| The objective of this thesis is to provide an efficient and accurate corrective solution to a system that is on verge of voltage collapse. This thesis describes, in detail, the development of an optimization scheme that aims to alleviate power system instability and voltage collapse condition based on the principles of an evolutionary approach called Genetic Algorithm. The state of a system is determined using a voltage stability identifier termed Collapse Proximity Index (CPI ) and the critical loading condition is identified. Applying principles of Genetic Algorithm, the critical system is brought back to a stable operating region. The sequential procedure and application of this scheme is primarily discussed in this thesis.;The thesis is structured to include theoretical discussion of the Collapse Proximity Index, development of a Genetic Algorithm - based solution to the voltage collapse problem and its simulated implementation on a test system, along with result analysis and suggestions for future development. Conclusions are drawn based on the efficiency of the application in maintaining system stability. |
