Neural networks for breakdown voltage estimation of various gas mixtures


Onal E.

ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS, vol.16, no.2, pp.73-78, 2008 (Journal Indexed in SCI) identifier

  • Publication Type: Article / Article
  • Volume: 16 Issue: 2
  • Publication Date: 2008
  • Title of Journal : ENGINEERING INTELLIGENT SYSTEMS FOR ELECTRICAL ENGINEERING AND COMMUNICATIONS
  • Page Numbers: pp.73-78

Abstract

In this paper, AC breakdown strengths of a Mixture of 99.875% CO(2)+0.125% SF(6) a nd those of N(2)+SF(6) mixtures containing 0.125, 0.5, 1% of SF(6) in non-uniform field are Studied. The relative gas pressure and the electrode gap spacing are varied within the range of 100-500 kPa and of 5-15 mm, respectively. The results are first measured experimentally and then estimated by means of Feedforward Neural Network Approach. The comparisons of measured and computed values show that there is a good agreement between two Values. The breakdown voltages of the mixtures can be found correctly by the Feedforward Neural Network (FNN) approach. Therefore, the Feed-Forward Neural Network Approach can be considered an alternative tool to estimate the new values in or out of the measurement range.