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Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms

Author

Listed:
  • Rade Čađenović

    (Department of Power Engineering, University of Split-FESB, 21000 Split, Croatia)

  • Damir Jakus

    (Department of Power Engineering, University of Split-FESB, 21000 Split, Croatia)

  • Petar Sarajčev

    (Department of Power Engineering, University of Split-FESB, 21000 Split, Croatia)

  • Josip Vasilj

    (Department of Power Engineering, University of Split-FESB, 21000 Split, Croatia)

Abstract

This paper presents novel cycle-break (spanning tree generation) algorithms which can be used to find the optimal distribution network topology. These algorithms (adjacency matrix/top-down/bottom-up cycle break) represent a novel way of obtaining radial network topology by cycle regrouping using adjacency matrix or elementary cycle information. Proposed methods assure connected radial network topology and can be used in combination with genetic algorithms to obtain optimal distribution network structure under minimum active power loss or network loading index framework. The cycle-break algorithms are used in initial population generation, crossover and mutation process to enhance the performance of the genetic algorithms in terms of convergence rate. These modifications make the proposed approach suitable for the use on realistic distribution networks without concern of its complexity. The algorithms are tested on a several standard test networks and the results are compared with the other existing approaches.

Suggested Citation

  • Rade Čađenović & Damir Jakus & Petar Sarajčev & Josip Vasilj, 2018. "Optimal Distribution Network Reconfiguration through Integration of Cycle-Break and Genetic Algorithms," Energies, MDPI, vol. 11(5), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1278-:d:146784
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    References listed on IDEAS

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    1. Ke-yan Liu & Wanxing Sheng & Yongmei Liu & Xiaoli Meng, 2017. "A Network Reconfiguration Method Considering Data Uncertainties in Smart Distribution Networks," Energies, MDPI, vol. 10(5), pages 1-17, May.
    2. Bogdan Tomoiagă & Mircea Chindriş & Andreas Sumper & Antoni Sudria-Andreu & Roberto Villafafila-Robles, 2013. "Pareto Optimal Reconfiguration of Power Distribution Systems Using a Genetic Algorithm Based on NSGA-II," Energies, MDPI, vol. 6(3), pages 1-17, March.
    3. Firas M. F. Flaih & Xiangning Lin & Mohammed Kdair Abd & Samir M. Dawoud & Zhengtian Li & Owolabi Sunday Adio, 2017. "A New Method for Distribution Network Reconfiguration Analysis under Different Load Demands," Energies, MDPI, vol. 10(4), pages 1-19, April.
    4. Wei-Tzer Huang & Tsai-Hsiang Chen & Hong-Ting Chen & Jhih-Siang Yang & Kuo-Lung Lian & Yung-Ruei Chang & Yih-Der Lee & Yuan-Hsiang Ho, 2015. "A Two-stage Optimal Network Reconfiguration Approach for Minimizing Energy Loss of Distribution Networks Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 8(12), pages 1-17, December.
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    Cited by:

    1. Filipe F. C. Silva & Pedro M. S. Carvalho & Luís A. F. M. Ferreira, 2021. "Improving PV Resilience by Dynamic Reconfiguration in Distribution Grids: Problem Complexity and Computation Requirements," Energies, MDPI, vol. 14(4), pages 1-15, February.
    2. 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.
    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. Min Zhu & Saber Arabi Nowdeh & Aspassia Daskalopulu, 2023. "An Improved Human-Inspired Algorithm for Distribution Network Stochastic Reconfiguration Using a Multi-Objective Intelligent Framework and Unscented Transformation," Mathematics, MDPI, vol. 11(17), pages 1-23, August.

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