Abstract:
The rapidly increasing population and industrialization has led to increased energy demand. Juja, which is about 1416 m above sea level; 10 10’ S, 370 7’ E and about 35 km from Nairobi, having a rapidly growing population and a major University requires an alternative energy source due to frequent power failure and high power demand. Use of generator backup is an additional cost and also pollutes the environment. Therefore, an accurate analysis on wind speed characteristics which is necessary in determining wind energy potential is critical. This study analyzed the wind speed characteristics for purposes of determining wind energy potential in Juja at heights of 10 m and 30 m for a period of three months. The wind data was recorded in hourly intervals and mean diurnal and monthly variations were calculated. Weibull scale and shape parameters were obtained using Weibull-fit, Regression and Maximum Likelihood methods. The wind speed distribution was modelled using the Weibull and Rayleigh probability distribution functions. Power densities for different methods were calculated. Windographer software © and Microsoft Excel were used to determine and generate the Probability Density functions (PDFs) and to analyze wind direction. Results obtained from Juja site were compared with results acquired from Naivasha, St. Xavier site which is at an altitude of 2,086 m; 0° 43' 0" S, 36° 26' 0" E. . The wind speed averages for the duration of three months for the 10 m and 30 m heights were 2.54 m/s and 3.04 m/s respectively. The wind shear exponent and roughness parameter were 0.1652 and 0.0374 respectively. The mean wind power densities for Juja (10 m and 30 m) and Naivasha (10 m) were 12.68 W/m², 20.65 W/m² and 39.95 W/m² by Weibull model and 14.51 W/m², 22.43 W/m² and 28.63 W/m² by Rayleigh model respectively. The methods that fitted best Juja and Naivasha sites were Weibull-fit and Maximum Likelihood respectively. The results are expected to be useful in guiding designers on selection of appropriate type of wind turbines for optimum power generation
