On-line dynamic security classification using probabilistic neural networks

Gavoyiannis, AE; Hatziargyriou, ND

dc.contributor.author	Gavoyiannis, AE	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:16:55Z
dc.date.available	2014-03-01T01:16:55Z
dc.date.issued	2001	en
dc.identifier.issn	0969-1170	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/14262
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0035358974&partnerID=40&md5=8273723d79c0b08decac9fe8d78072da	en
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0035358974&partnerID=40&md5=8273723d79c0b08decac9fe8d78072da	en
dc.subject	Convenient maintenance	en
dc.subject	Gaussian mixtures	en
dc.subject	Maximum likelihood (ML)	en
dc.subject	Parallel process	en
dc.subject	Probabilistic neural network (PNN)	en
dc.subject	Probability density function (PDF)	en
dc.subject.classification	Computer Science, Artificial Intelligence	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Decision making	en
dc.subject.other	Electric power system protection	en
dc.subject.other	Maximum likelihood estimation	en
dc.subject.other	Neural networks	en
dc.subject.other	Online systems	en
dc.subject.other	Parallel processing systems	en
dc.subject.other	Pattern recognition	en
dc.subject.other	Probabilistic logics	en
dc.subject.other	Probability density function	en
dc.subject.other	Gaussian mixtures	en
dc.subject.other	Online security classification	en
dc.subject.other	Security systems	en
dc.title	On-line dynamic security classification using probabilistic neural networks	en
heal.type	journalArticle	en
heal.language	English	en
heal.publicationDate	2001	en
heal.abstract	This paper addresses the problem of on-line dynamic security classification of electrical power systems using multiclass pattern recognition with Probabilistic Neural Networks. The various patterns are recognized by supervised learning with posterior probabilities of an input sample belonging to each class. These probabilities can be used in a subsequent decision-making stage to arrive at a classification. The learning of each class can be performed in parallel. Results regarding performance of the proposed pattern recognition tested on the dynamic security of an actual island power system are presented and discussed.	en
heal.publisher	C R L PUBLISHING LTD	en
heal.journalName	International Journal of Engineering Intelligent Systems for Electrical Engineering and Communications	en
dc.identifier.isi	ISI:000169744600003	en
dc.identifier.volume	9	en
dc.identifier.issue	2	en
dc.identifier.spage	83	en
dc.identifier.epage	89	en