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Research Article

Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP

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  • @ARTICLE{10.4108/ew.7242,
        author={Xiaokun Han and Dongming Jia and Xiang Dong and Dongwei Chen},
        title={Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP},
        journal={EAI Endorsed Transactions on Energy Web},
        volume={11},
        number={1},
        publisher={EAI},
        journal_a={EW},
        year={2024},
        month={9},
        keywords={Backpropagation neural network, Particle swarm algorithm, Dynamic coefficients, Acceleration constants, Distribution network, Faults},
        doi={10.4108/ew.7242}
    }
    
  • Xiaokun Han
    Dongming Jia
    Xiang Dong
    Dongwei Chen
    Year: 2024
    Fault Diagnosis of Distributed Energy Distribution Network Based on PSO-BP
    EW
    EAI
    DOI: 10.4108/ew.7242
Xiaokun Han1,*, Dongming Jia1, Xiang Dong1, Dongwei Chen1
  • 1: State Grid Beijing Electric Power Maintenance Branch
*Contact email: HhxKunkun@163.com

Abstract

With the increasing scale of distribution network at distribution time, its complexity grows geometrically, and its fault diagnosis becomes more and more difficult. Aiming at the slow convergence and low accuracy of traditional backpropagation neural network in dealing with single-phase ground faults, the study proposes a backpropagation neural network based on improved particle swarm optimization. The model optimizes the weights and acceleration constants of the particle swarm algorithm by introducing dynamic coefficients to enhance its global and local optimization seeking ability. It is also applied in optimizing the parameters of backpropagation neural network and constructing the routing model and ranging model for fault diagnosis about distributed energy distribution network. The simulation results revealed that the maximum absolute error of the improved method is 0.08. While the maximum absolute errors of the traditional backpropagation neural network and the particle swarm optimized backpropagation neural network were 0.65 and 0.10, respectively. The fluctuation of the relative errors of the research method was small under different ranges of measurements. At 8.0 km, the minimum relative error was 0.39% and the maximum relative error was 2.81%. The results show that the improved method proposed in the study significantly improves the accuracy and stability of fault diagnosis and localization in distribution networks and is applicable to complex distribution network environments. The method has high training efficiency and fault detection capability and provides an effective tool for distribution network fault management.

Keywords
Backpropagation neural network, Particle swarm algorithm, Dynamic coefficients, Acceleration constants, Distribution network, Faults
Received
2024-07-11
Accepted
2024-09-04
Published
2024-09-10
Publisher
EAI
http://dx.doi.org/10.4108/ew.7242

Copyright © 2024 X. Han, licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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