Decision trees-aided self-organized maps for corrective dynamic security

Voumvoulakis, EM; Hatziargyriou, ND

dc.contributor.author	Voumvoulakis, EM	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:28:07Z
dc.date.available	2014-03-01T01:28:07Z
dc.date.issued	2008	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/18710
dc.subject	Artificial intelligence	en
dc.subject	Corrective control	en
dc.subject	Decision trees	en
dc.subject	Dynamic security	en
dc.subject	Load shedding	en
dc.subject	Machine learning	en
dc.subject	Preventive control	en
dc.subject	Self-organized maps	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Artificial intelligence	en
dc.subject.other	Decision trees	en
dc.subject.other	Learning systems	en
dc.subject.other	Self organizing maps	en
dc.subject.other	Corrective control	en
dc.subject.other	Dynamic security	en
dc.subject.other	Hellenic power system	en
dc.subject.other	Electric load shedding	en
dc.title	Decision trees-aided self-organized maps for corrective dynamic security	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2008.920194	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2008.920194	en
heal.language	English	en
heal.publicationDate	2008	en
heal.abstract	Difficulties in expanding the generation and transmission system force modern power systems to operate often close to their stability limits, in order to meet the continuously growing demand. An effective way to face power system contingencies that can lead to instability is load shedding. This paper proposes a machine learning framework for the evaluation of load shedding for corrective dynamic security of the system. The proposed method employs a self-organized map with decision trees nested in some of its nodes in order to classify the load profiles of a power system. The method is applied on a realistic model of the Hellenic Power System and its added value is shown by comparing results with the ones obtained from the application of simple self-organized maps and simple decision trees. © 2008 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2008.920194	en
dc.identifier.isi	ISI:000258765900040	en
dc.identifier.volume	23	en
dc.identifier.issue	2	en
dc.identifier.spage	622	en
dc.identifier.epage	630	en