Reconfiguration of Radial Distribution Systems with Variable Demands Using the Clonal Selection Algorithm and the Specialized Genetic Algorithm of Chu–Beasley

This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproduc...

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Bibliographic Details
Published in:Journal of control, automation & electrical systems automation & electrical systems, 2016-12, Vol.27 (6), p.689-701
Main Authors: Souza, Simone S. F., Romero, Ruben, Pereira, Jorge, Saraiva, João T.
Format: Article
Language:English
Subjects:
Algorithms
Combinatorial analysis
Control
Control and Systems Theory
Electrical Engineering
Engineering
Genetic algorithms
Immune system
Mechatronics
Nodes
Optimization
Optimization techniques
Radial distribution
Reconfiguration
Robotics
Robotics and Automation
Topology
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Summary:This paper presents two new approaches to solve the reconfiguration problem of electrical distribution systems (EDSs) with variable demands, using the CLONALG and the SGACB algorithms. The CLONALG is a combinatorial optimization technique inspired by biological immune systems, which aims at reproducing the main properties and functions of the system. The SGACB is an optimization algorithm inspired by natural selection and the evolution of species. The reconfiguration problem with variable demands is a complex combinatorial problem that aims at identifying the best radial topology for an EDS, while satisfying all technical constraints at every demand level and minimizing the cost of energy losses in a given operation period. Both algorithms were implemented in C++ and test systems with 33, 84, and 136 nodes, as well as a real system with 417 nodes, in order to validate the proposed methods. The obtained results were compared with results available in the literature in order to verify the efficiency of the proposed approaches.
ISSN:2195-3880
2195-3899
DOI:10.1007/s40313-016-0268-9