Please use this identifier to cite or link to this item:
Title: Designing of High Voltage Cable Bonding with Intelligence Algorithms to Avoid Cable Insulation Faults and Electroshock in High Voltage Lines
Authors: Akbal, B.
Keywords: High Voltage Cable Bonding
Hybrid Artificial Neural Network
Issue Date: 2023
Publisher: University of Kuwait
Abstract: Insulation faults are major problems in high-voltage cable lines. The major factors in insulation faults are the harmonic currents and the metal sheath voltage (MV) that occur on the metal sheath of cables. MV and harmonic distortion should be minimized to prevent insulation faults. Thus, sectional solid bonding with different grounding resistance (SSBr) methods has been developed as a new bonding method for minimizing harmonic current and MV. In addition, SSBr should be optimized by optimizing the minimum MV and harmonic distortion rate of high-voltage cables. Inertia-weighted particle swarm optimization (iPSO), particle swarm optimization (PSO), genetic algorithm (GA), and differential evolution algorithm (DEA) are used for the optimization of SSBr, and three groups of prediction methods are used separately as objective functions of the optimization methods to determine the minimum MV and harmonic distortion; these groups include neural networks, hybrid neural networks, and regression methods. Hybrid neural network with inertia-weighted particle swarm optimization (H-iPSO), linear regression, and feedforward backpropagation neural networks were selected from their groups according to training errors. Solid bonding method, which is widely used for bonding high-voltage cables, is simulated in this study. When solid bonding is used, the maximum harmonic distortion rate is measured as 8.15 %, and the maximum MV is measured as 1086 V. When H-iPSO is used as the prediction method and PSO is used as the optimization method, the maximum harmonic distortion rate is measured as 5.28 %, and the maximum MV is measured as 57 V. Both insulation fault and electroshock can be prevented by the optimized SSBr method. © 2023 University of Kuwait. All rights reserved.
ISSN: 2307-1877
Appears in Collections:Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections

Show full item record

CORE Recommender

Google ScholarTM



Items in GCRIS Repository are protected by copyright, with all rights reserved, unless otherwise indicated.