A flexible statistical pattern recognition approach in transient stability studies

Souflis, JL; Machias, AV; Papadias, BC

dc.contributor.author	Souflis, JL	en
dc.contributor.author	Machias, AV	en
dc.contributor.author	Papadias, BC	en
dc.date.accessioned	2014-03-01T01:07:02Z
dc.date.available	2014-03-01T01:07:02Z
dc.date.issued	1988	en
dc.identifier.issn	0167-8655	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/9758
dc.subject	Bayes' decision rule	en
dc.subject	electric power systems	en
dc.subject	Lyapunov functions	en
dc.subject	point estimates method	en
dc.subject	statistical pattern recognition	en
dc.subject	transient energy	en
dc.subject	Transient stability	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.title	A flexible statistical pattern recognition approach in transient stability studies	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/0167-8655(88)90092-X	en
heal.identifier.secondary	http://dx.doi.org/10.1016/0167-8655(88)90092-X	en
heal.language	English	en
heal.publicationDate	1988	en
heal.abstract	A statistical pattern recognition approach is presented for the on-line transient stability evaluation of electric power systems. The classifier that is used implements the Bayes' decision rule for classification. A flexible point estimates method is used to provide accurate values of the transient energy statistics, required in the classification procedures. © 1988.	en
heal.publisher	ELSEVIER SCIENCE BV	en
heal.journalName	Pattern Recognition Letters	en
dc.identifier.doi	10.1016/0167-8655(88)90092-X	en
dc.identifier.isi	ISI:A1988Q932800002	en
dc.identifier.volume	8	en
dc.identifier.issue	3	en
dc.identifier.spage	147	en
dc.identifier.epage	151	en