Fault Detection at Power Transmission Lines by Extreme Learning Machine

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dc.authoridERTUGRUL, Ömer Faruk/0000-0003-0710-0867
dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authorwosidKAYA, Yılmaz/C-3822-2017
dc.authorwosidERTUGRUL, Ömer Faruk/F-7057-2015
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.contributor.authorErtugrul, Omer Faruk
dc.contributor.authorTagluk, M. Emin
dc.contributor.authorKaya, Yilmaz
dc.date.accessioned2024-08-04T20:58:51Z
dc.date.available2024-08-04T20:58:51Z
dc.date.issued2013
dc.departmentİnönü Üniversitesien_US
dc.description21st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUSen_US
dc.description.abstractWith the increase of energy demand continuous energy transmission gained considerable attention. For a continuous energy transmission, the faulty power transmission line needs to be quickly isolated from the system. In this study, Extreme Learning Machine (ELM) possessing fast learning and high generalization capacity was used for this purpose and it was found as showing a good performance in detecting the faulty transmission line. In the study real fault signals recorded from transmission lines were used. A feature vector was formed from a cycle of the energy signal using relative entropy and classified via ELM. The obtained results were compared with the ones obtained through SVM, YSA, NB, J48 and PART learning techniques and the ones obtained in the previous studies. According the obtained results ELM both in terms of speed and performance was found superior.en_US
dc.identifier.isbn978-1-4673-5563-6
dc.identifier.isbn978-1-4673-5562-9
dc.identifier.issn2165-0608
dc.identifier.urihttps://hdl.handle.net/11616/103209
dc.identifier.wosWOS:000325005300050en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherIeeeen_US
dc.relation.ispartof2013 21st 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.subjectcomponenten_US
dc.subjecttransmission lineen_US
dc.subjectfault detectionen_US
dc.subjectrelative entropyen_US
dc.subjectELMen_US
dc.titleFault Detection at Power Transmission Lines by Extreme Learning Machineen_US
dc.typeConference Objecten_US

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