Abstract
Due to the increasing environmental and economic cost of fossil fuels, alternative sources of energy are needed. One of these sources is wind energy. The wind-turbines extract kinetic energy from the wind to convert it to mechanical energy and then transfer to electrical energy. Wind speed is the most important parameter for an efficient wind energy system. In this work the Microsoft excel software used to analysis of wind speed data and evaluate the wind speed distribution. the wind speed probability estimated and analyzed by using five methods of Weibull and Rayleigh distributions and evaluated the best methods to represent the actual data based on monthly mean wind speed data of the Ma'an city site, Jordan. furthermore, from the analysis, it has been found that the energy pattern factor method EPFM is the best method to represent the actual data and the EPFM is the best and most accurate and efficient method to determine the Weibull distribution parameters ( k ) and ( c ). In addition, in this work, the annual average shape parameter ( k ) is 3.4 and the annual average scale parameter ( c ) is 4.0 m/s. The most probable wind speed is 4.4 m/s in August and the maximum wind speed carrying maximum energy is 5.2 m/s occurs in October. Meanwhile, the maximum power and energy density are 57.5 W/m 2 , 42.8 kWh/m 2 respectively in August. Moreover, the site has annual power density 39.3W/m 2 and 345.5 kWh/m 2 of energy density. Keywords : Renewable energy, Wind energy, Wind speed, Weibull distributions, Power density, Energy density DOI: 10.7176/JETP/11-4-05 Publication date: September 30th 2021
Highlights
The energy crisis and growing environmental awareness within the present day has become more important, and as a result of that, there are a global shifting towards substitutes of the conventional energy to sustainable resources and new technologies for the demand consumption
The wind atlas indicates two regions in Jordan that have large potential wind energy especially in southern and northern parts of Jordan (Alrawashdeh, 2018) there are another studying of wind energy potential in the world and in Jordan such as the (Al Nhoud and Mohammad, 2015) studied the Weibull parameters in the Azraq south, Northeast Badia of Jordan using real wind speed data, the data measured at 10 m height and the mean wind speed data was analyzed, he found the highest and the lowest wind power potential are in July and December, respectively
In this work, the wind speed data were recorded at a height of 10 m, continuously by a cup-generator anemometer at the Jordan Metrological Department/ Ma'an City and the two parameters of the Weibull www.iiste.org probability density function have been determined by MOM, Empirical method (EM), Energy Pattern Factor Method (EPFM) and Graphical Method (GM)
Summary
The energy crisis and growing environmental awareness within the present day has become more important, and as a result of that, there are a global shifting towards substitutes of the conventional energy to sustainable resources and new technologies for the demand consumption. The Wind energy conversion systems are chosen based on wind speed potential analysis of a region, Jordan has high potential of wind energy resources, where at 10 m height the annual average wind speed exceeds 7 m/s in some areas of the country such Amman, Irbid, Ma'an, Tafilah, Aqaba and Mafraq. This energy has a very low cost (Baniyounes, 2017). Found the least squares regression method is the better performance method than other selected methods in the investigation. (Islam, et al, 2011) studied the parameters of Weibull distribution to evaluate and assess the wind energy potentiality at Kudat and Labuan, Malaysia. (Keyhani, et al, 2010) studied the parameters of Weibull distribution to assess the wind energy potentiality in the Tehran, Iran
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