Neuware -The management of power systems has become more difficult than earlier because power systems are operated closer to security limits and environmental constraints restrict. The loading of a transmission network can be increased by maintaining proper voltage profile through injecting appropriate reactive power into the system. There had been many attempts to solve for these multi objective solutions in the optimal placement of reactive power in the power system. Generally, the conventional methodologies typically often get stuck at the local optimum rather than at the global optimum. Due to Genetic Algorithms (GAs) are stochastic searching algorithms, it makes the searching process jumps randomly from point to point, thus allowing escape from the local optimum, in which other conventional optimization algorithms might land, and it searches for many sub-optimum points in parallel. This book demonstrates an evolutionary computation algorithm for enhancing voltage stability. GAs will find out the most vulnerable bus in the power system, where the Static VAR Compensator (SVC) is needed to be installed at that bus as well as considering economic issues such as installation cost of SVC.Books on Demand GmbH, �berseering 33, 22297 Hamburg 96 pp. Englisch.

Bestandsnummer des Verk�ufers 9783843378956

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Titel: OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR USING GENETIC ALGORITHMS
Verlag: LAP LAMBERT Academic Publishing Nov 2010
Erscheinungsjahr: 2010
Sprache: Englisch
ISBN-10: 3843378959
ISBN-13: 9783843378956
Einband: Taschenbuch
Zustand: Neu

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The management of power systems has become more difficult than earlier because power systems are operated closer to security limits and environmental constraints restrict. The loading of a transmission network can be increased by maintaining proper voltage profile through injecting appropriate reactive power into the system. There had been many attempts to solve for these multi objective solutions in the optimal placement of reactive power in the power system. Generally, the conventional methodologies typically often get stuck at the local optimum rather than at the global optimum. Due to Genetic Algorithms (GAs) are stochastic searching algorithms, it makes the searching process jumps randomly from point to point, thus allowing escape from the local optimum, in which other conventional optimization algorithms might land, and it searches for many sub-optimum points in parallel. This book demonstrates an evolutionary computation algorithm for enhancing voltage stability. GAs will find out the most vulnerable bus in the power system, where the Static VAR Compensator (SVC) is needed to be installed at that bus as well as considering economic issues such as installation cost of SVC.

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