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Novel FGbSA: Fuzzy-Galaxy-based search algorithm for multi-objective reconfiguration of distribution systems

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Abstract

Reconfiguration according to different criteria is an important problem in distribution systems. This paper presents a new method for optimal multi-objective reconfiguration of distribution system based on the Galaxy-based Search Algorithm (GbSA). To avoid the convergence problem, the input and output data are normalized in the same range using fuzzy sets. The main objectives of the proposed algorithm have been considered as power loss reduction, voltage profile improvement and increase of the system load balancing. The proposed technique has been investigated using the IEEE 33-bus test system and a real distribution network i.e. Tai-Power 11.4-kV distribution system. The obtained results revealed the superiority of the proposed fuzzy-GbSA method in terms of accuracy compared to the GbSA and other intelligent search algorithms such as Genetic Algorithm (GA) or Particle Swarm Optimization (PSO). Furthermore, the proposed algorithm efficiently converged to the optimum solution compared to the other intelligent counterpart algorithms.

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Authors and Affiliations

  1. Department of Electrical Engineering, Khorramabad Branch, Islamic Azacl University, Khorramabad, Iran

    Hajar Bagheri Tolabi

  2. Faculty of Engineering, Lorestan University, Khorramabad, Iran

    Mahmoud Reza Shakarami

  3. Department of Computer Engineering, Shahr-e-Qods Branch, Islamic Azad University, Tehran, Iran

    Rahil Hosseini

  4. Faculty of Electrical Engineering, Universiti Teknologi Malaysia, Johor, Malaysia

    S. B. M. Ayob

Authors
  1. Hajar Bagheri Tolabi
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  2. Mahmoud Reza Shakarami
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  3. Rahil Hosseini
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  4. S. B. M. Ayob
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Correspondence to Hajar Bagheri Tolabi.

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Tolabi, H.B., Shakarami, M.R., Hosseini, R. et al. Novel FGbSA: Fuzzy-Galaxy-based search algorithm for multi-objective reconfiguration of distribution systems. Russ. Electr. Engin. 87, 588–595 (2016). https://doi.org/10.3103/S1068371216100072

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  • DOI: https://doi.org/10.3103/S1068371216100072

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