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

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Abstract

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.

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Acknowledgments

The authors gratefully acknowledge INESC TEC, Porto, Portugal and CNPq/Brazil and CAPES/Brazil for supporting this research.

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Authors and Affiliations

  1. Electrical Engineering Department, Unesp, Univ Estadual Paulista, Av. Brasil 56, Ilha Solteira, SP, 15385-000, Brazil

    Simone S. F. Souza & Ruben Romero

  2. INESC TEC and University of Porto, Av. Dr. Roberto Frias 378, Porto, 4200-465, Portugal

    Jorge Pereira & João T. Saraiva

Authors
  1. Simone S. F. Souza
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  2. Ruben Romero
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  3. Jorge Pereira
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  4. João T. Saraiva
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Correspondence to Simone S. F. Souza.

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Souza, S.S.F., Romero, R., Pereira, J. et al. Reconfiguration of Radial Distribution Systems with Variable Demands Using the Clonal Selection Algorithm and the Specialized Genetic Algorithm of Chu–Beasley. J Control Autom Electr Syst 27, 689–701 (2016). https://doi.org/10.1007/s40313-016-0268-9

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  • DOI: https://doi.org/10.1007/s40313-016-0268-9

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