for short term wind speed forecasting in Mardin, Turkey. Journal of Energy of Southern Africa 23(4): 2-7.
Olaofe, Z. O. & Folly, K. A. 2012. Statistical Analysis of Wind Resources at Darling for Energy Production. International Journal of Renewable Energy Research 2(2): 250-261.
Özgür, M. A. 2014. ANN-based evaluation of wind power generation: A case study in Kutahya, Turkey. Journal of Energy in Southern Africa 25(4): 11-22.
Ranganai, E. & Nzuza, M. B. 2015. A comparative study of the stochastic models and harmonically coupled stochastic models in the analysis and forecasting of solar radiation data. Journal of Energy in Southern Africa 26(1): 125-137.
Safari, B. & Gasore, J. 2010. A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda. Renewable Energy 35(12): 2874-2880.
https://doi.org/10.1016/j.renene.2010.04.032
Saucier, R. 2000. Computer generation of statistical dis-tributions. Defense Technical Information Center.
https://doi.org/10.21236/ADA374109
Taylor, R., 1990. Interpretation of the correlation coeffi-cient: A basic review. Journal of Diagnostic Medical Sonography 6(1): 35-39.
https://doi.org/10.1177/875647939000600106
Views
  • Abstract 0
  • pdf 0
Views and downloads are with effect from 11 January 2018
Published
2018-06-22