Wind energy has become one of the world’s most renewable energy sources in recent years. It is regarded as a clean energy source because it produces no greenhouse gas emissions. The assessment of wind energy resources is an important step in the development of any wind energy conversion system (WECS). As a result, this article examines the wind energy potential of nine Jordanian wind locations: Queen Alia Airport, Civil Amman Airport, King Hussein Airport, Irbid, Mafraq, Ma’an, Ghor Al Safi, Safawi, and Irwaished. The available wind speed data were implemented using three statistical distribution models, Weibull, Rayleigh, and Gamma distributions, and one traditional estimation method, the Maximum Likelihood Method (MLM). Three optimization techniques were used to assign parameters to each distribution model: Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA). To determine the optimal distribution model, the performance of these distribution models was tested. According to the findings, King Hussein Airport features the highest wind power density, followed by Queen Alia Airport, while Irbid features the lowest, followed by Ghor Al Safi.
The average wind speed in a given area has a significant impact on the amount of energy that can be harvested by wind turbines. The regions with the most attractive possibilities are typically those that are close to the seaside and have open terrain inland. There is also good potential in several mountainous locations. Despite these geographical restrictions on where wind energy projects can be located, there is enough topography in most of the world’s regions to use wind energy projects to meet a significant amount of the local electricity needs. This paper presents a new method of energy prediction of wind resources in several wind sites in Jordan, which can be used to decide whether a specific wind site is suitable for wind farm installation purposes. Three distribution models, Weibull, Gamma and Rayleigh, were employed to characterize the provided wind data. Different estimation methods were used to assign the parameters associated with each distribution model and the optimal parameters were estimated using whale optimization algorithms which reduce the error between the estimated and the measured wind speed probability. The distribution models’ performance was investigated using three statistical indicators. These indicators were: root mean square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). Finally, using the superlative distribution models, the wind energy for the chosen wind sites was estimated. This estimation was based on the calculation of the wind power density (ED) and the total wind energy (ET) of the wind regime. The results show that the total wind energy ranged from slightly under 100 kWh/m2 to nearly 1250 kWh/m2. In addition, the sites recording the highest estimated wind energy had the optimum average wind speed and the most symmetrical distribution pattern.
Choosing the right wind site and estimating the extracted energy of the wind turbines are essential to successfully establishing a wind farm in a specific wind site. In this paper, a method for estimating the extracted energy of the wind farms using several mathematical models is proposed. The estimating method, which was based on five wind turbines, Q1, Q2, Q3, Q4, and Q5 and three wind distribution models, gamma, Weibull, and Rayleigh, was used to suggest suitable specifications of a wind turbine for a specific wind site and maximize the extracted energy of the proposed wind farm. An optimization problem, developed for this purpose, was solved using the whale optimization algorithm (WOA). The suggested method was tested using several potential wind sites in Jordan. The proposed wind farms at these sites achieved the maximum extracted energy, maximum capacity factor (CF), and minimum levelized cost of energy (LCoE) based on the solution of the developed optimization problem. The developed model with Q3 and the Rayleigh distribution function was validated with real measurement data from several wind farms in Jordan. Error analysis showed that the difference between the measured and estimated energy was less than 20%. The study validated the provided model, which can now be utilized routinely for the assessment of wind energy potential at a specific wind site.
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