Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network (ANN)

Abstract

The performance of correlation between the dielectric parameters of Baobab Oil (BAO) and Mongongo Oil (MGO) is evaluated using Artificial Neural Network (ANN). The BAO and MGO naturally own high Unsaturated Fatty Acids (UFAs) and are highly biodegradable. The temperature studies and dielectric studies are carried out and found that the Natural Esters (NEs) show a reliable performance over mineral oil-based Transformer Oil (TO). Further the endurance test, Partial Discharge Inception Voltage (PDIV) repetition rate and drop after 30 days, dielectric measurements are done as per the standards of IEC (International Electrotechnical Commission) and ASTM (American Society for Testing and Materials). The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO. The C[Formula: see text]H[Formula: see text] or Kerosene (KER) and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation. The C[Formula: see text]H[Formula: see text] acts as a thinning agent and reduces the ageing rate of the NEs, and this leads to slower rate of water saturation. This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90[Formula: see text]C shows better strength of liquid insulation. The performance of association between the dielectric properties like breakdown voltage and water content, dissipation factor and thermal conductivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.