Result: Multi-Objective Evolutionary Algorithm for single and multiple fault service restoration in large-scale distribution systems

Title:
Multi-Objective Evolutionary Algorithm for single and multiple fault service restoration in large-scale distribution systems
Authors:
SIPOLI SANCHES, Danilo, LONDON, João Bosco A, DELBEM, Alexandre Cláudio B
Source:
Electric power systems research. 110:144-153
Publisher Information:
Amsterdam: Elsevier, 2014.
Publication Year:
2014
Physical Description:
print, 23 ref
Original Material:
INIST-CNRS
Subject Terms:
Electrical engineering, Electrotechnique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Electrotechnique. Electroenergetique, Electrical engineering. Electrical power engineering, Electroénergétique, Electrical power engineering, Réseaux et lignes électriques, Power networks and lines, Divers, Miscellaneous, Algorithme génétique, Genetic algorithm, Algoritmo genético, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Architecture reconfigurable, Reconfigurable architectures, Codage, Coding, Codificación, Commutation, Switching, Conmutación, Essai non destructif, Non destructive test, Ensayo no destructivo, Implémentation, Implementation, Implementación, Méthode combinatoire, Combinatorial method, Método combinatorio, Méthode heuristique, Heuristic method, Método heurístico, Méthode optimisation, Optimization method, Método optimización, Programmation multiobjectif, Multiobjective programming, Programación multiobjetivo, Restauration service, Service restoration, Restablecimiento servicio, Réseau distribution, Distribution network, Red distribución, Réseau ordinateur, Computer network, Red informática, Réseau électrique, Electrical network, Red eléctrica, Service télécommunication, Telecommunication services, Système grande taille, Large scale system, Sistema gran escala, Large-scale distribution system, Multi-Objective Evolutionary Algorithms, Multiple faults, Node-Depth Encoding
Document Type:
Academic journal Article
File Description:
text
Language:
English
Author Affiliations:
Federal Technological University of Parana, Av. Alberto Carazzai, 1640, CEP 86300-000, Cornelio Procopio, PR, Brazil
Sao Carlos School of Engineering, University of São Paulo, Av. Trabalhador Sao-carlense, 400, Arnold Schimidt, CEP 13566-590, Sao Carlos, SP, Brazil
Institute of Mathematical and Computing Sciences, University of São Paulo, Av. Trabalhador Sao-carlense, 400, Arnold Schimidt, CEP 13566-590, Sao Carlos, SP, Brazil
ISSN:
0378-7796
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28360699
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Electrical engineering. Electroenergetics
Accession Number:
edscal.28360699
Database:
PASCAL Archive

Further Information

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.