Ertugrul, Omer FarukTagluk, M. EminKaya, Yilmaz2024-08-042024-08-042013978-1-4673-5563-6978-1-4673-5562-92165-0608https://hdl.handle.net/11616/10320921st Signal Processing and Communications Applications Conference (SIU) -- APR 24-26, 2013 -- CYPRUSWith 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/closedAccesscomponenttransmission linefault detectionrelative entropyELMConference ObjectWOS:000325005300050N/A