الفهرس 
Wind EnergyOnly 14 pages are availabe for public view
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Abstract
Recently, wind energy is regarded as the prospective energy source for electricity generation. Several strategic factors influence the high level of penetration of the wind energy into power grids worldwide, such as depletion of our natural resources, increase in fuel price, climate concerns, and trend to a clean energy. At the present, variable-speed wind generators have become more dominant for the modern wind power industry. This interest is due to the superior characteristics that they offered, such as less mechanical stress, lower quantities of ripples, and better control capability. Permanent-magnet synchronous generators (PMSGs) are the most commonly machines used in the wind power generation systems due to their features, including the self-excitation, the high-power density, and the high power factor operation. The variable-speed wind turbine driving PMSG is connected to the power grid through a full-scale frequency converter. The well-designed cascaded control scheme is used to control the generator- and grid-side converter/inverter. This control strategy is essentially based on the proportional-integral (PI) controllers due to their robustness and the fact of the widely stability margins that they offered. However, these controllers suffer from the high sensitivity to the system nonlinearity and parameters’ variation. This thesis presents different control strategies with the purpose of enhancing the dynamic and transient stability of a grid-connected wind generator system. A linearquadratic regulator controller, a hybrid adaptive neuro-fuzzy inference system-genetic algorithm controller, and a continuous mixed -norm-based adaptive fuzzy logic controller are presented to optimally control the generator- and grid-side converter/inverter through a cascaded control scheme. For achieving realistic responses, real wind speed data extracted from Zaafarana wind farm, Egypt, are considered in the analyses. The effectiveness of the proposed controllers is compared with that achieved using an optimal PI control scheme, considering severe grid disturbances. Genetic algorithm, grey wolf optimizer algorithm, and particle swarm optimization techniques are presented to properly fine-tune the PI controllers. The validity of the proposed control strategies is extensively verified by the simulation analyses, which are carried out using MATLAB/Simulink environment.