Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network

The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to d...

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Main Authors: Behniafar Ali, Darabi Ahmad, Banejad Mahdi, Baghayipour Mohammadreza
Format: Article
Language:English
Published: Faculty of Technical Sciences in Cacak 2013-01-01
Series:Serbian Journal of Electrical Engineering
Subjects:
Online Access:http://www.doiserbia.nb.rs/img/doi/1451-4869/2013/1451-48691300015B.pdf
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author Behniafar Ali
Darabi Ahmad
Banejad Mahdi
Baghayipour Mohammadreza
author_facet Behniafar Ali
Darabi Ahmad
Banejad Mahdi
Baghayipour Mohammadreza
author_sort Behniafar Ali
collection DOAJ
description The electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP) neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach.
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spelling doaj.art-4ab502f94bd440898b5d9d5b73a363372022-12-22T01:34:41ZengFaculty of Technical Sciences in CacakSerbian Journal of Electrical Engineering1451-48692217-71832013-01-0110344545710.2298/SJEE130601015B1451-48691300015BDetecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural networkBehniafar Ali0Darabi Ahmad1Banejad Mahdi2Baghayipour Mohammadreza3Faculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, IranFaculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, IranFaculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, IranFaculty of Electrical and Robotic Engineering, Shahrood University of Technology, Shahrood, IranThe electric marine instruments are newly inserted in the trade and industry, for which the existence of an equipped and reliable power system is necessitated. One of the features of such a power system is that it cannot have an earth system causing the protection relays not to be able to detect the single line to ground short circuit fault. While on the other hand, the occurrence of another similar fault at the same time can lead to the double line fault and thereby the tripping of relays and shortening of vital loads. This in turn endangers the personals' security and causes the loss of military plans. From the above considerations, it is inferred that detecting the single line to ground fault in the marine instruments is of a special importance. In this way, this paper intends to detect the single line to ground fault in the power systems of the marine instruments using the wavelet transform and Multi-Layer Perceptron (MLP) neural network. In the numerical analysis, several different types of short circuit faults are simulated on several marine power systems and the proposed approach is applied to detect the single line to ground fault. The results are of a high quality and preciseness and perfectly demonstrate the effectiveness of the proposed approach.http://www.doiserbia.nb.rs/img/doi/1451-4869/2013/1451-48691300015B.pdfsingle phase to ground faultungrounded systemwavelet transformneural networkshipboard
spellingShingle Behniafar Ali
Darabi Ahmad
Banejad Mahdi
Baghayipour Mohammadreza
Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
Serbian Journal of Electrical Engineering
single phase to ground fault
ungrounded system
wavelet transform
neural network
shipboard
title Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
title_full Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
title_fullStr Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
title_full_unstemmed Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
title_short Detecting the single line to ground short circuit fault in the submarine’s power system using the artificial neural network
title_sort detecting the single line to ground short circuit fault in the submarine s power system using the artificial neural network
topic single phase to ground fault
ungrounded system
wavelet transform
neural network
shipboard
url http://www.doiserbia.nb.rs/img/doi/1451-4869/2013/1451-48691300015B.pdf
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AT darabiahmad detectingthesinglelinetogroundshortcircuitfaultinthesubmarinespowersystemusingtheartificialneuralnetwork
AT banejadmahdi detectingthesinglelinetogroundshortcircuitfaultinthesubmarinespowersystemusingtheartificialneuralnetwork
AT baghayipourmohammadreza detectingthesinglelinetogroundshortcircuitfaultinthesubmarinespowersystemusingtheartificialneuralnetwork