• DocumentCode
    3363077
  • Title

    Voltage Stability Assessment Based on BP Neural Network

  • Author

    Han, Xiaoqing ; Zheng, Zhijing ; Tian, Nannan ; Hou, Yuanyuan

  • Author_Institution
    Coll. of Electr. & Power Eng., Taiyuan Univ. of Technol., Taiyuan
  • fYear
    2009
  • fDate
    27-31 March 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    An assessment approach on power system voltage stability is provided using Back Propagation (BP) Neural Network, which takes the Voltage Collapse Proximity Indicator (VCPI) as assessment index. The key feature of the method is to establish static and dynamic assessment models on voltage stability. The training results of the static models based on load flow calculation can reflect the nonlinear mapping relationship correctly between power flows and voltages on load bus with given load increasing mode; Based on integrated load model, the dynamic model uses two three-layer BP networks to make classification and prediction on system, respectively. With two instances of WSCC-9 and 3 generator-12 bus power system, it is verified that the method is effective to voltage stability assessment on power system.
  • Keywords
    backpropagation; neural nets; power system stability; assessment index; back propagation neural network; load flow calculation; nonlinear mapping; power system voltage stability; voltage collapse proximity indicator; Load flow; Load modeling; Neural networks; Nonlinear dynamical systems; Power generation; Power system dynamics; Power system modeling; Power system stability; Predictive models; Voltage;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Power and Energy Engineering Conference, 2009. APPEEC 2009. Asia-Pacific
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-2486-3
  • Electronic_ISBN
    978-1-4244-2487-0
  • Type

    conf

  • DOI
    10.1109/APPEEC.2009.4918962
  • Filename
    4918962