On-line dynamic security assessment of isolated networks integrating large wind power production

Pecas Lopes, JA; Hatziargyriou, N; Vasconcelos, M; Karapidakis, E; Fidalgo, J

dc.contributor.author	Pecas Lopes, JA	en
dc.contributor.author	Hatziargyriou, N	en
dc.contributor.author	Vasconcelos, M	en
dc.contributor.author	Karapidakis, E	en
dc.contributor.author	Fidalgo, J	en
dc.date.accessioned	2014-03-01T02:48:46Z
dc.date.available	2014-03-01T02:48:46Z
dc.date.issued	1999	en
dc.identifier.issn	0309524X	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/34089
dc.relation.uri	http://www.scopus.com/inward/record.url?eid=2-s2.0-0343183038&partnerID=40&md5=a3023e03c60f74d132003cd8a840d24f	en
dc.subject.other	Decision theory	en
dc.subject.other	Electric power generation	en
dc.subject.other	Electric power system protection	en
dc.subject.other	Learning systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Trees (mathematics)	en
dc.subject.other	Control advice for renewable energy power (CARE) systems	en
dc.subject.other	Isolated power systems	en
dc.subject.other	Wind power	en
dc.subject.other	control method	en
dc.subject.other	on-line technique	en
dc.subject.other	wind power control	en
dc.subject.other	wind power system	en
dc.title	On-line dynamic security assessment of isolated networks integrating large wind power production	en
heal.type	conferenceItem	en
heal.publicationDate	1999	en
heal.abstract	The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.The paper describes the on-line dynamic security assessment functions developed within the European Union, DGXII programme, CARE. These functions are based exclusively on the application of machine learning techniques. A description of the problem and the data set generation procedure for the Crete island power system are included. Comparative results regarding performances of Decision Trees, Kernel Regression Trees and Neural Networks are presented and discussed.	en
heal.publisher	Multi-Science Publishing Co, Ltd, Brentwood, United Kingdom	en
heal.journalName	Wind Engineering	en
dc.identifier.volume	23	en
dc.identifier.issue	2	en
dc.identifier.spage	107	en
dc.identifier.epage	117	en