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Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques

This paper presents transient stability assessment of a large 87-bus system using a new method called the probabilistic neural network (PNN) with incorporation of feature selection and extraction methods. The investigated power system is divided into smaller areas depending on the coherency of the a...

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Main Authors: Abdul Wahab, Noor Izzri, Mohamed, Azah, Hussain, Aini
Format: Article
Language:English
Published: Elsevier 2011
Online Access:http://psasir.upm.edu.my/id/eprint/23038/1/Fast%20transient%20stability%20assessment%20of%20large%20power%20system%20using%20probabilistic%20neural%20network%20with%20feature%20reduction%20techniques.pdf
http://psasir.upm.edu.my/id/eprint/23038/
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spelling my.upm.eprints.230382015-12-09T07:23:43Z http://psasir.upm.edu.my/id/eprint/23038/ Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques Abdul Wahab, Noor Izzri Mohamed, Azah Hussain, Aini This paper presents transient stability assessment of a large 87-bus system using a new method called the probabilistic neural network (PNN) with incorporation of feature selection and extraction methods. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. Feature reduction techniques are then incorporated to reduce the number of features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with the incorporation of feature reduction techniques reduces the time taken to train the PNN without affecting the accuracy of the classification results. Elsevier 2011-09 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/23038/1/Fast%20transient%20stability%20assessment%20of%20large%20power%20system%20using%20probabilistic%20neural%20network%20with%20feature%20reduction%20techniques.pdf Abdul Wahab, Noor Izzri and Mohamed, Azah and Hussain, Aini (2011) Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques. Expert Systems with Applications, 38 (9). pp. 11112-11119. ISSN 0957-4174; ESSN: 1873-6793 10.1016/j.eswa.2011.02.156
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 This paper presents transient stability assessment of a large 87-bus system using a new method called the probabilistic neural network (PNN) with incorporation of feature selection and extraction methods. The investigated power system is divided into smaller areas depending on the coherency of the areas when subjected to disturbances. This is to reduce the amount of data sets collected for the respective areas. Transient stability of the power system is first determined based on the generator relative rotor angles obtained from time domain simulations carried out by considering three phase faults at different loading conditions. The data collected from the time domain simulations are then used as inputs to the PNN. Feature reduction techniques are then incorporated to reduce the number of features to the PNN which is used as a classifier to determine whether the power system is stable or unstable. It can be concluded that the PNN with the incorporation of feature reduction techniques reduces the time taken to train the PNN without affecting the accuracy of the classification results.
format Article
author Abdul Wahab, Noor Izzri
Mohamed, Azah
Hussain, Aini
spellingShingle Abdul Wahab, Noor Izzri
Mohamed, Azah
Hussain, Aini
Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
author_facet Abdul Wahab, Noor Izzri
Mohamed, Azah
Hussain, Aini
author_sort Abdul Wahab, Noor Izzri
title Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
title_short Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
title_full Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
title_fullStr Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
title_full_unstemmed Fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
title_sort fast transient stability assessment of large power system using probabilistic neural network with feature reduction techniques
publisher Elsevier
publishDate 2011
url http://psasir.upm.edu.my/id/eprint/23038/1/Fast%20transient%20stability%20assessment%20of%20large%20power%20system%20using%20probabilistic%20neural%20network%20with%20feature%20reduction%20techniques.pdf
http://psasir.upm.edu.my/id/eprint/23038/
_version_ 1643827942301630464
score 13.211869

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