Detecting Fault Type and Fault Location in Power Transmission Lines by Extreme Learning Machines

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867;
dc.authorwosidMamis, Mehmet/AAC-3247-2019
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidArkan, Müslüm/A-5114-2016
dc.contributor.authorTagluk, M. Emin
dc.contributor.authorMamis, Mehmet Salih
dc.contributor.authorArkan, Muslum
dc.contributor.authorErtugrul, Omer Faruk
dc.date.accessioned2024-08-04T20:41:07Z
dc.date.available2024-08-04T20:41:07Z
dc.date.issued2015
dc.departmentİnönü Üniversitesien_US
dc.description23nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYen_US
dc.description.abstractImportance of supplying qualified and undisturbed electricity is increasing day by day. Therefore, detecting fault, fault type and fault location is a major issue in power transmission system in order to prevent power delivery system security. In previous studies, we observed that faults can be easily determined by extreme learning machine (ELM) and the aim of this study is to determine applicability of ELM in fault type, zone and location detection. 8 different feature sets were exacted from fault data that produced by ATP and these features were assessed by 15 different classifier and 5 different regression method. The results showed that ELM can be employed for detecting fault types and locations successfully.en_US
dc.description.sponsorshipDept Comp Engn & Elect & Elect Engn,Elect & Elect Engn,Bilkent Univen_US
dc.identifier.endpage1093en_US
dc.identifier.isbn978-1-4673-7386-9
dc.identifier.issn2165-0608
dc.identifier.scopus2-s2.0-84939133810en_US
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage1090en_US
dc.identifier.urihttps://hdl.handle.net/11616/96903
dc.identifier.wosWOS:000380500900253en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2015 23rd Signal Processing and Communications Applications Conference (Siu)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectPower Transmission Linesen_US
dc.subjectFault Typeen_US
dc.subjectFault Locationen_US
dc.subjectExtreme Learning Machineen_US
dc.titleDetecting Fault Type and Fault Location in Power Transmission Lines by Extreme Learning Machinesen_US
dc.typeConference Objecten_US

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