• DocumentCode
    2613780
  • Title

    Optimization the management of Electrical power transmission network by neural networks to decrease the Corona loss, based on ICT

  • Author

    Zandesh, Gholam Reza ; Ebrahimpour, Reza ; Bakherad, Behnaz ; Adib, Negin Karimi

  • Author_Institution
    Islamic Azad Univ., Tehran
  • fYear
    2005
  • fDate
    2005
  • Firstpage
    1
  • Lastpage
    6
  • Abstract
    Most rate of electrical power network´s corona loss occurs in transmission part. In this paper a new method for estimating corona loss with the intention of optimal selection of transmission lines in local or international dispatching centers with high response rate, is presented. In this method, suggested algorithm by using artificial neural network with receiving information from sensors which placed on high voltage substation and transmission by SCADA network, is able to calculate Corona for estimating the corona loss. Designed algorithm based on this suggested model in Iran´s 400 kv power network and considering 6 climatic areas in the country with 432 exploited samples for training neural network, is implemented. Results of algorithm indicate the generalization possibility and high response rate of the suggested model
  • Keywords
    SCADA systems; corona; neural nets; power engineering computing; power system management; power transmission lines; SCADA network; artificial neural network; corona loss; electrical power transmission network; high voltage substation; optimization; sensors; transmission lines; Artificial neural networks; Corona; Dispatching; Energy management; Neural networks; Power transmission; Power transmission lines; Propagation losses; Substations; Voltage; ArtificialNeuralNetworks; Corona; Dispatching Center; ICT; Multilayer Perceptron; Transmission Line Network;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Transmission and Distribution Conference and Exhibition: Asia and Pacific, 2005 IEEE/PES
  • Conference_Location
    Dalian
  • Print_ISBN
    0-7803-9114-4
  • Type

    conf

  • DOI
    10.1109/TDC.2005.1546933
  • Filename
    1546933