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Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms

Author

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  • Alex Guamán

    (Postgraduate Department, Universidad Politécnica Salesiana, Quito 170525, Ecuador
    These authors contributed equally to this work.)

  • Alex Valenzuela

    (Postgraduate Department, Smart Grid Research Group (GIREI), Universidad Politécnica Salesiana, Quito 170525, Ecuador
    These authors contributed equally to this work.)

Abstract

The distribution network is the most exposed part of the electrical power system relative to different abnormal events; therefore, it reports the highest occurrence rates in terms of electrical and mechanical failures. The present project describes a strategy for restoring faulty areas after the occurrence of an abnormal event causing an outage; consequently, the proposed algorithm is not only focused on the maximization of the connected loads but also deals with the minimization of the switching operations by considering technical operational constraints. The remarked study has two stages: The first one finds an initial set of tie-line candidates using the spanning tree technique, while the second stage applies a genetic algorithm to determine the optimal solution considering all the constraints. Three cases studies have been used to test the proposed algorithm, then the simulation and results of IEEE 13, 37 and 94 node feeders depict the effectiveness of the restoration strategy.

Suggested Citation

  • Alex Guamán & Alex Valenzuela, 2021. "Distribution Network Reconfiguration Applied to Multiple Faulty Branches Based on Spanning Tree and Genetic Algorithms," Energies, MDPI, vol. 14(20), pages 1-16, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6699-:d:657049
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    References listed on IDEAS

    as
    1. Alex Valenzuela & Iván Montalvo & Esteban Inga, 2019. "A Decision-Making Tool for Electric Distribution Network Planning Based on Heuristics and Georeferenced Data," Energies, MDPI, vol. 12(21), pages 1-18, October.
    2. Alex Valenzuela & Esteban Inga & Silvio Simani, 2019. "Planning of a Resilient Underground Distribution Network Using Georeferenced Data," Energies, MDPI, vol. 12(4), pages 1-20, February.
    3. Alex Valenzuela & Silvio Simani & Esteban Inga, 2021. "Automatic Overcurrent Protection Coordination after Distribution Network Reconfiguration Based on Peer-To-Peer Communication," Energies, MDPI, vol. 14(11), pages 1-22, June.
    4. Badran, Ola & Mekhilef, Saad & Mokhlis, Hazlie & Dahalan, Wardiah, 2017. "Optimal reconfiguration of distribution system connected with distributed generations: A review of different methodologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 854-867.
    5. Ding, Tao & Lin, Yanling & Bie, Zhaohong & Chen, Chen, 2017. "A resilient microgrid formation strategy for load restoration considering master-slave distributed generators and topology reconfiguration," Applied Energy, Elsevier, vol. 199(C), pages 205-216.
    6. Lin, Yanling & Bie, Zhaohong, 2018. "Tri-level optimal hardening plan for a resilient distribution system considering reconfiguration and DG islanding," Applied Energy, Elsevier, vol. 210(C), pages 1266-1279.
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    Cited by:

    1. Matheus Diniz Gonçalves-Leite & Edgar Manuel Carreño-Franco & Jesús M. López-Lezama, 2023. "Impact of Distributed Generation on the Effectiveness of Electric Distribution System Reconfiguration," Energies, MDPI, vol. 16(17), pages 1-20, August.
    2. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2022. "A Mixed-Integer Linear Programming Model for the Simultaneous Optimal Distribution Network Reconfiguration and Optimal Placement of Distributed Generation," Energies, MDPI, vol. 15(9), pages 1-26, April.
    3. Luis A. Gallego Pareja & Jesús M. López-Lezama & Oscar Gómez Carmona, 2023. "Optimal Integration of Distribution Network Reconfiguration and Conductor Selection in Power Distribution Systems via MILP," Energies, MDPI, vol. 16(19), pages 1-25, October.

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