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Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV

With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity. Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system. In this paper a prediction of volt...

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Main Authors: Mehdi, Omer H., Abdul Wahab, Noor Izzri, Mohammad K. Abd,
Format: Article
Language:English
Published: Canadian Center of Science and Education 2011
Online Access:http://psasir.upm.edu.my/id/eprint/23041/1/Fast%20prediction%20of%20voltage%20stability%20index%20based%20on%20radial%20basis%20function%20neural%20network%20Iraqi%20super%20grid%20network%2C%20400-kV.pdf
http://psasir.upm.edu.my/id/eprint/23041/
http://ccsenet.org/journal/index.php/mas/article/view/11637
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spelling my.upm.eprints.230412015-12-09T07:15:06Z http://psasir.upm.edu.my/id/eprint/23041/ Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV Mehdi, Omer H. Abdul Wahab, Noor Izzri Mohammad K. Abd, With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity. Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system. In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV. Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method. The input data was performed by using a 135 samples test with different bus voltage (Vb), Bus active and reactive power (Pb, Qb), bus load angle (?b) and FVSIij. The RBFNN model has four input representing the (Vb, Pb, Qb and ?b), sixteen nodes at hidden layer and one output node representing FVSIij have been used to assess the security on line. The proposed method has been tested in the IEEE 30 and a practical system. In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added. Canadian Center of Science and Education 2011-08 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23041/1/Fast%20prediction%20of%20voltage%20stability%20index%20based%20on%20radial%20basis%20function%20neural%20network%20Iraqi%20super%20grid%20network%2C%20400-kV.pdf Mehdi, Omer H. and Abdul Wahab, Noor Izzri and Mohammad K. Abd, (2011) Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV. Modern Applied Science, 5 (4). pp. 190-199. ISSN 1913-1844; ESSN: 1913-1852 http://ccsenet.org/journal/index.php/mas/article/view/11637 10.5539/mas.v5n4p190
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 With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity. Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system. In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV. Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method. The input data was performed by using a 135 samples test with different bus voltage (Vb), Bus active and reactive power (Pb, Qb), bus load angle (?b) and FVSIij. The RBFNN model has four input representing the (Vb, Pb, Qb and ?b), sixteen nodes at hidden layer and one output node representing FVSIij have been used to assess the security on line. The proposed method has been tested in the IEEE 30 and a practical system. In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added.
format Article
author Mehdi, Omer H.
Abdul Wahab, Noor Izzri
Mohammad K. Abd,
spellingShingle Mehdi, Omer H.
Abdul Wahab, Noor Izzri
Mohammad K. Abd,
Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
author_facet Mehdi, Omer H.
Abdul Wahab, Noor Izzri
Mohammad K. Abd,
author_sort Mehdi, Omer H.
title Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
title_short Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
title_full Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
title_fullStr Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
title_full_unstemmed Fast prediction of voltage stability index based on radial basis function neural network: Iraqi super grid network, 400-kV
title_sort fast prediction of voltage stability index based on radial basis function neural network: iraqi super grid network, 400-kv
publisher Canadian Center of Science and Education
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/23041/1/Fast%20prediction%20of%20voltage%20stability%20index%20based%20on%20radial%20basis%20function%20neural%20network%20Iraqi%20super%20grid%20network%2C%20400-kV.pdf
http://psasir.upm.edu.my/id/eprint/23041/
http://ccsenet.org/journal/index.php/mas/article/view/11637
_version_ 1643827943187677184
score 13.211869

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