DocumentCode :
3524542
Title :
Prediction of breakdown voltages in N2 + SF6 gas mixtures
Author :
Tezcan, S.S. ; Dincer, M.S. ; Hiziroglu, H.R.
Author_Institution :
Dept. of Electr. & Electron. Eng., Gazi Univ., Ankara
fYear :
2006
fDate :
15-18 Oct. 2006
Firstpage :
222
Lastpage :
225
Abstract :
This study proposes artificial neural networks (ANN) to predict the breakdown voltages in N2 + SF6 gas mixtures. The proposed ANN consists of one input layer, two hidden layers and one output layer, which is essentially the predicted breakdown voltage. In order to train the ANN, the experimental data available in literature for N2 + SF6 have been used. When compared with the experimental data the average relative errors on predicted breakdown voltages are found to be less than plusmn5% for training as well as for testing in all cases using the proposed ANNs. Since the average errors are less than 5%, it is recommended to use the proposed ANNs to predict the breakdown voltages.
Keywords :
electric breakdown; gas mixtures; gaseous insulation; neural nets; nitrogen; power engineering computing; sulphur compounds; N2; SF6; artificial neural networks; breakdown voltages; gas mixtures; gaseous insulating medium; power industry; Artificial neural networks; Biological neural networks; Central nervous system; Computer networks; Dielectrics and electrical insulation; Gas insulation; Input variables; Neurons; Power industry; Sulfur hexafluoride;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electrical Insulation and Dielectric Phenomena, 2006 IEEE Conference on
Conference_Location :
Kansas City, MO
Print_ISBN :
1-4244-0546-7
Electronic_ISBN :
1-4244-0547-5
Type :
conf
DOI :
10.1109/CEIDP.2006.312101
Filename :
4105409
Link To Document :
بازگشت