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2013 | 123 | 2 | 250-253
Article title

Discharge Currents Discrimination Technique Based on Multi-Linear Regression Line and Artificial Neural Networks for Power Transformers Diagnosis

Content
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Languages of publication
EN
Abstracts
EN
The proposed work will be consecrated to the study of positive pre-breakdown currents triggered in mineral transformer oil under 50 Hz alternating overvoltage. Since negative currents are recorded in low rates and for higher voltage levels than positive ones, only the latter will be prior taken into consideration. Both streamer propagation and arc discharge current types are identified and are used in the training process of an artificial neural network and the multi-linear regression line of these currents in order to develop a complementary diagnosis tool that can serve as an on-line transformer protection. More successful results than those obtained by other developed techniques are expected.
Keywords
EN
Contributors
author
  • Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers, 16311, Algeria
author
  • Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers, 16311, Algeria
author
  • Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers, 16311, Algeria
author
  • Laboratory of Electrical and Industrial Systems, FEI, USTHB, BP 32 Bab Ezzouar, Algiers, 16311, Algeria
author
  • Ecole Centrale de Lyon, Ampere CNRS UMR 5005, 36 av. Guy de Collongue, 69134 Ecully, France
References
  • [1] A. Beroual, M. Zahn, A. Badent, K. Kist, A.J. Schwabe, H. Yamashita, K. Yamazawa, M. Danikas, W.D. Chadband, Y. Torshin, IEEE Electr. Insul. Mag. 14, 6 (1998)
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  • [13] L. Lundgaard, D. Linhjel, G. Berg, S. Sigmond, in: 12th Int. Conf. on Conduction and Breakdown in Dielectric Liquids, ICDL'96, Roma (Italy), Rome 1996, p. 175
  • [14] C.T. Lin, C.S.G. Lee, in: Neural Fuzzy Systems, Prentice-Hall, Englewood Cliffs 1996, p. 205
Document Type
Publication order reference
Identifiers
YADDA identifier
bwmeta1.element.bwnjournal-article-appv123n2030kz
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