• DocumentCode
    1774144
  • Title

    Fault location in distribution network with distributed generation based on neural network

  • Author

    Ge Liang ; Peng Liyuan ; Liu Ruihuan ; Zhou Fen ; Wang Xin

  • Author_Institution
    Beijing Sifang Autom. Co., Ltd., Beijing, China
  • fYear
    2014
  • fDate
    23-26 Sept. 2014
  • Firstpage
    209
  • Lastpage
    212
  • Abstract
    In this paper, the fault location problem of the distribution network with distributed generations(DGs) is studied. A fault identification and location method based on multi-stage neural network is proposed. Particle Swarm Optimization (PSO) algorithm is employed to optimize the network structure. The model and algorithm realize the fault location accurately. And when the input variables increases, When the input variables increases, its size is much reduced through gradual decomposition of the network compared to traditional the single-stage network. Finally, a typical distribution network with distributed generation is established in the Digsilent environment. The sample data are obtained through simulation experiment. The neural network model is established in Matlab to get the algorithm results. The experiment verified the correctness and effectiveness of the model and algorithm.
  • Keywords
    distributed power generation; fault location; neural nets; particle swarm optimisation; power distribution faults; power engineering computing; Digsilent environment; Matlab; PSO algorithm; distributed generation; distribution network; fault location method; fault location problem; gradual decomposition; multistage neural network; neural network model; particle swarm optimization; single-stage network; Abstracts; Artificial neural networks; Fault location; Distributed Generation (DG); Fault Location; Multi-Stage Neural Network; Particle Swarm Optimization (PSO);
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electricity Distribution (CICED), 2014 China International Conference on
  • Conference_Location
    Shenzhen
  • Type

    conf

  • DOI
    10.1109/CICED.2014.6991695
  • Filename
    6991695