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Byline: Gyanendra. Kumar, R. Sarathi, Archana. Sharma Background: Electro-permeablization using a pulse generator is a novel non-invasive approach for cancer therapy. It serves as a cell permeability enhancing agent for [...]

One of the major issues with natural ester fluid as transformer insulation is encountered in the oxidation process, which forms a thin gel layer due to polymerization. This latter can hinder the electrical and heat transfer properties. In order to understand the process, methodical thermal aging of natural esters is carried out for a 500-h duration under atmospheric conditions at 160 °C. The characteristic variation of fluid-forming gel is investigated by nuclear magnetic resonance (NMR) with thermal aging, through which molecular weight degradation and iodine index of thermally aged oil are estimated for transformer applications. The molecular weight and iodine index of thermally aged oil are reduced to 8.31% and 43.016%, respectively, after 500 h of aging. Thermogravimetric analysis (TGA) is carried out to further comprehend this, and heating calorific value is estimated through bomb calorimetric studies. Heat calorific value (HV) for 500-h aged oil and gel is nearly similar. The electrical discharge performance of the oil is estimated through corona inception voltage (CIV) studies under nonuniform electric field configuration adopting ultrahigh frequency (UHF) sensor techniques. The iodine index is in direct correlation with the CIV measurement.

The present study investigates the performance of nanofluid-impregnated pressboard material for surface discharges. Surface discharge is simulated as per IEC 60112 standard with nano TiO2 dispersed mineral oil-impregnated pressboard. During surface discharge, multiple activities, such as localized surface discharge inception followed by bright light occurrences, bubble formation, and bridging of electrodes, occur over an extended period of time. The surface discharge activity under ac and transient voltage profiles was monitored by adopting ultrahigh-frequency (UHF) and optical detection sensors. The pressboard material impregnated with nanoparticle dispersed fluid shows that 9.7% improvement is surface discharge inception voltage (SDIV) compared to pressboard impregnated with pure transformer oil. Furthermore, three machine learning (ML) algorithms, support vector machine (SVM), random forest (RF), and k-nearest neighbor (kNN) algorithms were used to evaluate the best-fit model for classifying the various stages of the surface discharge process. Classification accuracy is achieved to be 97.3% with the optimized parameters. Laser-induced breakdown spectroscopy (LIBS) identifies that the elemental composition at the discharge sites on the pressboard and the plasma temperature of carbon traces were evaluated. Plasma temperature for lightning impulse treated is lower due to the instantaneous breakdown occurring in the surface of the pressboard in comparison to the ac voltage profile.