A fuzzy logic optimization methodology for the estimation of the critical flashover voltage on insulators

Asimakopoulou, GE; Kontargyri, VT; Tsekouras, GJ; Elias, ChN; Asimakopoulou, FE; Stathopulos, IA

dc.contributor.author	Asimakopoulou, GE	en
dc.contributor.author	Kontargyri, VT	en
dc.contributor.author	Tsekouras, GJ	en
dc.contributor.author	Elias, ChN	en
dc.contributor.author	Asimakopoulou, FE	en
dc.contributor.author	Stathopulos, IA	en
dc.date.accessioned	2014-03-01T01:34:52Z
dc.date.available	2014-03-01T01:34:52Z
dc.date.issued	2011	en
dc.identifier.issn	0378-7796	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20915
dc.subject	Confidence interval	en
dc.subject	Critical flashover voltage	en
dc.subject	Fuzzy logic	en
dc.subject	High voltage insulators	en
dc.subject	Polluted insulators	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Confidence interval	en
dc.subject.other	Critical flashover voltages	en
dc.subject.other	Fuzzifications	en
dc.subject.other	High voltage insulators	en
dc.subject.other	Polluted insulators	en
dc.subject.other	Resampling method	en
dc.subject.other	Triangular membership functions	en
dc.subject.other	Electric insulation	en
dc.subject.other	Electric insulators	en
dc.subject.other	Flashover	en
dc.subject.other	Forecasting	en
dc.subject.other	Fuzzy sets	en
dc.subject.other	Membership functions	en
dc.subject.other	Optimization	en
dc.subject.other	Fuzzy logic	en
dc.title	A fuzzy logic optimization methodology for the estimation of the critical flashover voltage on insulators	en
heal.type	journalArticle	en
heal.identifier.primary	10.1016/j.epsr.2010.10.024	en
heal.identifier.secondary	http://dx.doi.org/10.1016/j.epsr.2010.10.024	en
heal.language	English	en
heal.publicationDate	2011	en
heal.abstract	The objective of this paper is to present a new methodology for predicting the critical flashover voltage of polluted insulators based on fuzzy logic. The prediction contains not only the estimated value, but also the respective confidence interval based on the re-sampling method. Various parameters, such as the number and the base width of the triangular membership functions used for the fuzzification process, etc., are assigned different values in order to optimize the estimation of the critical flashover voltage. Additionally, different methods for training the fuzzy system are applied and compared for their appropriateness in accurately predicting the critical flashover voltage. (C) 2010 Elsevier B.V. All rights reserved.	en
heal.publisher	ELSEVIER SCIENCE SA	en
heal.journalName	Electric Power Systems Research	en
dc.identifier.doi	10.1016/j.epsr.2010.10.024	en
dc.identifier.isi	ISI:000286962700038	en
dc.identifier.volume	81	en
dc.identifier.issue	2	en
dc.identifier.spage	580	en
dc.identifier.epage	588	en