Power system topological observability analysis using artificial neural networks

Amit Jain, R. Balasubramanian, S. C. Tripathy, Brij N. Singh, Yoshiyuki Kawazoe

Research output: Chapter in Book/Report/Conference proceedingConference contribution

12 Citations (Scopus)

Abstract

This paper presents a new method for the power system topological observability analysis using the artificial neural networks. The power system observability problem, related to the power system configuration or network topology, called as the topological observability, is studied utilizing the artificial neural network model, based on multilayer perceptrons using the Back-propagation algorithm as the training algorithm. Another training algorithm, quickprop is also applied for training the similar artificial neural network to further check the suitability of other training algorithm also. The proposed artificial forward neural network model has been tested on sample power systems and results are presented.

Original languageEnglish
Title of host publication2005 IEEE Power Engineering Society General Meeting
Pages497-502
Number of pages6
Publication statusPublished - 2005 Oct 31
Event2005 IEEE Power Engineering Society General Meeting - San Francisco, CA, United States
Duration: 2005 Jun 122005 Jun 16

Publication series

Name2005 IEEE Power Engineering Society General Meeting
Volume1

Other

Other2005 IEEE Power Engineering Society General Meeting
CountryUnited States
CitySan Francisco, CA
Period05/6/1205/6/16

Keywords

  • Artificial neural network
  • Back propagation
  • Quickprop
  • State estimation
  • Topological observability

ASJC Scopus subject areas

  • Engineering(all)

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