Forecasting applications of the sheath current of high voltage cable with artificial neural network based hybrid methods

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dc.contributor.authorAkbal, Bahadir
dc.date.accessioned2020-03-26T19:41:47Z
dc.date.available2020-03-26T19:41:47Z
dc.date.issued2017
dc.departmentSelçuk Üniversitesien_US
dc.description.abstractThe sheath current causes cable faults and electroshock risk in high voltage underground cable lines. Also the sheath current increases cable temperature and it reduces cable ampacity. Hence, cable performance decreases due to the sheath current. Different precautions can be taken to reduce the sheath current effects in high voltage underground cable line. However, primarily the sheath current must be detected at the project phase of high voltage underground cable line. In literature, artificial neural networks are used for forecasting studies. In this study, artificial neural network (ANN) is used with particle swarm optimization, particle swarm optimization with inertia weight and genetic algorithm to generate hybrid ANN methods for forecasting of the sheath current. High voltage underground cable line is modeled in PSCAD/EMTDC to measure the sheath current of different high voltage underground lines, and the obtained data from PSCAD/EMTDC are used to train artificial neural network based hybrid methods to forecast the sheath current of any high voltage underground cable line. When particle swarm optimization with inertia weight is used with artificial neural network, hybrid ANN-iPSO method is developed. The results of ANN-iPSO are better than the results of ANN-GA and ANN-PSO. If ANN-iPSO is used to determine the sheath current, the sheath current of high voltage underground cable line can be determined at the project phase of high voltage underground cable line. Hence, the most suitable precautions can be implemented, and cable faults and electroshock risk can be prevented, also cable performance is increased in high voltage underground cable line.en_US
dc.identifier.doi10.5505/pajes.2016.84669en_US
dc.identifier.endpage125en_US
dc.identifier.issn1300-7009en_US
dc.identifier.issn2147-5881en_US
dc.identifier.issue2en_US
dc.identifier.pmid#YOKen_US
dc.identifier.startpage119en_US
dc.identifier.urihttps://dx.doi.org/10.5505/pajes.2016.84669
dc.identifier.urihttps://hdl.handle.net/20.500.12395/35135
dc.identifier.volume23en_US
dc.identifier.wosWOS:000443167700004en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.language.isotren_US
dc.publisherPAMUKKALE UNIVen_US
dc.relation.ispartofPAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISIen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectHigh voltage underground cableen_US
dc.subjectArtificial neural networken_US
dc.subjectOptimization methodsen_US
dc.subjectThe sheath currenten_US
dc.subjectHybrid methodsen_US
dc.titleForecasting applications of the sheath current of high voltage cable with artificial neural network based hybrid methodsen_US
dc.typeArticleen_US

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