الفهرس 
Some Definitions And ConceptsOnly 14 pages are availabe for public view
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Abstract
This thesis deals with one of the most important problems facing any power system nowadays. This piobiem is the voltage stability of the power system. Many large interconnected power systems are increasingly experiencing abnormally low voltages which may cause?-omplete voltage collapse. These voltage problems are associated with the increased loading of transmission lines, insufficient local reactive supply, and the shipping of power across long distances or due to severe system contingencies. Voltage stability v. ill be first studied. The definitions, mechanisms, scenarios for both steady-state (static) and transient (dynamic) stability are demonstrated. The voltage stability may be analyzed by either static or dynamic approaches. Static approaches means that voltaic instability can be analyzed by using algebraic equations such as load flow equation-- On the other hand, dynamic approaches are used when dealing with fast acting loads as induction motors or HVDC lines using complete differential algebraic model t-i the system. In this thesis, we focus on the steady-state voltage stability issues im-hiding detection and analysis methods based on the algebraic modeling of tin s\ stein.
Finally, the counieimeasures or different controls used in the enhancement of voltage stability are discussed One of the most effective tools in the enhancement of voltage stability is the reactive power compensation. A certain type of optimal power flow algorithms basic alls concerned with reactive power is usually used to determine the optimum control scheme to improve the voltage stability. This formulation will also be presented. Thr problem is formulated in the form of an optimization problem. To solve this optimi/ation problem, we have adopted the newly developed Particle Swarm Optimization technique. The Particle Swarm Optimization technique belongs to the family of modern evolutionary optimization techniques like the Genetic Algorithm or the; Simulated Annealing techniques. These techniques basically depend on the theory of evolution in different systems in the nature. Therefore, a quick review on the classical optimization techniques is demonstrated then we focus on the Particle Swarm Optimization its theory, modeling, and applications.