New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz

New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz

Recently, there are still many cases of voltage collapse incidents occur all around the world. This is due to the reason that most power systems today are being operated very close to their stability limits because of the exponentially growing demands, the desires to obtain maximum economic benefits...

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Bibliographic Details
Main Author: Ab Aziz, Nur Fadilah
Format: Book Section
Language:English
Published: Institute of Graduate Studies, UiTM 2014
Subjects:
Malaysia
Online Access:http://ir.uitm.edu.my/id/eprint/19450/
http://ir.uitm.edu.my/id/eprint/19450/1/ABS_NUR%20FADILAH%20AB%20AZIZ%20TDRA%20VOL%206%20IGS_14.pdf
id uitm-19450
recordtype eprints
spelling uitm-194502018-06-11T07:52:00Z http://ir.uitm.edu.my/id/eprint/19450/ New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz Ab Aziz, Nur Fadilah Malaysia Recently, there are still many cases of voltage collapse incidents occur all around the world. This is due to the reason that most power systems today are being operated very close to their stability limits because of the exponentially growing demands, the desires to obtain maximum economic benefits and environmental constraints. Therefore, this thesis presents novel techniques for voltage stability evaluation and enhancement in power system. Firstly, a new bus voltage stability index named as Voltage Stability Condition Indicator (VSCI) was developed. The competency of VSCI was corroborated in three tasks; weak bus identification, automatic line outage contingency ranking and weak area identification. In addition, a new method to detect weak areas in a system termed as Weak Area Clustering Margin (WACM) was also developed. In the first part of study, all methods were tested on IEEE 30-bus and IEEE 118-bus test system. Secondly, a new voltage stability prediction technique utilising state of the art machine learning, Support Vector Machine (SVM) was developed. Institute of Graduate Studies, UiTM 2014 Book Section PeerReviewed text en http://ir.uitm.edu.my/id/eprint/19450/1/ABS_NUR%20FADILAH%20AB%20AZIZ%20TDRA%20VOL%206%20IGS_14.pdf Ab Aziz, Nur Fadilah (2014) New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz. In: The Doctoral Research Abstracts. IPSis Biannual Publication, 6 (6). Institute of Graduate Studies, UiTM, Shah Alam.
repository_type Digital Repository
institution_category Local University
institution Universiti Teknologi MARA
building UiTM Institutional Repository
collection Online Access
language English
topic Malaysia
spellingShingle Malaysia
Ab Aziz, Nur Fadilah
New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
description Recently, there are still many cases of voltage collapse incidents occur all around the world. This is due to the reason that most power systems today are being operated very close to their stability limits because of the exponentially growing demands, the desires to obtain maximum economic benefits and environmental constraints. Therefore, this thesis presents novel techniques for voltage stability evaluation and enhancement in power system. Firstly, a new bus voltage stability index named as Voltage Stability Condition Indicator (VSCI) was developed. The competency of VSCI was corroborated in three tasks; weak bus identification, automatic line outage contingency ranking and weak area identification. In addition, a new method to detect weak areas in a system termed as Weak Area Clustering Margin (WACM) was also developed. In the first part of study, all methods were tested on IEEE 30-bus and IEEE 118-bus test system. Secondly, a new voltage stability prediction technique utilising state of the art machine learning, Support Vector Machine (SVM) was developed.
format Book Section
author Ab Aziz, Nur Fadilah
author_facet Ab Aziz, Nur Fadilah
author_sort Ab Aziz, Nur Fadilah
title New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
title_short New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
title_full New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
title_fullStr New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
title_full_unstemmed New techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / Nur Fadilah Ab Aziz
title_sort new techniques incorporating computational intelligence based for voltage stability evaluation and improvement in power system / nur fadilah ab aziz
publisher Institute of Graduate Studies, UiTM
publishDate 2014
url http://ir.uitm.edu.my/id/eprint/19450/
http://ir.uitm.edu.my/id/eprint/19450/1/ABS_NUR%20FADILAH%20AB%20AZIZ%20TDRA%20VOL%206%20IGS_14.pdf
first_indexed 2023-09-18T23:02:36Z
last_indexed 2023-09-18T23:02:36Z
_version_ 1777418272591839232

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