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Sinopsis
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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Reseña del editor
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.
Biografía del autor
Ms. Wong Yan Chiew, is a lecturer in Universiti Teknikal Malaysia Melaka, Malaysia. Currently she is working toward the Ph.D degree from the University of Edinburgh, UK. She leads and involves in a number of research projects in the stated fields and has produced numerous local and international publications.
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OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR USING GENETIC ALGORITHMS
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LAP LAMBERT Academic Publishing Nov 2010, 2010
ISBN 10: 3843378959
ISBN 13: 9783843378956
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Alemania
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 96 pp. Englisch. Nº de ref. del artículo: 9783843378956
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OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR USING GENETIC ALGORITHMS
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843378959
ISBN 13: 9783843378956
Nuevo
Tapa blanda
Librería: moluna, Greven, Alemania
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Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Wong Yan ChiewMs. Wong Yan Chiew, is a lecturer in Universiti Teknikal Malaysia Melaka, Malaysia. Currently she is working toward the Ph.D degree from the University of Edinburgh, UK. She leads and involves in a number of research pr. Nº de ref. del artículo: 5467759
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OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR USING GENETIC ALGORITHMS
Publicado por
LAP LAMBERT Academic Publishing Nov 2010, 2010
ISBN 10: 3843378959
ISBN 13: 9783843378956
Nuevo
Taschenbuch
Librería: buchversandmimpf2000, Emtmannsberg, BAYE, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. 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, Überseering 33, 22297 Hamburg 96 pp. Englisch. Nº de ref. del artículo: 9783843378956
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OPTIMAL PLACEMENT OF STATIC VAR COMPENSATOR USING GENETIC ALGORITHMS : Practical Approach
Publicado por
LAP LAMBERT Academic Publishing, 2010
ISBN 10: 3843378959
ISBN 13: 9783843378956
Nuevo
Taschenbuch
Librería: AHA-BUCH GmbH, Einbeck, Alemania
Calificación del vendedor: 5 de 5 estrellas
Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783843378956
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