Study and analysis of power system stabiliser performance using artificial neural network

This paper represents a study and analysis of designing a PSS (Power system stabilizer) to be used with SMIB (a Single Machine and Infinite Bus) system that was developed using ANN (Artificial Neural Network). Furthermore, the dynamic performance of ANN based stabilizer is established and compared w...

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Main Authors: Magzoub, M.A., Saad, N.B., Ibrahim, R.B.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.32320 /
Published: Trans Tech Publications Ltd 2014
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904396660&doi=10.4028%2fwww.scientific.net%2fAMM.575.605&partnerID=40&md5=64503f4744fd9fea9649b46d7073de7d
http://eprints.utp.edu.my/32320/
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spelling utp-eprints.323202022-03-29T05:03:42Z Study and analysis of power system stabiliser performance using artificial neural network Magzoub, M.A. Saad, N.B. Ibrahim, R.B. This paper represents a study and analysis of designing a PSS (Power system stabilizer) to be used with SMIB (a Single Machine and Infinite Bus) system that was developed using ANN (Artificial Neural Network). Furthermore, the dynamic performance of ANN based stabilizer is established and compared with conventional types of PSS. The proposed scheme's effectiveness was tested through simulation in order to analyse the stability features of the small signal of the system regarding the operating situation of the steady state when a transmission line is lost. The focus of the primary method was on how the control performed. This was later confirmed to possess the level of a reaching time that was shorter and a spike that was lower. © (2014) Trans Tech Publications, Switzerland. Trans Tech Publications Ltd 2014 Article NonPeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904396660&doi=10.4028%2fwww.scientific.net%2fAMM.575.605&partnerID=40&md5=64503f4744fd9fea9649b46d7073de7d Magzoub, M.A. and Saad, N.B. and Ibrahim, R.B. (2014) Study and analysis of power system stabiliser performance using artificial neural network. Applied Mechanics and Materials, 575 . pp. 605-609. http://eprints.utp.edu.my/32320/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description This paper represents a study and analysis of designing a PSS (Power system stabilizer) to be used with SMIB (a Single Machine and Infinite Bus) system that was developed using ANN (Artificial Neural Network). Furthermore, the dynamic performance of ANN based stabilizer is established and compared with conventional types of PSS. The proposed scheme's effectiveness was tested through simulation in order to analyse the stability features of the small signal of the system regarding the operating situation of the steady state when a transmission line is lost. The focus of the primary method was on how the control performed. This was later confirmed to possess the level of a reaching time that was shorter and a spike that was lower. © (2014) Trans Tech Publications, Switzerland.
format Article
author Magzoub, M.A.
Saad, N.B.
Ibrahim, R.B.
spellingShingle Magzoub, M.A.
Saad, N.B.
Ibrahim, R.B.
Study and analysis of power system stabiliser performance using artificial neural network
author_sort Magzoub, M.A.
title Study and analysis of power system stabiliser performance using artificial neural network
title_short Study and analysis of power system stabiliser performance using artificial neural network
title_full Study and analysis of power system stabiliser performance using artificial neural network
title_fullStr Study and analysis of power system stabiliser performance using artificial neural network
title_full_unstemmed Study and analysis of power system stabiliser performance using artificial neural network
title_sort study and analysis of power system stabiliser performance using artificial neural network
publisher Trans Tech Publications Ltd
publishDate 2014
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904396660&doi=10.4028%2fwww.scientific.net%2fAMM.575.605&partnerID=40&md5=64503f4744fd9fea9649b46d7073de7d
http://eprints.utp.edu.my/32320/
_version_ 1741197717240020992
score 11.62408

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