Comparative Fault Location Estimation by Using Image Processing in Mixed Transmission Lines

dc.contributor.author Budak, Serkan
dc.contributor.author Akbal, Bahadır
dc.date.accessioned 2024-12-02T19:42:30Z
dc.date.available 2024-12-02T19:42:30Z
dc.date.issued 2020
dc.description.abstract Overhead lines are generally used for electrical energy transmission. Also, XLPE underground cable lines are generally used in the city center and the crowded areas to provide electrical safety, so high voltage underground cable lines are used together with overhead line in the transmission lines, and these lines are called as the mixed lines. The distance protection relays are used to determine the impedance based fault location according to the current and voltage magnitudes in the transmission lines. However, the fault location cannot be correctly detected in mixed transmission lines due to different characteristic impedance per unit length because the characteristic impedance of high voltage cable line is significantly different from overhead line. Thus, determinations of the fault section and location with the distance protection relays are difficult in the mixed transmission lines. In this study, 154 kV overhead transmission line and underground cable line are examined as the mixed transmission line for the distance protection relays. Phase to ground faults are created in the mixed transmission line. overhead line section and underground cable section are simulated by using PSCAD/ EMTDC ™. The short circuit fault images are generated in the distance protection relay for the overhead transmission line and underground cable transmission line faults. The images include the R-X impedance diagram of the fault, and the R-X impedance diagram have been detected by applying image processing steps. Artificial neural network (ANN) and the regression methods are used for prediction of the fault location, and the results of image processing are used as the input parameters for the training process of ANN and the regression methods. The results of ANN and regression methods are compared to select the most suitable method at the end of this study for forecasting of the fault location in transmission lines.
dc.description.version Hakemli
dc.format.medium Basılı+Elektronik
dc.identifier 6696638
dc.identifier.issn 2147-9364
dc.identifier.uri https://hdl.handle.net/20.500.13091/9612
dc.language.iso en en_US
dc.publisher arXiv
dc.relation TR DİZİN
dc.relation.ispartof Konya mühendislik bilimleri dergisi (Online) en_US
dc.subject Mühendislik Temel Alanı->Elektrik-Elektronik Mühendisliği
dc.subject Mixed transmission lines
dc.subject Matlab regression learner
dc.subject Fault location estimation
dc.subject Artificial neural network
dc.subject Short circuit faults
dc.title Comparative Fault Location Estimation by Using Image Processing in Mixed Transmission Lines en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.author.scopusid 57220895614
gdc.author.scopusid 55364837100
gdc.coar.type text::journal::journal article
gdc.description.department Fakülteler, Mühendislik ve Doğa Bilimleri Fakültesi, Elektrik-Elektronik Mühendisliği Bölümü
gdc.description.endpage 11 en_US
gdc.description.publicationcategory Makale - Uluslararası - Kurum Öğretim Elemanı
gdc.description.scopusquality N/A
gdc.description.startpage 1 en_US
gdc.description.volume 8 en_US
gdc.description.wosquality N/A
gdc.publishedmonth December
gdc.virtual.author Akbal, Bahadır
gdc.virtual.author Budak, Serkan
relation.isAuthorOfPublication b90b225e-d7cf-42d3-b274-074e30423d04
relation.isAuthorOfPublication 2ca79fb0-174c-418a-b4fa-7d2af8253776
relation.isAuthorOfPublication.latestForDiscovery b90b225e-d7cf-42d3-b274-074e30423d04

Files

Original bundle

Now showing 1 - 1 of 1
No Thumbnail Available
Name:
2102.11085v1.pdf
Size:
564.75 KB
Format:
Adobe Portable Document Format