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

被引:2
|
作者
Yadav, Suman [1 ]
Chamorro, Harold R. [2 ]
Flores, Wilfredo C. [3 ]
Mehta, Ram Krishna [1 ]
机构
[1] North Eastern Reg Inst Sci & Technol, Dept Elect Engn, Nirjuli 791109, Arunachal Prade, India
[2] KTH Royal Inst Technol, Dept Elect Engn, S-10044 Stockholm, Sweden
[3] Univ Tecnol Centroamer UNITEC, Fac Ingn, Tegucigalpa 11101, Honduras
关键词
Insulators; Thermal conductivity; Conductors; Power transformer insulation; Conductivity; Oil insulation; Finite element analysis; Artificial neural network; electro-thermal analysis; finite element modelling; material performance; PSO; GWO; thermal conductivity; transformer bushing; OPTIMIZATION; DESIGN; INSULATION; PREDICTION; MODEL;
D O I
10.1109/ACCESS.2021.3124917
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页码:149196 / 149217
页数:22
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