Tagluk, M. EminMamis, Mehmet SalihArkan, MuslumErtugrul, Omer Faruk2024-08-042024-08-042015978-1-4673-7386-92165-0608https://hdl.handle.net/11616/9690323nd Signal Processing and Communications Applications Conference (SIU) -- MAY 16-19, 2015 -- Inonu Univ, Malatya, TURKEYImportance 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.trinfo:eu-repo/semantics/closedAccessPower Transmission LinesFault TypeFault LocationExtreme Learning MachineConference Object109010932-s2.0-84939133810N/AWOS:000380500900253N/A