• DocumentCode
    2355207
  • Title

    Power system voltage stability analysis using ANN and Continuation Power Flow Methods

  • Author

    Balasubramanian, R. ; Singh, Rhythm

  • Author_Institution
    Centre for Energy Studies, Indian Inst. of Technol., Delhi, India
  • fYear
    2011
  • fDate
    25-28 Sept. 2011
  • Firstpage
    1
  • Lastpage
    7
  • Abstract
    This project presents an Artificial Neural Network (ANN) based method, involving the usage of Continuation Power Flow Methods, for on-line voltage stability assessment of power systems. A continuation power flow type algorithm is implemented using the MATLAB toolbox. This implementation generates the nose curves, used for voltage stability analysis for the IEEE 30 bus test system which, in turn, are used as target outputs for training the ANNs, by finding the distance to voltage collapse from the current system operating point. The trained ANN is supposed to provide, as output, the Voltage Collapse Proximity Indicators (VCPI) for all the vulnerable load buses of the system, which are a measure of the voltage stability margin for such buses.
  • Keywords
    load flow; neural nets; power engineering computing; power system stability; ANN; IEEE 30 bus test system; MATLAB toolbox; VCPI; artificial neural network; continuation power flow methods; continuation power flow type algorithm; on-line voltage stability assessment; power system voltage stability analysis; voltage collapse proximity indicators; Artificial neural networks; Generators; Load flow; Loading; Power system stability; Reactive power; Training;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent System Application to Power Systems (ISAP), 2011 16th International Conference on
  • Conference_Location
    Hersonissos
  • Print_ISBN
    978-1-4577-0807-7
  • Electronic_ISBN
    978-1-4577-0808-4
  • Type

    conf

  • DOI
    10.1109/ISAP.2011.6082192
  • Filename
    6082192