APPLICATION OF MACHINE LEARNING TECHNIQUES ON THE DYNAMIC SECURITY OF ISOLATED POWER SYSTEMS WITH LARGE WIND POWER PENETRATION

Georgiadis, D; Karapidakis, E; Hatziargyriou, N

dc.contributor.author	Georgiadis, D	en
dc.contributor.author	Karapidakis, E	en
dc.contributor.author	Hatziargyriou, N	en
dc.date.accessioned	2014-03-01T01:51:28Z
dc.date.available	2014-03-01T01:51:28Z
dc.date.issued	2002	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/26310
dc.relation.uri	http://lpis.csd.auth.gr/setn02/poster_papers/257.pdf	en
dc.subject	Integrated Control	en
dc.subject	Machine Learning	en
dc.subject	Power System	en
dc.subject	scada system	en
dc.subject	Security Assessment	en
dc.subject	Wind Power	en
dc.subject	Decision Tree	en
dc.subject	Neural Network	en
dc.title	APPLICATION OF MACHINE LEARNING TECHNIQUES ON THE DYNAMIC SECURITY OF ISOLATED POWER SYSTEMS WITH LARGE WIND POWER PENETRATION	en
heal.type	journalArticle	en
heal.publicationDate	2002	en
heal.abstract	In isolated power systems increased renewable power penetration with a high level of security can be achieved by the provision of advanced control tools to provide advice to the operators. The CARE system, an integrated control software that has been installed in the SCADA system of Crete includes on-line dynamic security assessment functions that supervise the security operating margins in	en