A novel fault location technique based on current signals only for thyristor controlled series compensated transmission lines using wavelet analysis and self organising map neural networks

W J Cheong, R K Aggarwal

Research output: Contribution to conferencePaper

21 Citations (Scopus)

Abstract

This paper describes the applications of discrete wavelet transforms (DWT) coupled with conventional artificial neural networks (ANN) to the development of a fault location technique under an improved TCSC transmission system model. The fault location scheme is modular based whereby fault type is verified before identifying the fault location using ANN. This method relies on utilising DWT to decompose the line currents obtained from a single terminal into a series of time-scale representations. A feature model using self-organising maps (SOM) is applied herein to verify the fault location capability of the extracted features. Simulation results indicate that this approach can be used as an effective tool for accurate fault location in TCSC systems.
Original languageEnglish
Pages224-227 Vol.1
Publication statusPublished - 2004
EventDevelopments in Power System Protection, 2004. Eighth IEE International Conference on -
Duration: 1 Jan 2004 → …

Conference

ConferenceDevelopments in Power System Protection, 2004. Eighth IEE International Conference on
Period1/01/04 → …

Keywords

  • line current signals
  • power transmission faults
  • DWT
  • thyristor applications
  • TCSC transmission system model
  • self-organising feature maps
  • ANN
  • fault classification
  • self organising maps
  • thyristor controlled series compensated transmission lines
  • SOM
  • fault location technique
  • series of time-scale representations
  • neural nets
  • artificial neural networks
  • fault location
  • discrete wavelet transforms

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  • Cite this

    Cheong, W. J., & Aggarwal, R. K. (2004). A novel fault location technique based on current signals only for thyristor controlled series compensated transmission lines using wavelet analysis and self organising map neural networks. 224-227 Vol.1. Paper presented at Developments in Power System Protection, 2004. Eighth IEE International Conference on, .