Applications of artificial intelligence and hybrid neural network methods with new bonding method to prevent electroshock risk and insulation faults in high-voltage underground cable lines

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dc.contributor.authorAkbal, Bahadir
dc.date.accessioned2020-03-26T19:52:55Z
dc.date.available2020-03-26T19:52:55Z
dc.date.issued2018
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe sheath current causes cable faults and electroshocks in the high-voltage underground cable lines. Thus, the sheath current must be determined before a new high-voltage cable line is installed to take the required precautions against the sheath current effects. In this study, PSCAD/EMTDC is used for simulation of high-voltage underground cable line to examine the sheath current. The sheath current is forecasted with artificial neural network (ANN) by using the results of simulation studies, and differential evolution algorithm (DEA), genetic algorithm (GA), clonal selection algorithm (CSA), hybrid DEA-CSA, artificial bee colony (ABC), particle swarm optimization (PSO) and inertia weight PSO (iPSO) are used to find the best weights of ANN. The sheath current formation factors which are obtained in PSCAD/EMTDC are used as inputs of ANN. It is seen at the end of simulation studies that many factors affect the formation of the sheath current, so Pearson correlation is used to determine the most effective sheath current formation factors for inputs of ANN. In the literature, three types of bonding methods are used to reduce the sheath current and voltage, but these methods are not sufficient to reduce both sheath current and the sheath voltage simultaneously. Therefore, in this study, the sectional solid bonding method is developed as new bonding method to reduce the sheath voltage and current in this study. When the sectional solid bonding is used, the sheath voltage can be reduced under touch voltage, and the sheath current can be reduced under the desired value.en_US
dc.identifier.doi10.1007/s00521-017-2860-8en_US
dc.identifier.endpage105en_US
dc.identifier.issn0941-0643en_US
dc.identifier.issn1433-3058en_US
dc.identifier.issue8en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage97en_US
dc.identifier.urihttps://dx.doi.org/10.1007/s00521-017-2860-8
dc.identifier.urihttps://hdl.handle.net/20.500.12395/36342
dc.identifier.volume29en_US
dc.identifier.wosWOS:000427799900008en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSPRINGER LONDON LTDen_US
dc.relation.ispartofNEURAL COMPUTING & APPLICATIONSen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial intelligenceen_US
dc.subjectNeural networken_US
dc.subjectHigh-voltage underground cableen_US
dc.subjectSheath currenten_US
dc.titleApplications of artificial intelligence and hybrid neural network methods with new bonding method to prevent electroshock risk and insulation faults in high-voltage underground cable linesen_US
dc.typeArticleen_US

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