VSC MT-HVDC Fault Identification Based on the VMD-TEO and Artificial Neural Network

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dc.contributor.authorFarkhani, Jalal S.
dc.contributor.authorCelik, Ozgur
dc.contributor.authorMa, Kaiqi
dc.contributor.authorBak, Claus Leth
dc.contributor.authorChen, Zhe
dc.date.accessioned2025-01-06T17:29:47Z
dc.date.available2025-01-06T17:29:47Z
dc.date.issued2023
dc.description2nd International Conference on Power Systems and Electrical Technology, PSET 2023 -- 25 August 2023 through 27 August 2023 -- Milan -- 195631
dc.description.abstractThis paper presents a single-ended protection scheme based on an artificial neural network (ANN) and a variational mode decomposition (VMD) for the voltage source converter (VSC) multiterminal high voltage DC (MT-HVDC) system. The developed technique depends on the utilization of VMD for feature extraction of measured voltage and current signals which are transferred into the intrinsic mode function (IMF). Then the Teager energy operator (TEO) is applied for tracking the energy content of the IMFs. The energy content of the IMFs is fed into the multilayer perceptron neural network (MLP) neural network for the identification of faults in a test system. The effectiveness of the proposed method is verified on a four-terminal HVDC system with several case studies including high impedance faults (HIFs), and disturbances by using the PSCAD/EMTDC software. The developed ANN-based fault identification method provides promising results in terms of accurate and fast fault detection features. Simulation results verify the accuracy of the proposed protection method for VSC MT-HVDC systems. © 2023 IEEE.
dc.identifier.doi10.1109/PSET59452.2023.10346558
dc.identifier.endpage224
dc.identifier.isbn979-835033970-3
dc.identifier.scopus2-s2.0-85182391507
dc.identifier.startpage219
dc.identifier.urihttps://doi.org/10.1109/PSET59452.2023.10346558
dc.identifier.urihttps://hdl.handle.net/20.500.14669/1356
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relation.ispartof2023 2nd International Conference on Power Systems and Electrical Technology, PSET 2023
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectArtificial neural network (ANN)
dc.subjectProtection systems
dc.subjectTeager energy operator (TEO)
dc.subjectVariational mode decomposition (VMD)
dc.subjectVSC MT-HVDC
dc.titleVSC MT-HVDC Fault Identification Based on the VMD-TEO and Artificial Neural Network
dc.typeConference Object

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