• DocumentCode
    1862538
  • Title

    Research on Time-Sharing ZIP Load Modeling Based on Linear BP Network

  • Author

    Kankan Wang ; Haixin Huang ; Chuanzhi Zang

  • Author_Institution
    Shenyang Inst. of Autom., Shenyang, China
  • Volume
    1
  • fYear
    2013
  • fDate
    26-27 Aug. 2013
  • Firstpage
    37
  • Lastpage
    41
  • Abstract
    Classic load model is conservative and inaccurate by only considering constant power load model, but actual situation can be described more precisely by using ZIP load model. The different load rate Based on ZIP load model has a serious impact on static voltage stability. Due to the time-varying load rate, time-sharing modeling, which is identified for every 20 minutes in a day, can describe the variation of different load rate for the whole day more accurate than all-day modeling, which is identified once by using all data in a day. In this paper, all-day ZIP load model and time-sharing ZIP load model are established respectively in order to justify the effectiveness of time-sharing ZIP load modeling. By using Linear-BP network, the model parameters can be identified more easily and precisely. A case study is performed by using data from Dalian University of Technology. The results verify that this network is more suitable to ZIP model parameters identification.
  • Keywords
    backpropagation; load flow; neural nets; parameter estimation; power engineering computing; Dalian University of Technology; ZIP model parameter identification; all-day ZIP load model; constant power load model; linear BP network; static voltage stability; time-sharing ZIP load modeling; time-varying load rate; Computational modeling; Load modeling; Mathematical model; Neurons; Power system dynamics; Reactive power; Linear-BP network; ZIP load model; all-day modeling; parameters identification; time-sharing modeling;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2013 5th International Conference on
  • Conference_Location
    Hangzhou
  • Print_ISBN
    978-0-7695-5011-4
  • Type

    conf

  • DOI
    10.1109/IHMSC.2013.16
  • Filename
    6643828