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The potential of wind energy in a country varies depending on the region. For example, in Northern regions of Nigeria, cities like Minna, Sokoto, Kano and Jos are the most potential locations and experience not more than 5.24 m/s mean wind speed with Jos having the highest average wind speed. In most cases internationally wind turbines are design to operate at the rated wind speed much greater than mean wind speeds in Nigeria (in most cases above 8 m/s). Installation of these wind turbines in a similar location to Nigeria (in terms of wind speed condition) will significantly decrease their performance. Therefore, it become necessary to design and produce wind turbines that will function efficiently in low wind speed locations. One of the technologies to improve wind turbine rotor blade effectiveness in low wind regime is to boost the operating angle of the wind turbine, and hence the prospect for the improvement of wind turbine performance. An innovative way recognized previously by some researchers for increasing the working angle of the blade at low wind speed for improved performance is modification of wind turbine blade leading edge. This research studied three-bladed wind turbine rotor with modified blades leading edges (by incorporating sinusoidal bumps) at low wind speeds using CFD method. Two CAD models of wind turbine blades based on 0.8m three-bladed wind turbine rotor were modelled and simulated. One blade model is having straight leading edge (N-Blade) and the other having bumpy leading edge (M-Blade). Both models have NASA LS (1)-0413 cross-section profile. Simulations were run using ANSYS 20 from a velocity of 2 m/s to 10 m/s at the interval of 2m/s considering TSR of 6 and 8. At a TSR of 6, the coefficients of performance (Cp) values are not equal, with the straight blade having better performance than the bumpy blade at all velocities tested; the Cp values of blade with straight leading edge are 0.180, 0.267, 0.300, 0.313, 0.322 at respective velocities of 2, 4, 6, 8, and 10m/s, while Cp values of blade with bumpy leading edge at the same velocities are 0.165, 0.250, 0.281, 0.294, and 0.303 respectively. At a TSR of 8, the CP values closely match for both straight and bumpy blade at all velocities tested for. Simulations were further run at constant angular velocity of 120 rad/s for a TSR of 2, 4, and 10, where the peak performance occurs is around TSR of 6 and 8. For instance, the Cp values of M-Blade are 0.022, 0.173, 0.294, 0.313, and 0.116 respectively at the TSR of 2, 4, 6, 8, and 10. At TSR of 2, 4, 8 and 10, the performance of both airfoils closely matches except at a TSR of 6 where the N-Blade Cp value is 0.313 and is better than that of M-Blade Cp value (0.294). It shows that the blade leading edge modification could not have advantage at all flow conditions and size of the HAWT blades; it shows no advantage on the performance of the rotor blade tested in this research at the velocities at which the simulation was conducted.

This paper presents an estimation of wind power potential of North East, Nigeria (Bauchi and Maiduguri) on the basis of monthly wind speed data at 10m height from the ground. The data for the locations were collected from Nigeria metrological station, Abuja for the period of (2013-2017). Mean monthly values were used in calculation of Weibull distribution parameters c (scale factor ms-1) and k (shape factor). The Weibull results shows that for Bauchi, the shape factor ranges from 2.86-5.96 and scale factor ranges from 2.32ms-1-2.54ms-1 while Maiduguri the shape factor ranges from 2.66-5.52 and values of scale factor ranges from 4.74ms-1-5.89ms-1. It is evident that the maximum average monthly value of wind speed in Bauchi occurs in year 2017 with value of 3.8ms-1 in the month of May while the maximum average wind speed in Maiduguri occurs in year 2013 with value of 8.5ms-1 in the month of December. The probability distribution function f(V) of wind speed, together with the duration function T(V) was evaluated for the period under investigation. From the statistical analysis of distributions, the Weibull distribution was found to have better fittings in the probability distribution functions f(V) and T(V). The value of power density was computed to be 33.47W/m 2 (class I) & 374.62W/m 2 (class II) and energy density was also computed to be 24.9 kWh/m 2 & 278kWh/m 2 for both Bauchi and Maiduguri respectively.

Abstract:The electrical and fuel energy consumption for seven years period was collected for rolling mill company, Kano northwest Nigeria. Monthly energy consumption data were gathered and analyzed. The average electrical energy and thermal energy consumption per tonne of product for the period considered are 34.04GJ/Tonne and 99.74GJ/Tonne respectively. The energy end users were identified as electric furnace, DC main motor, Compressor machine, Air-conditioning, Lightning and Electrical equipment. Regression techniques were used to correlate the monthly energy consumption with product weights in Tons. The Coefficient of determinant (R2) for electrical energy consumption was 0.8458 while R2 for fuel oil energy consumption was 0.7773 indicating strong correlation for electrical and fuel oil energy consumption.

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