ARTIFICIAL NEURAL NETWORK PREDICTION PARAMETERS OF 10%SF6-90%N2 MIXTURE

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Authors

  • FAISSEL BELOUCIF LGEG Laboratory, Université 8 mai 1945 Guelma. BP.401, Algeria
  • AMAR BOUDEFEL LGEG Laboratory, Université 8 mai 1945 Guelma. BP.401, Algeria
  • ASSIA GUERROUI LGEG Laboratory, Université 8 mai 1945 Guelma. BP.401, Algeria
  • AHCENE LEMZADMI LGEG Laboratory, Université 8 mai 1945 Guelma. BP.401
  • ABDELKRIM MOUSSAOUI LGEG Laboratory, Université 8 mai 1945 Guelma. BP.401

Keywords:

Sulfur hexafluoride, Gas mixture SF6-N2, Onset voltages, Corona discharges, Ionic mobility

Abstract

The present study outlines the application of Artificial Neural Networks for the Prediction of corona discharge parameters in SF6-N2 gas mixture. The artificial neural networks modeling is used to predict corona discharge temperature, ionic mobility, and onset voltages for different gas pressures with a mixture of 10% SF6 - 90% N2, and using experimental data obtained previously. The results of artificial neural networks' prediction of ionic mobility (μ) onset voltages (Vs) and temperature are found to be around ± 6% for training as well as for testing and are significantly consistent with the experimental values.

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Published

01.07.2022

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Section

Électrotechnique et électroénergétique / Electrical and Power Engineering