الفهرس 
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Abstract
This thesis proposes an integrated system consisting of a flux-modulated magnetic gear (FMMG) surrounding a permanent magnet synchronous generator (PMSG) in a wind energy transmission system. The purpose of using FMMG is to raise the output speed of the wind turbine to match the speed required to produce electrical energy at the desired frequency for the electrical grid. The proposed FMMG is a high torque density magnetic gear topology with a cost-effective PM arrangement. The principle of operation of the FMMG depends on achieving the gear impact by modifying magnetic fields using stationary ferromagnetic pole pieces. The role of ferromagnetic pole pieces is explained by analyzing the flux density waveforms and their corresponding space harmonics in the air gaps. Also, the thesis proposes employing a new optimization algorithm, the Pelican Optimization Algorithm (POA), presented in 2022 in the optimal design process of FMMG. The purpose of the optimal design process of FMMG is to determine the thickness and the dimension of each region within a definite volume of the FMMG to produce the maximum torque density. Another optimal design process of FMMG based on the well-known Particle Swarm Optimization (PSO) is performed to compare the PSO-based design results with that of POA-based design. The comparative study between the results of the POA and PSO is executed for the same FMMG under the same conditions and constraints. Before performing the optimal design process, a sensitivity analysis based on the Taguchi method is implemented to determine the approximate optimal levels for the design parameters and identify the most influential design parameter on system’s performance. The results underline that the optimum design for the FMMG/PMSG integrated system based on POA achieves more torque/weight ratio and then more generated power and currents as compared to PSO. It is concluded that the proposed design based on POA gives a superior and efficient design with substantial stability and rapid convergence with fewer needed optimization parameters.