الفهرس 
IntroductionOnly 14 pages are availabe for public view
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Abstract
Microgrids (MGs) have been widely used to benefit both utilities and customers by integrating distributed energy resources (DERs), renewables, and non-renewables. MGs are evolving in conjunction with advances in information and communication technology (ICT). MG technologies help the power grid grow into one that is more efficient, less polluting, has lower losses, and is more adaptable to meet the needs of energy consumers. However, numerous technological issues have arisen as a result of the nature of diverse renewable energy sources (RESs) incorporated into the MGs, such as unpredictability and difficulty to accurately predict and control. Three modes of operation MGs can operate in; utility grid- connected, islanded and isolated. Isolated MG may exist in remote areas because of the geographical and/or utility investment limitations. The isolated MG has its own advantages and challenges depending on its configuration, energy resources and nature of loads. The capability of energy sharing between the energy sources within the isolated MG requires designing an effective power management strategy (PMS).
This thesis introduces a PMS based on fuzzy inference system (FIS) and adaptive neuro fuzzy inference system (ANFIS) for AC MG consisting of a diesel generator (DG), a doubly fed induction generator (DFIG) driven by a wind turbine (WT) and a solar photovoltaic (PV) panel. The proposed strategy aims to achieve MG power balance, decrease DG fossil fuel to minimum consumption, keep the MG voltage and frequency stability and finally tracking the maximum power point (MPP) of each RES. The climatic parameters are the inputs of the proposed controllers to help deduction of the most accurate mechanical power of the DG. The DFIG is operating on sub-synchronous mode to promote the connection of the PV terminals to DC link and save the use of a separate inverter between the PV and the MG load bus voltage.
Metaheuristic optimization techniques, including genetic algorithm (GA) and particle swarm optimization (PSO), are employed to train the ANFIS to accomplish the desired objectives and fulfill the generation/consumption balance. The proposed AC MG with the PMS is simulated by the MATLAB/Simulink software in order to analyze the system performance under different climatic conditions and load nature to reveal the robustness of the presented strategy. The simulation results under symmetrical and asymmetrical electrical faults validated the effectiveness of the proposed strategy.