Transmission line fault location using traveling wave frequencies and extreme learning machine

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dc.authoridTağluk, M. Emin/0000-0001-7789-6376
dc.authoridArkan, Muslum/0000-0001-5313-2400
dc.authoridMAMIS, MEHMET SALIH/0000-0002-6562-0839
dc.authorwosidMamis, Mehmet/AAC-3247-2019
dc.authorwosidTağluk, M. Emin/ABH-1005-2020
dc.authorwosidArkan, Muslum/A-5114-2016
dc.contributor.authorAkmaz, Duzgun
dc.contributor.authorMamis, Mehmet Salih
dc.contributor.authorArkan, Muslum
dc.contributor.authorTagluk, Mehmet Emin
dc.date.accessioned2024-08-04T20:44:01Z
dc.date.available2024-08-04T20:44:01Z
dc.date.issued2018
dc.departmentİnönü Üniversitesien_US
dc.description.abstractIn this research, a new approach was proposed for determining the fault location in transmission lines. Traveling wave frequencies and an extreme learning machine (ELM) were used to determine fault location. Transient signals in the time domain were transformed to the frequency domain using the fast Fourier transform (FFT) and the traveling wave frequencies were detected from the transient frequency spectrum. In order to detect the location of fault, traveling wave frequency was used initially to predict the fault location. The prediction of this fault location was tested for many different fault conditions and was found to be adversely affected by only the source inductance value. This is due to the negative effect of source inductance on wave velocity. Regression feature of ELM was used in order to improve the prediction of fault location and to minimize the negative effect of source inductance. For ELM regression training, values of the fault distance estimated from the traveling-wave frequencies and the source inductance values were used as ELM input data, and the actual distance values were used as ELM output data. After ELM regression training, ELM predicted a new fault location using the input data. The Alternative Transients Program (ATP/EMTP) was used to model J. Marti frequency dependent line model, and the MATLAB program was used to perform fault-detection algorithms. Simulation results show that the proposed method is very successful against many variables such as different fault resistances, source inductances, transmission line characteristics, transmission line lengths. (C) 2017 Elsevier B.V. All rights reserved.en_US
dc.description.sponsorshipScientific and Technological Research Council of Turkey (TUBITAK) [EEEAG-114E152]en_US
dc.description.sponsorshipThis work was supported by the Scientific and Technological Research Council of Turkey (TUBITAK) under Grant EEEAG-114E152.en_US
dc.identifier.doi10.1016/j.epsr.2017.09.019
dc.identifier.endpage7en_US
dc.identifier.issn0378-7796
dc.identifier.issn1873-2046
dc.identifier.scopus2-s2.0-85030091064en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1016/j.epsr.2017.09.019
dc.identifier.urihttps://hdl.handle.net/11616/97981
dc.identifier.volume155en_US
dc.identifier.wosWOS:000419410300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Science Saen_US
dc.relation.ispartofElectric Power Systems Researchen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectTransmission linesen_US
dc.subjectFault-location estimationen_US
dc.subjectFast Fourier transformen_US
dc.subjectTraveling wave frequenciesen_US
dc.subjectExtreme learning machineen_US
dc.titleTransmission line fault location using traveling wave frequencies and extreme learning machineen_US
dc.typeArticleen_US

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