• DocumentCode
    2058673
  • Title

    Low-voltage area prediction model research based on self-organizing competitive neural network

  • Author

    Zhang Ying ; Zhang Shu-xin ; Ru Wei-kang ; Wu Cai-biao ; Chen Yong

  • Author_Institution
    Qingpu Power Supply Co., SMEPC, Shanghai, China
  • fYear
    2012
  • fDate
    10-14 Sept. 2012
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    The distribution network voltage level is directly related to residents´ normal use of electricity, in order to correctly predict the low-voltage distribution network voltage quality, to take timely and effective means to prevent low-voltage phenomenon, the article collects nine indicators to reflect the characteristics of low-voltage distribution network voltage quality which are mainline diameter, mainline type, branch line diameter, branch line type, power supply radius, distribution transformer capacity -load ratio, the three-phase load unbalance rate, single-phase-home number, reactive power compensation rate. Then the article establishes a self-organizing competitive neural network model to automatic cluster the samples into three kinds which are normal, existing low voltage risk and existing severe low voltage risk. Using the known practical result to compare with the calculation results will indicate that the network model has high accuracy and feasibility.
  • Keywords
    distribution networks; neural nets; reactive power control; branch line diameter; branch line type; distribution network voltage level; distribution transformer capacity-load ratio; low-voltage area prediction model; low-voltage distribution network voltage quality; mainline diameter; mainline type; power supply radius; reactive power compensation rate; self-organizing competitive neural network; single-phase-home number; three-phase load unbalance rate; Voltage quality; automatic clustering; low-voltage of distribution network; self-organizing competitive neural network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Electricity Distribution (CICED), 2012 China International Conference on
  • Conference_Location
    Shanghai
  • ISSN
    2161-7481
  • Print_ISBN
    978-1-4673-6065-4
  • Electronic_ISBN
    2161-7481
  • Type

    conf

  • DOI
    10.1109/CICED.2012.6508513
  • Filename
    6508513