Novel Hybrid Fuzzy-Intelligent Water Drops Approach for Optimal Feeder Multi Objective Reconfiguration by Considering Multiple-Distributed Generation

Document Type : Research paper

Authors

1 Faculty of Engineering, Islamic Azad University, Khorramabad Branch, Khorramabad, Iran

2 2Department of Electrical and Computer Engineering, University of Memphis, Tennessee, United States

3 Department of Electrical Engineering, Delhi Technological University, Delhi-110042, India

Abstract

This paper presents a new hybrid method for optimal multi-objective reconfiguration in a distribution feeder in addition to determining the optimal size and location of multiple-Distributed Generation (DG). The purposes of this research are mitigation of losses, improving the voltage profile and equalizing the feeder load balancing in distribution systems. To reduce the search space, the improved analytical method has been employed to select the optimum candidate locations for multiple-DGs, and the intelligent water drops approach as a novel swarm intelligence based algorithm is used to simultaneously reconfigure and identify the optimal capacity for installation of DG units in the distribution network. In order to facilitate the algorithm for multi-objective search ability, the optimization problem is formulated for minimizing fuzzy performance indices. The proposed method is validated using the Tai-Power 11.4-kV distribution system as a real distribution network. The obtained results proved that this combined technique is more accurate and has the lowest fitness value as compared with other intelligent search algorithms. Also, the obtained results leadto the conclusion that multi-objective simultaneous placement of DGs along with reconfiguration can be more beneficial than separate single-objective optimization.

Keywords

Journal of Operation and Automation in Power Engineering
Volume 2, Issue 2 - Serial Number 2
December 2014
Pages 91-102

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History

  • Receive Date: 24 December 2014
  • Revise Date: 25 December 2014
  • Accept Date: 25 December 2014
  • First Publish Date: 25 December 2014

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