Publication: ASSESSMENT OF WIND CHARACTERISTICS AND POWER POTENTIAL AT KESSES LOCATION - UASIN-GISHU COUNTY, KENYA
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2015
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University of Eldoret
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Abstract
Wind energy is increasingly becoming popular all over the world as a green energy source for electricity generation since it is renewable and environmentally friendly. Pioneer wind turbines for electricity generation in Kenya were recently installed at Ngong Hills and Lake Turkana, and more is expected to be initiated in different parts of the country. Wind turbines extract the kinetic energy carried by the flowing wind and this energy is directly proportional to the cube of wind speed. Thus, the wind speed is the most important parameter to consider in designing and selecting an efficient wind energy conversion system. Meteorological Department (MET) and some learning institutions in Kenya have been collecting and storing climatic data for several years, including wind speeds and most of them have not been analyzed. Precise knowledge of availability of wind at any given location is a pre-requisite for the effective planning and implementation and speed analysis is useful for the assessment of wind characteristicsand power potential at a location. In this work, analyses of five years (2009-2013) wind speed data collected at a meteorological unit at Moi University, Kesses area, Uasin Gishu County- Kenya, was done. The station measures wind speed at a height of 2 m and were extrapolated to the standard height of measurements of 10 m and typical hub heights of 40 m, 70 m and 100 m for purposes of characterization and determination of energy potential respectively. The extrapolated results revealed that the average annual wind speed at the height of 10 m is 3.86 ms-1, meaning that the location wind speed can be classified as class IV with a maximum wind power density of 100 Wm-2. The average annual wind speed at the hub heights of 40 m, 70 m and 100 m were 5.48ms-1, 6.33ms-1and 6.93ms-1, giving corresponding power densities of 115.563Wm-2, 175.395Wm-2and 228.917 Wm-2respectively. Weibull distribution model was used in the analysis of wind speed distribution. The Weibull scale parameter range from 2.543 ms-1to a maximum of 3.046 ms-1. The Weibull shape parameter was peaked at 5.902 in the year 2012. Both cumulative and probability density function were assessed and graphically presented.Results showed that the site has potential for harnessing wind energy for electricity generation and both small and medium scale wind power turbines are recommended for installation at the site.
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University of Eldoret