DocumentCode :
1094625
Title :
A model to predict current distributions in heavy current parallel conductor configurations
Author :
Ghandakly, Adel A. ; Curran, Richard L.
Author_Institution :
Dept. of Electr. Eng., Toledo Univ., OH, USA
Volume :
30
Issue :
2
fYear :
1994
Firstpage :
240
Lastpage :
244
Abstract :
This paper presents a model for predicting the current distribution in high current cables consisting of relatively widely spaced parallel conductors. These cables are typically used in electric glass melters to interconnect the power transformers and the melter secondary bus installations. Due to mutual inductive coupling between the conductors, electromagnetic forces will cause a nonuniform current distribution. The proposed model has been developed to account for these forces in specified parallel conductor configurations. The model also takes into account the “skin effect” impact on the individual conductor resistances and self inductances. The model is coded in a simple computer program which can be used to predict current distributions in electric glass melters and similar heavy current applications. Results obtained using the proposed model for a Scott-T transformer system with a variety of multiconductor cable configurations are presented in the paper for demonstration purposes
Keywords :
current distribution; digital simulation; electric heating; glass industry; melting; power cables; power engineering computing; skin effect; Scott-T transformer system; bundled conductors; computer program; conductor resistances; current distributions; electric glass melters; electromagnetic forces; heavy current parallel conductor configurations; melter secondary bus installations; multiconductor cable configurations; mutual inductive coupling; nonuniform current distribution; power transformers; self inductances; skin effect; widely spaced parallel conductors; Cables; Conductors; Current distribution; Distributed computing; Electromagnetic coupling; Electromagnetic forces; Glass; Mutual coupling; Power transformers; Predictive models;
fLanguage :
English
Journal_Title :
Industry Applications, IEEE Transactions on
Publisher :
ieee
ISSN :
0093-9994
Type :
jour
DOI :
10.1109/28.287538
Filename :
287538
Link To Document :
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