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SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm

SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm Abstract Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Archives of Electrical Engineering de Gruyter

SVC placement for voltage constrained loss minimization using self-adaptive Firefly algorithm

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References (21)

Publisher
de Gruyter
Copyright
Copyright © 2013 by the
ISSN
0004-0746
DOI
10.2478/aee-2013-0051
Publisher site
See Article on Publisher Site

Abstract

Abstract Static Var Compensator (SVC) is a popular FACTS device for providing reactive power support in power systems and its placement representing the location and size has significant influence on network loss, while keeping the voltage magnitudes within the acceptable range. This paper presents a Firefly algorithm based optimization strategy for placement of SVC in power systems with a view of minimizing the transmission loss besides keeping the voltage magnitude within the acceptable range. The method uses a self-adaptive scheme for tuning the parameters in the Firefly algorithm. The strategy is tested on three IEEE test systems and their results are presented to demonstrate its effectiveness.

Journal

Archives of Electrical Engineeringde Gruyter

Published: Dec 1, 2013

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