Browsing by Author "El Sayed, Ahmad"

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Minimizing cogging torque in permanent magnet synchronous generators for small wind turbine applications
(Gazi University, 2021-06) El Sayed, Ahmad; Ertürk, E.; El Sayed, Ahmad
Permanent magnet synchronous generators have a wide usage among electric machines, especially in small wind turbine systems. However permanent magnet synchronous generators suffer from cogging torque due to the magnetic interaction between the poles of the rotor’s permanent magnets and the steel laminations of the stator’s teeth. The cogging torque drawback is a major problem in this kind of generators that affects its functionality negatively. In the literature many different approaches for reducing the cogging torque are proposed and each different approach achieves different amount of reduction in the cogging torque. In this study 6 different cogging torque reduction techniques are considered and with finite element simulations using the JMAG simulation software, they are compared with each other in terms of percent reduction in the cogging torque. Present simulations show that among the considered different approaches, continuous skewing technique reduces the cogging torque the most with respect to the each other techniques considered. Also using the step skewing of the rotor or stator, changing the slot opening width and having dummy slots techniques can decrease the cogging torque more or less the same amount in magnitude. Present results indicate that changing the radial shoe depth technique has almost no effect in reducing the cogging torque.
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Multi-criteria decision model for the assessment of offshore wind energy potential of Tunisia
(IEEE, 2021-05-05) Zahmoul, Houda; El Sayed, Ahmad; Poyrazoğlu, Göktürk; Electrical & Electronics Engineering; POYRAZOĞLU, Göktürk; Zahmoul, Houda; El Sayed, Ahmad
This paper investigates the potential for the development of offshore wind power plants in Tunisia. The goal of this research is to increase the possibility of using renewable energy available in the country and reduce the dependence on fossil fuels. The data used for the analysis is obtained from remote sensing satellites and weather stations located across the country's coastal regions. A multi-criteria decision model is built to perform geographic and technical analysis of the study area. This research reveals locations suitable for wind turbine installations. The results of this study aid the transition to wind energy with its optimal adaptation in Tunisia. Only 7% of Tunisian offshore territory is found fit for the deployment of fixed-bottom wind farms. Six locations, in particular, were chosen in the Gulf of Tunisia, Gabes, Sousse, Sfax, Bizerte, and Djerba. The GIS-based methodology proposed in this paper can be applied to other offshore wind farm allocation investigations. The environmental impact of proposed wind farms on GHG reduction and employment opportunities are investigated. The suggested wind farms will reduce the GHG emissions and the energy price in Tunisia as well as create new jobs for society.
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Prediction algorithm & learner selection for European day-ahead electricity prices
(IEEE, 2020-10) Ülgen, Toygar; El Sayed, Ahmad; Poyrazoğlu, Göktürk; Electrical & Electronics Engineering; POYRAZOĞLU, Göktürk; Ülgen, Toygar; El Sayed, Ahmad
The prediction of day-ahead electricity prices with higher accuracy is always helpful for the market players of the power exchange. This study was intended in the first place to find out the best time series prediction method for the selected 14 European countries. The test results of four time-series methods show that the next day prices were more in line with the previous day prices in 87% of the selected countries; Later, a classification approach is followed by 33 different features of each country to answer the question of which method would be the best for the other countries, that were not studied in this paper, would be? As a result, the support vector machine algorithm results in 57% accuracy in classifying an unknown European country to determine the best prediction method. Therefore, this paper focuses now on two correlated studies to find out the best time series prediction methods and a classification approach for selected countries.