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  Published Paper Details:

  Paper Title

DETECTION AND IDENTIFICATION OF FAULTS IN UNDERGROUND CABLE USING ARTIFICIAL NEURAL NETWORK

  Authors

  Athira N,  Roshni V V,  Sreethi Pradeep,  Vaishna Mammathan,  Lakshmi S Suresh

  Keywords

Underground cable, Simulink, ANN, Training, Fault types.

  Abstract


This paper presents an Artificial Neural Network (ANN) simulated model of fault detection and identification in an underground cable network. To be precise, a two-step sectionalized process is carried out in a phased manner. Step one is initiated by the modelling of transmission system using MATLAB-SIMULINK followed by the creation of faults in the system network. Secondly the Fourier analyzed fault parameters obtained from the Simulink model is fed to the training set of ANN. Identifying the fault type plays a vital role as the sudden outcome of different fault types implicate the stability, reliability and other serious post fault effects which is to be suppressed before identifying the location and method of isolation.

  IJCRT's Publication Details

  Unique Identification Number - IJCRTH020003

  Paper ID - 211928

  Page Number(s) - 14-19

  Pubished in - Volume 9 | Issue 10 | October 2021

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  Athira N,  Roshni V V,  Sreethi Pradeep,  Vaishna Mammathan,  Lakshmi S Suresh,   "DETECTION AND IDENTIFICATION OF FAULTS IN UNDERGROUND CABLE USING ARTIFICIAL NEURAL NETWORK", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.9, Issue 10, pp.14-19, October 2021, Available at :http://www.ijcrt.org/papers/IJCRTH020003.pdf

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ISSN: 2320-2882
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Journal Starting Year (ESTD) : 2013
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ISSN and 7.97 Impact Factor Details


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ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
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