An Assessment of Wind Power Density in South East Nigeria, Enugu
American Journal of Modern Energy
Volume 2, Issue 6, December 2016, Pages: 58-62
Received: Aug. 29, 2016;
Accepted: Dec. 9, 2016;
Published: Jan. 16, 2017
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A. Ahmed, Department of Mechanical Engineering, Kano University of Science and Technology, Wudil, Nigeria
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The aim of this study is to establish the wind power potential for the wind energy resource in Enugu, south east Nigeria. For this purpose, monthly wind speed data were obtained at 10m height from Nigeria Meteorological station NIMET, Abuja for the period (1990 – 2006). The monthly average values of wind speed, standard deviation, Weibull parameters and wind power were determined. The Weibull and Rayleigh probability density function and the cumulative distribution function respectively were also evaluated. The results show that this region, according to wind power classification is in wind power class II because values of wind power density is greater than 100W/m2. From the results obtained electricity generation from wind power is quite promising for the installation of wind turbines.
Power Density, Nigeria, Wind Class, Rayleigh, Weibull
To cite this article
An Assessment of Wind Power Density in South East Nigeria, Enugu, American Journal of Modern Energy.
Vol. 2, No. 6,
2016, pp. 58-62.
Copyright © 2016 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/
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Ahmed A, Bello AA, Habou D. An evaluation of wind energy potential in the northern and southern regions of Nigeria on the basis of Weibull and Rayleigh models. American Journal of Energy Engineering 2013; 1: 37–42.
Ahmed A, El-Suleiman A, Nasir A. An assessment of wind energy resource in north central Nigeria, Plateau. Science Journal of Energy Engineering 2013; 1: 13-17.
Akpinar EK, Akpinar S. Statistical analysis of wind energy potential on the basis of the Weibulland Rayleigh distribution for Agin-Elazig Turkey. Power and Energy 2004; 218: 557–565.
Alsaad, MA. Wind energy potential in selected areas in Jordan. Energy Conversion and Managment 2013; 65: 704–708.
Chen D, Song MX, Zhang X. A statistical method to merge wind cases for wind power assess-ment of wind farm. Journal of Wind Engineering and Industrial Aerodynamics 2013; 119: 69–77.
Chen D, Zhang W. Exploitation and research on wind energy. Energy Conversion Technology2007; 4: 339–343.
Emami N, Behbahani-Nia A. The statistical evaluation of wind speed and power density in the Firouzkouh region in Iran. Energy Sources 2012; 34: 1076–1083.
Jie W, Jianzhou W, Dezhong C. Wind energy potential assessment for the site of Inner Mongoliain China. Renewable and Sustainable Energy Reviews 2013; 21: 215–228.
Landberg L. Short-term prediction of power production from wind farms. Journal of Wind Engineering and Industrial Aerodynamics 1999; 80: 207–220.
Stathopoulos C, Kaperoni A, Galanis G, Kallos G. Wind power prediction based on numerical and statistical models. Journal of Wind Engineering and Industrial Aerodynamics 2013, 112: 25-38.
Wiser R, Bolinger M. Annual report on U.S. Wind power installation, cost and performance trend2006, NREL, US Department of Energy, Golden, Co: Available at:(http://www.nrel.gov/wind/pdfs/41435.pdf).