Breakdown strength estimation of air and a mixture of air+SF6 using artificial neural network approach

Kevork Mardikyan; Serhat Seker; Emine Ayaz

Breakdown strength estimation of air and a mixture of air+SF₆ using artificial neural network approach

Kevork Mardikyan^*
, Serhat Seker
, Emine Ayaz

^*Corresponding author for this work

Department of Electrical Engineering

Istanbul Technical University

Research output: Contribution to journal › Article › peer-review

Abstract

The breakdown strength of dry air and a mixture of dry air + SF₆ containing 1% SF6 in non-uniform field was studied in a pressure range of 50 to 450 kPa. Addition of 1% of SF₆ to dry air increased considerably the breakdown voltage in a non-uniform field. An Artificial Neural Network (ANN) gave a very good performance in the extrapolation of breakdown voltage values above the measuring range.

Original language	English
Pages (from-to)	1-14
Number of pages	14
Journal	Advances in Modelling and Analysis B
Volume	43
Issue number	1-2
Publication status	Published - 2000

Cite this

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title = "Breakdown strength estimation of air and a mixture of air+SF6 using artificial neural network approach",

abstract = "The breakdown strength of dry air and a mixture of dry air + SF6 containing 1\% SF6 in non-uniform field was studied in a pressure range of 50 to 450 kPa. Addition of 1\% of SF6 to dry air increased considerably the breakdown voltage in a non-uniform field. An Artificial Neural Network (ANN) gave a very good performance in the extrapolation of breakdown voltage values above the measuring range.",

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AU - Ayaz, Emine

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N2 - The breakdown strength of dry air and a mixture of dry air + SF6 containing 1% SF6 in non-uniform field was studied in a pressure range of 50 to 450 kPa. Addition of 1% of SF6 to dry air increased considerably the breakdown voltage in a non-uniform field. An Artificial Neural Network (ANN) gave a very good performance in the extrapolation of breakdown voltage values above the measuring range.

AB - The breakdown strength of dry air and a mixture of dry air + SF6 containing 1% SF6 in non-uniform field was studied in a pressure range of 50 to 450 kPa. Addition of 1% of SF6 to dry air increased considerably the breakdown voltage in a non-uniform field. An Artificial Neural Network (ANN) gave a very good performance in the extrapolation of breakdown voltage values above the measuring range.

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JO - Advances in Modelling and Analysis B

JF - Advances in Modelling and Analysis B

IS - 1-2

ER -

Breakdown strength estimation of air and a mixture of air+SF₆ using artificial neural network approach

Abstract

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