Hybrid ANN Methods to Reduce The Sheath Current Effects in High Voltage Underground Cable Line

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dc.contributor.authorAkbal, Bahadir
dc.date.accessioned2020-03-26T19:24:32Z
dc.date.available2020-03-26T19:24:32Z
dc.date.issued2016
dc.departmentSelçuk Üniversitesien_US
dc.description4th International Istanbul Smart Grid Congress and Fair (ICSG) -- APR 20-21, 2016 -- Istanbul, TURKEYen_US
dc.description.abstractThe sheath current generates on metal sheath of underground cable, and it causes cable faults, electroshock risk and reducing of cable performance in high voltage underground cable line. Therefore, the sheath current must be reduced, and if the sheath current can be determined before high voltage underground line is installed, the required precautions can be taken to reduce the sheath current. Hence, cable faults and electroshock risk are prevented, and cable performance increases. In this study, simulations of high voltage underground cable line are made in PSCAD/EMTDC, and the sheath current is forecasted by using hybrid artificial neural network (ANN) method. Differential evolution algorithm (DEA) and particle swarm optimization (PSO) are used to generate hybrid ANN method. The results of hybrid ANN method are better than classic ANN, and the results of DEA-ANN method are better than PSO-ANN. Also DEA-ANN can be used in forecasting studiesen_US
dc.description.sponsorshipRepubl Turkey, Minist EU Affairs, Turkiye Cumhuriyeti Kultur Turizm Bakanligi, KOSGEB, TEDAS, TEIAS, Istanbul Buyuksehir Belediyesi, Turkish Electro Technol, Energy Business Council, Foreign Econ Relat Board, Istanbul Kanalizasyon Idaresi, BOTAS, IGDAS Gokyuzuyle Arkadas, Istanbul Ticaret Odasi, Istabul Sanayi Odasi, UHE, UFI, Elder, GAZBIR, TENVA, Turk Sanayici Isadamlari VAKFI, Organize Sanayi Bolgeleri Dernegi, Teknoloji Ar Ge Bilim Inouasyon Dernegi, TURKCELL, Vodafone, LUNA, STATUEAZ, SABAH, HITACHI, KOHLER, ORACLE, aselsan, ERICSSON, NETAS, SIEMENS, Microsoft, best, HHB EXPO, Republ Turkey, Minist Sci Ind & Technol, Republ Turkey, Minist Environm & Urbanisat, Republ Turkey, Minist Energy & Nat Resources, EPDK, Republ Turkey, Istanbul Metropolitan Municipal, Ugetam, IEEE SMARTGRID, IEEE Power & Energy Socen_US
dc.identifier.endpage46en_US
dc.identifier.isbn978-1-5090-0866-7
dc.identifier.scopusqualityN/Aen_US
dc.identifier.startpage42en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12395/33673
dc.identifier.wosWOS:000389660400007en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherIEEEen_US
dc.relation.ispartof2016 4TH INTERNATIONAL ISTANBUL SMART GRID CONGRESS AND FAIR (ICSG)en_US
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.selcuk20240510_oaigen_US
dc.subjectArtificial neural networken_US
dc.subjectdifferential evolution algorithmen_US
dc.subjecthigh voltage underground cable lineen_US
dc.subjectparticle swarm optimizationen_US
dc.subjectthe sheath currenten_US
dc.titleHybrid ANN Methods to Reduce The Sheath Current Effects in High Voltage Underground Cable Lineen_US
dc.typeConference Objecten_US

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