dc.creator | Sanches, Danilo Sipoli | |
dc.creator | Junior, Joao Bosco Augusto London | |
dc.creator | Delbem, Alexandre Cláudio Botazzo | |
dc.date.accessioned | 2014-04-22T19:10:52Z | |
dc.date.accessioned | 2018-07-04T16:45:16Z | |
dc.date.available | 2014-04-22T19:10:52Z | |
dc.date.available | 2018-07-04T16:45:16Z | |
dc.date.created | 2014-04-22T19:10:52Z | |
dc.date.issued | 2014-05 | |
dc.identifier | Electric Power Systems Research, Amsterdam, v. 110, p. 144-153, May 2014 | |
dc.identifier | http://www.producao.usp.br/handle/BDPI/44571 | |
dc.identifier | 10.1016/j.epsr.2014.01.017 | |
dc.identifier | http://www.sciencedirect.com/science/article/pii/S0378779614000212# | |
dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1639906 | |
dc.description.abstract | Network reconfiguration for service restoration (SR) in distribution systems is a complex optimization
problem. For large-scale distribution systems, it is computationally hard to find adequate SR plans in
real time since the problem is combinatorial and non-linear, involving several constraints and objectives.
Two Multi-Objective Evolutionary Algorithms that use Node-Depth Encoding (NDE) have proved able to
efficiently generate adequate SR plans for large distribution systems: (i) one of them is the hybridization
of the Non-Dominated Sorting Genetic Algorithm-II (NSGA-II) with NDE, named NSGA-N; (ii) the other is
a Multi-Objective Evolutionary Algorithm based on subpopulation tables that uses NDE, named MEAN.
Further challenges are faced now, i.e. the design of SR plans for larger systems as good as those for
relatively smaller ones and for multiple faults as good as those for one fault (single fault). In order to tackle
both challenges, this paper proposes a method that results from the combination of NSGA-N, MEAN and
a new heuristic. Such a heuristic focuses on the application of NDE operators to alarming network zones
according to technical constraints. The method generates similar quality SR plans in distribution systems
of significantly different sizes (from 3860 to 30,880 buses). Moreover, the number of switching operations
required to implement the SR plans generated by the proposed method increases in a moderate way with
the number of faults. | |
dc.language | eng | |
dc.publisher | Elsevier | |
dc.publisher | Amsterdam | |
dc.relation | Electric Power Systems Research | |
dc.rights | Copyright Elsevier | |
dc.rights | restrictedAccess | |
dc.subject | Large-scale distribution system | |
dc.subject | Service restoration | |
dc.subject | Multiple faults | |
dc.subject | Node-Depth Encoding | |
dc.subject | Multi-Objective Evolutionary Algorithms | |
dc.title | Multi-objective evolutionary algorithm for single and multiple fault service restoration in large-scale distribution systems | |
dc.type | Artículos de revistas | |