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Erschienen in: Neural Computing and Applications 9/2018

18.02.2017 | Original Article

An efficient methodology for optimal reconfiguration of electric distribution network considering reliability indices via binary particle swarm gravity search algorithm

verfasst von: Ahmed Fathy, Mahdi El-Arini, Osama El-Baksawy

Erschienen in: Neural Computing and Applications | Ausgabe 9/2018

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Abstract

One important aspect that should be achieved during the operation of the distribution network is minimizing the total active loss. This objective can be achieved by network reconfiguration in which switching events such as closing tie switches or opening sectionalizing switches are efficiently determined. This paper presents a reliable meta-heuristic algorithm for optimal reconfiguration of the distribution network which is binary particle swarm optimization gravity search algorithm (BPSOGSA). The methodology is applied on four test systems: 16-bus system, 33-bus system, 69-bus system and 119-bus system. Reliability indices, system average interruption frequency, system average interruption duration and energy not supplied, are incorporated to check the validity of the network after reconfiguration process. Comparison with other reported previous methods is performed; the power loss is reduced by 9.3242% in 16-bus system, for 33-bus system; the power loss is reduced by 31.46%. In case of 69-bus system, the power loss is reduced by 56.1761%, while for 119-bus, the power loss is reduced by 33.7216%. Additionally, the performance of the proposed BPSOGSA is the best one compared with the others for all studied cases. The obtained results prove the reliability of the proposed methodology.

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Metadaten
Titel
An efficient methodology for optimal reconfiguration of electric distribution network considering reliability indices via binary particle swarm gravity search algorithm
verfasst von
Ahmed Fathy
Mahdi El-Arini
Osama El-Baksawy
Publikationsdatum
18.02.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 9/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2877-z

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