• DocumentCode
    1181164
  • Title

    Best ANN structures for fault location in single-and double-circuit transmission lines

  • Author

    Gracia, J. ; Mazón, A.J. ; Zamora, I.

  • Author_Institution
    Gov. of the Autonomous Community of Aragon, Zaragoza, Spain
  • Volume
    20
  • Issue
    4
  • fYear
    2005
  • Firstpage
    2389
  • Lastpage
    2395
  • Abstract
    The great development in computing power has allowed the implementation of artificial neural networks (ANNs) in the most diverse fields of technology. This paper shows how diverse ANN structures can be applied to the processes of fault classification and fault location in overhead two-terminal transmission lines, with single and double circuit. The existence of a large group of valid ANN structures guarantees the applicability of ANNs in the fault classification and location processes. The selection of the best ANN structures for each process has been carried out by means of a software tool called SARENEUR.
  • Keywords
    fault location; neural nets; power engineering computing; power transmission faults; ANN structures; SARENEUR software tool; artificial neural nets; fault classification; fault location; single-circuit transmission lines; Artificial neural networks; Circuit faults; Data acquisition; Electric variables measurement; Fault location; Neural networks; Power transmission lines; Pulse measurements; Transmission lines; Vector quantization; Artificial neural networks (ANNs); fault classification; fault location; learning vector quantization; multilayer perceptron;
  • fLanguage
    English
  • Journal_Title
    Power Delivery, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0885-8977
  • Type

    jour

  • DOI
    10.1109/TPWRD.2005.855482
  • Filename
    1514483