Research Article Open Access

Epoxy Insulators’ Lifetime Prediction Implementing Neural Network Technique

L. S. Nasrat1 and A. M. Ibrahim2
  • 1 South Valley University, Egypt
  • 2 Ain Shams University, Egypt
American Journal of Engineering and Applied Sciences
Volume 5 No. 2, 2012, 157-162

DOI: https://doi.org/10.3844/ajeassp.2012.157.162

Submitted On: 26 May 2012 Published On: 7 August 2012

How to Cite: Nasrat, L. S. & Ibrahim, A. M. (2012). Epoxy Insulators’ Lifetime Prediction Implementing Neural Network Technique. American Journal of Engineering and Applied Sciences, 5(2), 157-162. https://doi.org/10.3844/ajeassp.2012.157.162

Abstract

Due to wide implementation of Epoxy insulators in industrial applications and its economic implications; development of various Epoxy insulator materials has to be evaluated along with a reliable prediction methodology of their lifetimes. In this study, a new methodology based on Artificial-Neural-Networks (ANN) is developed to predict Epoxy insulators lifetime using laboratory measurements of their surface leakage current under accelerated aging. The effect of adding fillers with various concentration rates to the Epoxy insulators such as; Calcium Silicate (CaSiO2), Mica and Magnesium Oxide (Mg(OH)2) on their lifetimes is compared with the base case (no filler and dry condition). Furthermore, the lifetime of each specimen under study is examined under various weather conditions such as dry, wet, salt wet (NaCl) and hydro carbon solvent Naphtha. The obtained results are weighing against the experimental measured data based on two ANN techniques; i.e., Feed-Forward-Neural-Network (FNN) and Recurrent-Neural-Network (RNN). The results obtained from the FNN and RNN are compared to validate the proposed methodology to predict the lifetime of epoxy insulators in terms of the type and percentage concentration of filler. The obtained Epoxy insulators predicted lifetime under various filler concentrations and weather conditions are compared and conclusions are reported.

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Keywords

  • Recurrent-Neural-Network (RNN)
  • Feed-Forward-Neural-Network (FNN)
  • Artificial-Neural-Networks (ANN)
  • Processing Elements (PE)