Optimum cable bonding with pareto optimal and hybrid neural methods to prevent high-voltage cable insulation faults in distributed generation systems
Yükleniyor...
Dosyalar
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Multidisciplinary Digital Publishing Institute (MDPI)
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
The 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.
Açıklama
Anahtar Kelimeler
Multi-objective Optimization, Hybrid Neural Network, Optimization, High-Voltage Cable Grounding
Kaynak
Multidisciplinary Digital Publishing Institute (MDPI)
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
12
Sayı
12
