Ertuğrul, Ömer FarukTağluk, Mehmet Emin2019-07-042019-07-042013-06-13Ertuğrul, Ö. F., Tağluk, M. E., Kaya, Y. (2013).Enerji iletim Hatlarında oluşan arızaların aşırı öğrenme makinesi ile tespiti. 2013 21st Signal Processing and Communications Applications Conference (SIU), 24-26 April 2013, Haspolat, Turkey. https://doi.org/10.1109/siu.2013.6531209978-1-4673-5563-6978-1-4673-5562-9978-1-4673-5561-2https://doi.org/10.1109/siu.2013.6531209https://hdl.handle.net/20.500.12402/2197With 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.trinfo:eu-repo/semantics/closedAccessAttribution-NonCommercial-ShareAlike 3.0 United StatesEnerji İletim HattıArıza TespitiBağıl EntopiAşırı Makine ÖğrenmesiComponentELMFault DetectionRelative EntropyTransmission LineConference ObjectN/AN/A