الفهرس 
INTRODUCTIONOnly 14 pages are availabe for public view
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Abstract
One type of system instability is a voltage collapse, which is a phenomenon that can affect transmission networks, when there is insufficient reactive power generation to meet reactive power demand generally during high demand and/or power transfer levels. This unbalance could occur because of unexpected significant increases in demand, generation losses, multiple circuit trips or various combinations of these events, leading to possible widespread voltage collapse and blackout. Voltage collapse is characterized by a slow variation in the system operating point, due to increase in loads, in such a way that voltage magnitudes gradually decrease until a sharp. Voltage collapse problem can be contained by carrying out preventive control actions as an optimization problem in active or reactive power dispatch in normal conditions, in order to minimize the power losses and increase the reactive power reserve to meet any unexpected increasing in reactive power demand. In this thesis, a proposed procedure is presented to solve security constrained optimal power dispatch (SCOPD) problem under normal and emergency conditions using ant colony optimization (ACO) algorithm as modern optimization technique. The objective function is to minimize the non-linear generation cost function with the valve-point effects that appears in a rectified sinusoidal function introduce ripples in the heat-rate curves, by optimizing the control variables of the generators real power under equality and inequality constraints. Six performance indices (PIs) are proposed for efficient detecting the most weakness load buses which may be subjected to voltage collapse occurrence at normal and emergency conditions. The classification of the suggested ranking PIs are dependent on their severity by the voltage collapse. The suggested PIs are based on the following physical quantities; equivalent system impedance and the related load impedance at each load bus, equivalent system admittance and the related load admittance at each load bus, load bus voltages and loading levels at iii different loading buses. The results detect efficiently the most sever load buses which may be subjected to voltage collapse occurrence. The optimal reactive power dispatch (ORPD) problem are solved using modern optimization techniques such as fuzzy linear programming (FLP), genetic algorithm (GA), particle swarm optimization (PSO) and ant colony optimization (ACO) algorithm. The objective of the ORPD is to minimize the transmission line losses under control and dependent variable constraints using proposed sensitivity parameters of reactive power that dependent on a modification of Fast Decoupled Power Flow (FDPF) model. These techniques are compared with the conventional linear programming technique. Simulation results show the capability of the proposed ACO algorithm for the ORPD problem, especially with increasing the system size. This thesis presents an optimal preventive-corrective control actions using ant colony optimization (ACO) algorithm to mitigate the occurrence of voltage collapse in stressed power systems. The proposed multi-objective functions are: minimizing the transmission line losses as optimal reactive power dispatch (ORPD) problem, maximizing the preventive control actions by; minimizing the voltage deviation of load buses with respect to specified voltage (equals to one) and minimizing the reactive power generation at generation buses based on control variables under voltage collapse, control and dependent variable constraints. A comparison between the initial, reactive power dispatch problem and optimal preventive control actions is presented at different emergency conditions using load flow method. The comparative study indicates that, the initial and reactive power dispatch problem have some violation in control or dependent variables. Therefore, corrective control actions should be required in order to remove these violations. The results show the capability of the proposed ACO algorithm for the maximal preventive control actions to remove different emergency condition effects.