Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM

The heat dissipation factor of conventional epoxy impregnated paper bushings is a subject of concern due to the large quantities of power in a High Voltage Direct Current (HVDC) system. The present work deals with the selection of better insulation as a replacement to the conventional resin impregna...

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Autores principales: Suman Yadav, Harold R. Chamorro, Wilfredo C. Flores, Ram Krishna Mehta
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Lenguaje:EN
Publicado: IEEE 2021
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spelling oai:doaj.org-article:e82fc72d833744208fd75a76f7f46d8f2021-11-18T00:10:56ZInvestigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM2169-353610.1109/ACCESS.2021.3124917https://doaj.org/article/e82fc72d833744208fd75a76f7f46d8f2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9598868/https://doaj.org/toc/2169-3536The heat dissipation factor of conventional epoxy impregnated paper bushings is a subject of concern due to the large quantities of power in a High Voltage Direct Current (HVDC) system. The present work deals with the selection of better insulation as a replacement to the conventional resin impregnated material employing nano-hexagonal-Boron Nitride and nano-hexagonal-Boron Nitride added with nano-cellulose-fiber. The bushing of the converter transformer is designed using the Finite Element Method (FEM), and the electrothermal analysis is performed at the loaded working condition. Besides, numerous optimization schemes are also presented for adapting the structure of the thermal conductor enclosed in the inner conductor. The electrothermal performances of the above materials with the optimized structure are compared and an advanced scheme is proposed. Further, the results obtained from the designed system are employed in the form of an Artificial Neural Network to simplify the process of thermal computation characterized by the selected scheme. The internal parameters of the neural network are tuned implementing a hybrid amalgamation of Particle Swarm optimization - Grey Wolf Optimiser and the performance is compared against the actual values. The supremacy of the implemented algorithm is justified by a comparative analysis with other well-established algorithms using various statistical parameters.Suman YadavHarold R. ChamorroWilfredo C. FloresRam Krishna MehtaIEEEarticleArtificial neural networkelectro-thermal analysisfinite element modellingmaterial performancePSOGWOElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149196-149217 (2021)
institution DOAJ
collection DOAJ
language EN
topic Artificial neural network
electro-thermal analysis
finite element modelling
material performance
PSO
GWO
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
spellingShingle Artificial neural network
electro-thermal analysis
finite element modelling
material performance
PSO
GWO
Electrical engineering. Electronics. Nuclear engineering
TK1-9971
Suman Yadav
Harold R. Chamorro
Wilfredo C. Flores
Ram Krishna Mehta
Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
description The heat dissipation factor of conventional epoxy impregnated paper bushings is a subject of concern due to the large quantities of power in a High Voltage Direct Current (HVDC) system. The present work deals with the selection of better insulation as a replacement to the conventional resin impregnated material employing nano-hexagonal-Boron Nitride and nano-hexagonal-Boron Nitride added with nano-cellulose-fiber. The bushing of the converter transformer is designed using the Finite Element Method (FEM), and the electrothermal analysis is performed at the loaded working condition. Besides, numerous optimization schemes are also presented for adapting the structure of the thermal conductor enclosed in the inner conductor. The electrothermal performances of the above materials with the optimized structure are compared and an advanced scheme is proposed. Further, the results obtained from the designed system are employed in the form of an Artificial Neural Network to simplify the process of thermal computation characterized by the selected scheme. The internal parameters of the neural network are tuned implementing a hybrid amalgamation of Particle Swarm optimization - Grey Wolf Optimiser and the performance is compared against the actual values. The supremacy of the implemented algorithm is justified by a comparative analysis with other well-established algorithms using various statistical parameters.
format article
author Suman Yadav
Harold R. Chamorro
Wilfredo C. Flores
Ram Krishna Mehta
author_facet Suman Yadav
Harold R. Chamorro
Wilfredo C. Flores
Ram Krishna Mehta
author_sort Suman Yadav
title Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
title_short Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
title_full Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
title_fullStr Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
title_full_unstemmed Investigation of Improved Thermal Dissipation of ±800 kV Converter Transformer Bushing Employing Nano-Hexagonal Boron Nitride Paper Using FEM
title_sort investigation of improved thermal dissipation of ±800 kv converter transformer bushing employing nano-hexagonal boron nitride paper using fem
publisher IEEE
publishDate 2021
url https://doaj.org/article/e82fc72d833744208fd75a76f7f46d8f
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