Determination of Critical Clearing Time in a Power System Using Support Vector Machine

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DC Field	Value	Language
dc.contributor.author	Bansal, Hari Om	-
dc.date.accessioned	2023-02-14T09:54:50Z	-
dc.date.available	2023-02-14T09:54:50Z	-
dc.date.issued	2009-10	-
dc.identifier.uri	https://papers.ssrn.com/sol3/papers.cfm?abstract_id=1490944	-
dc.identifier.uri	http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/9223	-
dc.description.abstract	A Support Vector Machine (SVM) is a new supervised machine learning method based on the statistical learning theory. It is a very useful method for classification and regression in small-sample cases such as critical clearing time (TCC) calculations, fault diagnosis, etc. In this paper, an effort has been made to determine the TCC using SVM for a system having exposed to a fault and the results obtained are compared with the results of ‘Step-by-Step’ method - a classical method for determining TCC - to prove its superiority over classical methods	en_US
dc.language.iso	en	en_US
dc.publisher	SSRN	en_US
dc.subject	EEE	en_US
dc.subject	Clearing time	en_US
dc.subject	Support Vector Machine (SVM)	en_US
dc.subject	Transient stability analysis	en_US
dc.subject	Dynamic Security Assessment (DSA)	en_US
dc.title	Determination of Critical Clearing Time in a Power System Using Support Vector Machine	en_US
dc.type	Article	en_US
Appears in Collections:	Department of Electrical and Electronics Engineering

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