Optimum cable bonding with pareto optimal and hybrid neural methods to prevent high-voltage cable insulation faults in distributed generation systems

dc.contributor.authorAkbal, Bahadır
dc.date.accessioned2025-02-07T13:56:59Z
dc.date.available2025-02-07T13:56:59Z
dc.date.issued2024
dc.departmentMühendislik Fakültesi
dc.description.abstractThe high voltage, current and harmonic distortion in high-voltage cable metal sheaths cause cable insulation faults. The SSBLR (Sectional Solid Bonding with Inductance (L) and Resistance) method was designed as a new cable grounding method to prevent insulation faults. SSBLR was optimized using multi-objective optimization (MOP) with the prediction method (PM) to minimize these factors. The Pareto optimal method was used for MOP. The artificial neural network, hybrid artificial neural network and regression methods were used as the PM. When the artificial neural network–genetic algorithm hybrid method was used as the PM, and the genetic algorithm was used as the optimization method, the voltage and current were significantly reduced in the metal sheath of the cable.
dc.identifier.doi10.3390/pr12122909
dc.identifier.issn2227-9717
dc.identifier.issue12en_US
dc.identifier.scopusqualityQ2
dc.identifier.urihttps://dx.doi.org/10.3390/pr12122909
dc.identifier.urihttps://hdl.handle.net/20.500.12451/12940
dc.identifier.volume12en_US
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.ispartofMultidisciplinary Digital Publishing Institute (MDPI)
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Başka Kurum Yazarı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectMulti-objective Optimization
dc.subjectHybrid Neural Network
dc.subjectOptimization
dc.subjectHigh-Voltage Cable Grounding
dc.titleOptimum cable bonding with pareto optimal and hybrid neural methods to prevent high-voltage cable insulation faults in distributed generation systems
dc.typeArticle

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