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Static power system security assessment via artificial neural network.

Maintaining system security is an important factor in the operation of a power system. The aim of this study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to determine the security status of a power system. Feed Forward Back Propagation Neural N...

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Main Authors:	Jasni, Jasronita, Ab Kadir, Mohd Zainal Abidin
Format:	Article
Language:	English
Published:	2011
Online Access:	http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf http://psasir.upm.edu.my/id/eprint/23187/ http://www.jatit.org/volumes/Vol31No2/thirtyfirst_volume_2_2011.php
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id	my.upm.eprints.23187
record_format	eprints
spelling	my.upm.eprints.231872015-10-07T03:16:54Z http://psasir.upm.edu.my/id/eprint/23187/ Static power system security assessment via artificial neural network. Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin Maintaining system security is an important factor in the operation of a power system. The aim of this study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to determine the security status of a power system. Feed Forward Back Propagation Neural Network is implemented to classify the security condition of IEEE 9 bus system. The input data of ANN are derived from offline Newton Raphson load flow analysis. The result obtained from the ANN method is compared with the Newton Raphson load flow analysis in terms of accuracy to predict the security level of IEEE 9 bus system and the computational time required by each method. The average time required by Newton- Raphson load flow analysis to evaluate security level of IEEE 9 bus system is 0.0481 seconds while the average time required by neural network is 0.0119 seconds. The accuracy of 13 hidden neurons feed forward back propagation neural network to predict the security level of IEEE 9 bus system is 98.57%. In conclusion, ANN is found to be reliable to evaluate the security level of IEEE 9 bus system. 2011 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf Jasni, Jasronita and Ab Kadir, Mohd Zainal Abidin (2011) Static power system security assessment via artificial neural network. Journal of Theoretical and Applied Information Technology, 31 (2). pp. 119-128. ISSN 1992-8645 http://www.jatit.org/volumes/Vol31No2/thirtyfirst_volume_2_2011.php
institution	Universiti Putra Malaysia
building	UPM Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Putra Malaysia
content_source	UPM Institutional Repository
url_provider	http://psasir.upm.edu.my/
language	English
description	Maintaining system security is an important factor in the operation of a power system. The aim of this study is to evaluate the reliability using artificial neural network (ANN) in static security assessment to determine the security status of a power system. Feed Forward Back Propagation Neural Network is implemented to classify the security condition of IEEE 9 bus system. The input data of ANN are derived from offline Newton Raphson load flow analysis. The result obtained from the ANN method is compared with the Newton Raphson load flow analysis in terms of accuracy to predict the security level of IEEE 9 bus system and the computational time required by each method. The average time required by Newton- Raphson load flow analysis to evaluate security level of IEEE 9 bus system is 0.0481 seconds while the average time required by neural network is 0.0119 seconds. The accuracy of 13 hidden neurons feed forward back propagation neural network to predict the security level of IEEE 9 bus system is 98.57%. In conclusion, ANN is found to be reliable to evaluate the security level of IEEE 9 bus system.
format	Article
author	Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin
spellingShingle	Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin Static power system security assessment via artificial neural network.
author_facet	Jasni, Jasronita Ab Kadir, Mohd Zainal Abidin
author_sort	Jasni, Jasronita
title	Static power system security assessment via artificial neural network.
title_short	Static power system security assessment via artificial neural network.
title_full	Static power system security assessment via artificial neural network.
title_fullStr	Static power system security assessment via artificial neural network.
title_full_unstemmed	Static power system security assessment via artificial neural network.
title_sort	static power system security assessment via artificial neural network.
publishDate	2011
url	http://psasir.upm.edu.my/id/eprint/23187/1/Static%20power%20system%20security%20assessment%20via%20artificial%20neural%20network.pdf http://psasir.upm.edu.my/id/eprint/23187/ http://www.jatit.org/volumes/Vol31No2/thirtyfirst_volume_2_2011.php
_version_	1643827983742402560
score	13.211869

Static power system security assessment via artificial neural network.

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