Epoxy nano–micro composite specimen prepared with micro silica and ion trapping nanoparticle, by shear mixing process, was exposed to gamma radiation and its performance for space charge and charge trap characteristics were analysed. The threshold for space charge accumulation of epoxy nanocomposites reduces and rate of space charge accumulation increases with an increase in dosage of gamma irradiation. The average growth of space charge density during poling and charge decay rate during depoling are relatively higher for gamma‐irradiated specimens than the virgin specimen. The initial surface potential has a marginal reduction with increase in the dosage of gamma radiation, but the surface potential decay rate has increased significantly. Trap distribution characteristics indicate more number of shallow traps and increase in charge mobility after irradiation. The relative permittivity and loss tangent of the specimens have high impact due to gamma irradiation. The activation energy calculated from DC conductivity by Arrhenius law reduces with increment in radiation dose. Laser‐induced breakdown spectroscopy reflected no change in elemental composition with gamma‐irradiated specimen. The variation in plasma temperature and ion line to atomic line intensity ratio with dosage of gamma radiation have direct correlation to the Vickers hardness number of the specimens.

Epoxy micro-nanocomposite specimens are subjected to different levels of gamma irradiation and water aging to analyze their degradation behavior after aging. The plasma temperature calculated from the laser-induced breakdown spectroscopy (LIBS) spectral data tends to decrease, and the crater depth after laser ablation tends to increase with increment in the level of aging, reflecting the reduction in surface hardness of the epoxy specimens after aging. Water aging creates a more severe impact on the surface hardness of the specimens, compared to gamma irradiation, in the range studied. The normalized intensity ratio of H(I)/Si(II) is in direct correlation with the water diffusion coefficient. Machine learning techniques, such as the principal component analysis (PCA) and the artificial neural network (ANN) analysis, have been performed on LIBS spectral data in order to classify the unaged, gamma-irradiated, and water-aged specimens. The number of neurons in the hidden layer of the neural network architecture has been optimized based on classification accuracy and the number of epochs required to converge. The ANN-adopted LIBS analysis is successful with good classification accuracy compared to PCA, for categorizing the level and type of aging in epoxy micro-nanocomposites as observed in this work.

This work reports an experimental and theoretical analysis of the rheological properties of mineral oil-based SiO ₂ nanofluid for their potential applications in transformer insulation. The flow electrification mechanism on the nanofluids with different surfactants such as cetyl trimethyl ammonium bromide (CTAB), oleic acid, and Span 80 is studied using a spinning disk technique. The results show a higher streaming current for the nanofluids with CTAB as a surfactant compared to oleic acid and Span 80. The rheological behavior of nanofluids is explored with the double gap concentric cylinder geometry. The variation of shear stress with shear rate follows a power law relationship along with a yield stress observed for all the nanofluids. A transition is seen from storage modulus to dominant loss modulus for the nanofluids during the frequency sweep analysis, whereas no transition is observed in the case of mineral oil. In addition, regression analysis using artificial neural network (ANN) algorithms are performed on the experimentally measured viscosity of the nanofluids in order to estimate theoretical parameters and provide insights into the streaming current formation. The desirable rheological characteristics of nanofluids are identified for achieving enhanced insulation performance in transformers.

Silicone rubber (SR) insulators have been coated with different concentrations of NaCl to study the contamination level adopting laser-induced breakdown spectroscopy (LIBS) analysis. An elemental study of LIBS spectral data has identified the sodium peaks, thereby indicating the presence of salt deposits. A regression coefficient is used for a better understanding of the direct correlation between the salt deposit density (SDD) and the normalized intensity ratio, at various standoff distances. A marginal decrement in the normalized intensity ratio of the sodium peaks has been noticed in the case of readings taken at morning and midday, compared to those taken at night at different SDD values and different energies of the laser pulse. With increase in SDD, the intensity distribution characteristics have a right shift on the intensity scale. The polluted SR specimens are successfully classified by employing artificial neural network technique. Overall, the LIBS method is successful in identifying the variations in the salt deposition on the surface of the insulator, even at far distances of 15 m and at any time of the day.

Silicone rubber (SR) micro nanocomposites formed with micro-ATH and nano-alumina particles have been subjected to different levels of gamma-ray irradiation to understand the characteristic variation in the surface condition of the material through contact angle measurement, Atomic Force Microscopy (AFM) studies, water droplet-initiated corona inception voltage (CIV) measurement and by laser-induced breakdown spectroscopy (LIBS) analysis. It is realized that the recovery rate of silicone rubber micro nanocomposites is less compared with the base SR material. It is observed that, irrespective of the level of irradiation, the contact angle and water droplet initiated CIV of the specimen have shown direct correlation. FTIR analysis clearly indicates variation in methyl group formation on irradiation is less with S2 specimen. It is also observed that fractal dimension calculated by three different techniques for the surface profile data obtained using AFM is in directly in proportion with average surface roughness. In addition, the lacunarity values of the irradiated samples followed the same pattern as that of the average surface roughness. The variation in thermal degradation temperature has been analyzed by adopting thermogravimetric analysis (TGA). The calculated plasma temperature values from laser-induced breakdown spectroscopy and the crater depth formed due to laser ablation have shown inverse relationship. Artificial neural network (ANN) has been employed successfully using LIBS data, to understand the level of irradiation of the SR samples. Overall, the silicone rubber micro-nanocomposite sample S2 filled with micro-ATH and nano Alumina has reflected relatively lesser degradation after being exposed to gamma irradiation, over other test specimens.