الفهرس 
Chapter one:- IntroductionOnly 14 pages are availabe for public view
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Abstract
On-Load Tap-Changer (OLTC) is utilized in regulating the voltage in electric power systems. Recently, Electronic On-Load Tap-Changer (EOLTC) is presented to eliminate the OLTC limitations and drawbacks. This thesis presents an intelligent controller for EOLTC. The proposed controller aims to regulate the load voltage via fast and accurate adjusting the EOLTC when load voltage or power system voltage is changed. A Multi-Layer Feed Forward Neural Network (MFFNN) controller is used in this proposal. The thesis proposes a solution to mitigate the unbalance voltage issue caused by the high penetration of distributed generations (DGs) and unsymmetrical loads in the low voltage distribution networks (LVDN) using EOLTC. Also, the performance of the EOLTC under voltage sag with connecting DGs and shunt capacitors is discussed. The results show the efficiency of EOLTC with the proposed technique for improving the power quality in sag periods. The proposed strategy is tested on a practical distribution feeder extracted from the North Delta Electrical Distribution Company in Egypt (NDEDC). MATLAB/Simulink is used as simulation tool for modelling the EOLTC intelligent control and distribution transformer for voltage regulation in LVDN. Furthermore, this thesis proposes a strategy for coordinating the operation of the OLTC and the reactive power compensation (RPC) with DGs in the distribution networks. The proposed coordination strategy obtains the optimal values of the active power generated from the DGs, the reactive power injected from the Switched Capacitors (SCs), and the tap settings of the OLTC by using the Multi-Objective Particle Swarm Optimization (MOPSO). The objective function is to minimize the Total Active Power Losses (TAPL) for all lines and the Total Voltage Deviation (TVD) for all nodes in the distribution network.