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ANN Based Fault Detection Classification in Power System Transmission line

Bishal, Md Rafayel ; Ahmed, Sabbir ; Molla, Nur Mohammad ; Mamun, Kazi Mohammad ; Rahman, Asfaqur and Hysam, Md Abdullah Al LU orcid (2021) 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021 In 2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
Abstract

The development of an artificial neural network to analyze power system faults is currently under way. However, the produced system is unsatisfactory because it is prone to failure and takes longer to detect faults. The ultimate goal of this project is to create an artificial neural network system that can identify and classify all forms of faults in power grid lines soon after they occur. In principle, three-phase currents are used as inputs, and the suggested approach takes into account all sorts of defects. Following that, an ANN training network for a power system was constructed in MATLAB/Simulink software, and the ANN network was trained using a feed-forward approach. Finally, the simulation shows that when a faulat occurs in a... (More)

The development of an artificial neural network to analyze power system faults is currently under way. However, the produced system is unsatisfactory because it is prone to failure and takes longer to detect faults. The ultimate goal of this project is to create an artificial neural network system that can identify and classify all forms of faults in power grid lines soon after they occur. In principle, three-phase currents are used as inputs, and the suggested approach takes into account all sorts of defects. Following that, an ANN training network for a power system was constructed in MATLAB/Simulink software, and the ANN network was trained using a feed-forward approach. Finally, the simulation shows that when a faulat occurs in a transmission line, the ANN accurately diagnoses the fault and minimizes any potential damage by efficiently identifying faults in overhead transmission systems.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
ANN, Fault classification, Fault detection, Transmission line
host publication
2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
series title
2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2021 International Conference on Science and Contemporary Technologies, ICSCT 2021
conference location
Dhaka, Bangladesh
conference dates
2021-08-05 - 2021-08-07
external identifiers
  • scopus:85124038288
ISBN
9781665421324
DOI
10.1109/ICSCT53883.2021.9642410
language
English
LU publication?
no
additional info
Publisher Copyright: © 2021 IEEE.
id
9aef0489-f3eb-4911-bd6b-6cd1e2fbc111
date added to LUP
2025-03-26 09:17:53
date last changed
2025-04-25 03:33:45
@inproceedings{9aef0489-f3eb-4911-bd6b-6cd1e2fbc111,
  abstract     = {{<p>The development of an artificial neural network to analyze power system faults is currently under way. However, the produced system is unsatisfactory because it is prone to failure and takes longer to detect faults. The ultimate goal of this project is to create an artificial neural network system that can identify and classify all forms of faults in power grid lines soon after they occur. In principle, three-phase currents are used as inputs, and the suggested approach takes into account all sorts of defects. Following that, an ANN training network for a power system was constructed in MATLAB/Simulink software, and the ANN network was trained using a feed-forward approach. Finally, the simulation shows that when a faulat occurs in a transmission line, the ANN accurately diagnoses the fault and minimizes any potential damage by efficiently identifying faults in overhead transmission systems.</p>}},
  author       = {{Bishal, Md Rafayel and Ahmed, Sabbir and Molla, Nur Mohammad and Mamun, Kazi Mohammad and Rahman, Asfaqur and Hysam, Md Abdullah Al}},
  booktitle    = {{2021 International Conference on Science and Contemporary Technologies, ICSCT 2021}},
  isbn         = {{9781665421324}},
  keywords     = {{ANN; Fault classification; Fault detection; Transmission line}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  series       = {{2021 International Conference on Science and Contemporary Technologies, ICSCT 2021}},
  title        = {{ANN Based Fault Detection Classification in Power System Transmission line}},
  url          = {{http://dx.doi.org/10.1109/ICSCT53883.2021.9642410}},
  doi          = {{10.1109/ICSCT53883.2021.9642410}},
  year         = {{2021}},
}