(untitled) (REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE, 2022)
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.