Breakdown voltage estimation of SF6+N2 mixture using Neural Network Approach

E. Onal; S. Seker; O. Kalenderli; K. Mardikyan

Breakdown voltage estimation of SF₆+N₂ mixture using Neural Network Approach

E. Onal^*, S. Seker, O. Kalenderli, K. Mardikyan

^*Corresponding author for this work

Department of Electrical Engineering

Istanbul Technical University

Research output: Contribution to journal › Conference article › peer-review

1 Citation (Scopus)

Abstract

In this paper, AC breakdown strengths of SF₆+N₂ mixtures containing 0.125, 0.5, 1% of SF₆ in non-uniform field are studied. The relative gas pressure are varied within the range of 100-500 kPa and the electrode gap spacing is 5 mm. The results are first measured experimentally and then estimated by means of Neural Network Approach. The comparison of measured and computed values shows that there is a good agreement between two values. The breakdown voltages of the mixtures can be found correctly by the Neural Network (NN) Approach. Therefore, the Neural Network Approach can be considered as an alternative tool to estimate the new values at the out of the measurement range.

Original language	English
Pages (from-to)	166-169
Number of pages	4
Journal	IEE Conference Publication
Issue number	473
Publication status	Published - 2000
Event	8th International Conference on Materials, Measurements and Applications - Edinburgh, UK Duration: 17 Sept 2000 → 21 Sept 2000

Cite this

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title = "Breakdown voltage estimation of SF6+N2 mixture using Neural Network Approach",

abstract = "In this paper, AC breakdown strengths of SF6+N2 mixtures containing 0.125, 0.5, 1% of SF6 in non-uniform field are studied. The relative gas pressure are varied within the range of 100-500 kPa and the electrode gap spacing is 5 mm. The results are first measured experimentally and then estimated by means of Neural Network Approach. The comparison of measured and computed values shows that there is a good agreement between two values. The breakdown voltages of the mixtures can be found correctly by the Neural Network (NN) Approach. Therefore, the Neural Network Approach can be considered as an alternative tool to estimate the new values at the out of the measurement range.",

author = "E. Onal and S. Seker and O. Kalenderli and K. Mardikyan",

year = "2000",

language = "English",

pages = "166--169",

journal = "IEE Conference Publication",

issn = "0537-9989",

publisher = "Institution of Engineering and Technology",

number = "473",

note = "8th International Conference on Materials, Measurements and Applications ; Conference date: 17-09-2000 Through 21-09-2000",

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TY - JOUR

T1 - Breakdown voltage estimation of SF6+N2 mixture using Neural Network Approach

AU - Onal, E.

AU - Seker, S.

AU - Kalenderli, O.

AU - Mardikyan, K.

PY - 2000

Y1 - 2000

N2 - In this paper, AC breakdown strengths of SF6+N2 mixtures containing 0.125, 0.5, 1% of SF6 in non-uniform field are studied. The relative gas pressure are varied within the range of 100-500 kPa and the electrode gap spacing is 5 mm. The results are first measured experimentally and then estimated by means of Neural Network Approach. The comparison of measured and computed values shows that there is a good agreement between two values. The breakdown voltages of the mixtures can be found correctly by the Neural Network (NN) Approach. Therefore, the Neural Network Approach can be considered as an alternative tool to estimate the new values at the out of the measurement range.

AB - In this paper, AC breakdown strengths of SF6+N2 mixtures containing 0.125, 0.5, 1% of SF6 in non-uniform field are studied. The relative gas pressure are varied within the range of 100-500 kPa and the electrode gap spacing is 5 mm. The results are first measured experimentally and then estimated by means of Neural Network Approach. The comparison of measured and computed values shows that there is a good agreement between two values. The breakdown voltages of the mixtures can be found correctly by the Neural Network (NN) Approach. Therefore, the Neural Network Approach can be considered as an alternative tool to estimate the new values at the out of the measurement range.

UR - http://www.scopus.com/inward/record.url?scp=0034539075&partnerID=8YFLogxK

M3 - Conference article

AN - SCOPUS:0034539075

SN - 0537-9989

SP - 166

EP - 169

JO - IEE Conference Publication

JF - IEE Conference Publication

IS - 473

T2 - 8th International Conference on Materials, Measurements and Applications

Y2 - 17 September 2000 through 21 September 2000

ER -

Breakdown voltage estimation of SF₆+N₂ mixture using Neural Network Approach

Abstract

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