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.
Highlights
Electrical aging of polymer insulated materials is still poorly known phenomena and the physical sense of the various parameters involved in the aging models is far from being obvious (Crine, 2007; 2005)
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
Artificial Neural Networks (ANN) are defined in (Tsoukalas and Uhrig, 1997; Rajasekaran and Pai, 2004) as a data processing system consisting of a large number of simple highly interconnected processing elements in architecture inspired by the structure of cerebral cortex of the brain
Summary
Electrical aging of polymer insulated materials is still poorly known phenomena and the physical sense of the various parameters involved in the aging models is far from being obvious (Crine, 2007; 2005). The long-term characteristics of the mechanical, electrical and contamination characteristics of the material have not been sufficiently clarified and the establishment of its assessment and diagnostic methods is desired (Hackam, 1998) Both the mechanical and electrical properties of polymers can be further improved or modified by the addition of inorganic fillers. The current problems in engineering applications of epoxy thermosets include low stiffness, strength and the exothermic heat generated by the curing of epoxy resins that causes serious processing difficulties (Dean et al, 2003) These characteristics can be further improved by adding inorganic fillers that increase the mechanical strength and change the electrical properties of the composites (Ng et al, 2001). Additives are often used to modify the properties and characteristics of materials that
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