Wind Resource Assessment for Wind Energy Utilization in Port Harcout, River State, Nigeria, Based on Weibull Probability Distribution Function

Yıl 2013, Cilt: 3 Sayı: 1, 180 - 185, 01.03.2013

Christopher Okechukwu Izelu Orobome Larry Agberegha Olusola Bode Oguntuberu

Öz

Abstract- The development and sitting of wind energy conversion systems, for electrical power generation and other applications, in various states of the Federal Republic of Nigeria demand proper wind resource assessment of the project sites. This paper therefore presents an assessment of wind resource for wind energy utilization in Port Harcourt, River State, Nigeria. The average monthly wind velocity data, obtained from the Nigerian Meteorological Agency, Port Harcourt, River State, Nigeria, was used, in conjunction with the logarithmic profile equation, to determine wind velocity data at a desired hub height, and with the Rayliegh probability distribution function, a form of Weibull probability distribution function, to determine wind velocity and energy distribution. The results obtained include the wind velocity distribution, wind energy distribution, and the optimum average wind velocity of 17.75 m/s at an altitude of 50 m, which corresponds to the optimum power density or yield of 1370.13 W/m2. The results also revealed a maximum power density or yield of 10731.08 W/m2. This amount of energy corresponds to a maximum average wind velocity of 35.25 m/s beyond which the power density drops off. These results are quite adequate and indicative of high wind energy potentials for Port Harcourt, River State, Nigeria.

Anahtar Kelimeler

Keywords: Wind Resource, Wind Energy Studies, Wind Energy Conversion System, Port Harcourt, River State, Nigeria

Kaynakça

• De Renzo, D.J., Wind Power: Developments, Noyes Data Corporation, New Jersey, pp.5-7, 1979
• Gipe, P., Wind Energy Comes of Age, John Wiley & Sons, London, pp.12-13, 1995.
• Gipe, P., “Wind Stats Newsletter”, vol. 10, No. 2, p 8, Boles, M.A. and Cengel, Y.A., Thermodynamics: An Engineering Approach, McGraw Hill Publishers, 1221
• Avenue of The Americas, New York, pp.394-375, 287- , 2002.
• Stiebler, M., Wind Energy systems for Electric Power Generation, Springer Publishers, Berlin, pp. 1 - 9, 11 - 23, NEED, Exploring Wind Energy - Student Guide, the NEED Projects, pp 10 - 16, 2007.
• GWEC, Global Wind 2006 Report, Global Wind Energy Council, 2007.
• TCOPA, Wind Energy, the Energy Report, pp 159 - 182, RETScreen, Wind Energy Project Analysis, Clean Energy Project Analysis: RETScreen Engineering and Cases Textbook, RETScreen International Clean Energy Decision Support center, pp 3 - 28, 2004.
• Hesling, S., Renewable Energy Trailer: Wind Turbine and Power Storage and Management Systems - specification, Design, Manufacture and Testing, School of Engineering and Physical Scientists, Herriot Watt University, Edinburgh, 2006.
• Jamdade, S. G. and Jamdade, P. G., “Analysis of Wind Speed Data for Four Locations in Ireland Based on Weibull Distribution’s linear regression Model”, Vol. 2, No. 3, PP 451 - 455, 2012.
• Al-Shemmeri, T., Wind Turbines, Case Study, T. Al-Shemmeri Bookboon.com, pp. 76 - 87, 2010. Ventus Publishing ApS, Appendix