Fault Detection, Classification, and Location Based on Empirical Wavelet Transform-Teager Energy Operator and ANN for Hybrid Transmission Lines in VSC-HVDC Systems

Basit Öğe Kaydı
dc.authoridChen, Zhe/0000-0002-2919-4481
dc.authoridSahebkar Farkhani, Jalal/0000-0002-6788-7588
dc.contributor.authorFarkhani, Jalal Sahebkar
dc.contributor.authorCelik, Ozgur
dc.contributor.authorMa, Kaiqi
dc.contributor.authorBak, Claus Leth
dc.contributor.authorChen, Zhe
dc.date.accessioned2026-02-27T07:33:10Z
dc.date.available2026-02-27T07:33:10Z
dc.date.issued2025
dc.description.abstractTraditional protection methods are not suitable for hybrid (cable and overhead) transmission lines in voltage source converter based high-voltage direct current (VSC-HVDC) systems. Accordingly, this paper presents the robust fault detection, classification, and location based on the empirical wavelet transform-Teager energy operator (EWT-TEO) and artificial neural network (ANN) for hybrid transmission lines in VSC-HVDC systems. The operational scheme of the proposed protection method consists of two loops: (1)an EWT-TEO based feature extraction loop, (2) and an ANN-based fault detection, classification, and location loop. Under the proposed protection method, the voltage and current signals are decomposed into several sub-passbands with low and high frequencies using the empirical wavelet transform (EWT) method. The energy content extracted by the EWT is fed into the ANN for fault detection, classification, and location. Various faul(t) cases, including the high-impedance fault (HIF) as well as noises, are performed to train the ANN with two hidden layers. The test system and signal decomposition are conducted by PSCAD/EMT-DC and MATLAB, respectively. The performance of the proposed protection method is compared with that of the traditional non-pilot traveling wave (TW) based protection method. The results confirm the high accuracy of the proposed protection method for hybrid transmission lines in VSC-HVDC systems, where a mean percentage error of approximately 0.1% is achieved.
dc.identifier.doi10.35833/MPCE.2023.000925
dc.identifier.endpage851
dc.identifier.issn2196-5625
dc.identifier.issn2196-5420
dc.identifier.issue3
dc.identifier.startpage840
dc.identifier.urihttp://dx.doi.org/10.35833/MPCE.2023.000925
dc.identifier.urihttps://hdl.handle.net/20.500.14669/4459
dc.identifier.volume13
dc.identifier.wosWOS:001499343700026
dc.indekslendigikaynakWeb of Science
dc.language.isoen
dc.publisherState Grid Electric Power Research Inst
dc.relation.ispartofJournal of Modern Power Systems and Clean Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20260302
dc.subjectProtection
dc.subjectPower conversion
dc.subjectConverters
dc.subjectFeature extraction
dc.subjectFault detection
dc.subjectArtificial neural networks
dc.subjectSignal processing
dc.subjectPower systems
dc.subjectHVDC transmission
dc.subjectElectrical fault detection
dc.subjectVoltage source converter based high-voltage direct current (VSC-HVDC)
dc.subjectprotection
dc.subjectfault detection
dc.subjectfault classification
dc.subjectfault location
dc.subjectempirical wavelet transform (EWT)
dc.subjectartificial neural network (ANN)
dc.subjecthybrid transmission line
dc.titleFault Detection, Classification, and Location Based on Empirical Wavelet Transform-Teager Energy Operator and ANN for Hybrid Transmission Lines in VSC-HVDC Systems
dc.typeArticle

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu