Network reconfiguration of unbalanced distribution networks using fuzzy-firefly algorithm

[Display omitted] This reconfigured network obtained by the proposed Fuzzy-Firefly algorithm gives the following compared to other techniques: (i)Lower Real Power Loss(ii)Lower Reactive Power Loss(iii)Improvement of voltage profiles(iv)Lower energy costs •A new load flow method has been suggested fo...

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Veröffentlicht in:Applied soft computing Jg. 49; S. 868 - 886
Hauptverfasser: Kaur, Manvir, Ghosh, Smarajit
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.12.2016
Schlagworte:
Firefly algorithm
Fuzzy logic
Network reconfiguration
Power loss reduction
Unbalanced distribution systems
Fuzzy logic
Unbalanced distribution systems
Power loss reduction
Network reconfiguration
Firefly algorithm
ISSN:1568-4946, 1872-9681
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Zusammenfassung:[Display omitted] This reconfigured network obtained by the proposed Fuzzy-Firefly algorithm gives the following compared to other techniques: (i)Lower Real Power Loss(ii)Lower Reactive Power Loss(iii)Improvement of voltage profiles(iv)Lower energy costs •A new load flow method has been suggested for unbalanced distribution networks.•Improvement in terms of loss reductions, minimum voltages, energy cost, CPU time, iteration number.•The outcomes obtained by PM is better than that of other existing approaches. This paper addresses a method to optimize the unbalanced distribution networks (UDNs) for keeping up the voltage profile with respect to the consequence of solving the multi-objective reconfiguration using the Firefly algorithm in a fuzzy domain with a load flow method proposed in this paper. The objectives to be minimized are the total network power losses, the deviation of bus voltage and load equalizing in the feeders with network reconfiguration. Every goal is moved into the fuzzy domain utilizing its membership function and fuzzified independently. The proposed method for network reconfiguration has been implemented in 25-node and 19-node UDNs. The outcomes obtained by the suggested method of these two unbalanced networks have been compared with that of obtained by Genetic algorithm (GA), ABC algorithm, PSO algorithm and GA-PSO algorithm using the same objective function. The juxtaposition of the proposed method with the available methods is also presented.
ISSN:1568-4946
1872-9681
DOI:10.1016/j.asoc.2016.09.019