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A Graph Partition-Based Large-Scale Distribution Network Reconfiguration Method.

Yuanxia Sha1

  • 1Department of Mathematical Sciences, Daqing Normal University, Daqing 163712, Heilongjiang, China.

Computational Intelligence and Neuroscience
|March 24, 2022
PubMed
Summary

This study introduces a graph theory-based approach for large-scale distribution network reconstruction, significantly reducing network loss by 10.68% and improving robustness against local optimization.

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Area of Science:

  • Electrical Engineering
  • Computer Science
  • Graph Theory
  • Optimization Algorithms

Background:

  • Traditional distribution networks struggle with distributed power integration and exhibit excessive network losses.
  • Graph theory and partitioning algorithms offer novel approaches for complex network analysis and segmentation.
  • Existing methods lack efficiency in large-scale distribution network reconstruction and loss reduction.

Purpose of the Study:

  • To develop and evaluate a graph theory-based algorithm for large-scale distribution network reconstruction.
  • To improve distribution network performance and minimize network losses.
  • To address challenges in feeder-to-feeder reconstruction and local optimization.

Main Methods:

  • A multi-division model for distribution network construction and reconstruction was established.
  • A graph theory-based division algorithm, incorporating clustering characteristics, was proposed.
  • A JA-BE-JA optimization algorithm was developed for large-scale distribution network reconfiguration.

Main Results:

  • The proposed improved graph segmentation algorithm demonstrated strong robustness and avoided local optimization.
  • Network loss was reduced by 454.3 KW, representing a 10.68% optimization compared to the initial state.
  • The method effectively located and isolated distribution network faults using FTU for quick repair.

Conclusions:

  • The graph theory-based approach significantly enhances distribution network performance and reduces energy loss.
  • The developed algorithms offer a robust solution for large-scale distribution network reconstruction challenges.
  • This method provides a viable strategy for optimizing new energy integration and improving grid efficiency.