الفهرس 
A GENETIC ALGORITHM APPROACH FOR WIND ENERGY RESOURCE ASSESSMENT AT GABAL AL-ZAYT WIND FARM IN EGYPT
SummaryOnly 14 pages are availabe for public view
 |  | from | 115 |  |  |
| | | from | 115 | | |
Abstract
The potential of wind energy as a sustainable and clean alternative to fossil fuels for power generation has attracted extensive attention in the last few decades. Wind speed distribution is one of the most significant parameters in the estimation of wind energy potential, design of wind farms, and selection of suitable wind turbines. The best distribution that describes the wind speed variation is the Weibull probabilistic distribution function.The two parameters of the Weibull probabilistic distribution function are widely used for assessment and prediction of wind energy resources. In the present study, seven different methods have been employed to obtain the parameters of the Weibull distribution function.The comparison between the employed methods is based on the use of the root mean square error (RMSE) and the correlation coefficient (R²) of the estimated data as compared to the field data. In this study, a genetic algorithm approach is also presented to minimize the estimation error in the parameters of the Weibull probabilistic distribution function. The real data at Gabal Al Zayt, collected through the period May 2018 to April 2019, is used to obtain the parameters of the Weibull probabilistic distribution function. Using the obtained parameters, the best wind turbine is selected according to the capacity factor. As a result, accurate planning decisions can be made during the development of current and new wind energy projects