Epoxy nano-composites along with inorganic nanoparticles as filler, can exhibit excellent properties, such as ameliorated mechanical strength, improved thermal conductivity, enhanced dielectric strength, and reduced dielectric dissipation factors. Therefore, epoxy-based insulation is employed in power transformers, motors, generators, converter transformers, gas-insulated switchgear, and gas-insulated transmission lines. This article aims to review the dielectric and thermal properties of epoxy nano-composites with incorporated inorganic nano-fillers. Proper dispersion of nanoparticles inside the polymer matrix can effectively improve the dielectric strength and thermal properties of epoxy insulation. Excessive dispersion of nano-fillers can cause agglomeration and defects between the fillers and matrix, resulting in insulation problems such as higher dielectric loss and reduced dielectric strength. It is considered that the dispersion of nanoparticles in proper ratio can effectively improve the dielectric properties of epoxy insulation. Therefore, an augmented perception of the nanoparticle–epoxy interfaces in nano-composites can enable a pragmatic analysis of the desired properties, opening new arrays for application.

This letter aims at designing a convolution neural network (CNN)-based classifier that detects and classifies 13 different types of power system events causing poor power quality. Early and accurate detection of those classes of disturbances helps power engineers take quick and suitable measures to protect power system components and to supply uninterrupted power to the consumers. The whole process comprises of the sensing of disturbance creating current signals from experimental setup, filtration by unsupervised $k$ -mean machine learning to remove noisy data, Big Data generation, architectural modification of AlexNet CNN, time–frequency informative image creation by continuous wavelet transform followed by convolution using kernel filters, and classification and performance evaluation. The proposed customized CNN has been compared with other benchmark CNN architectures. The method has shown 98.46 ${\%}$ accuracy with minimum computational time.

This paper describes a practical method to sense and measure the polarization and depolarization current of a solid dielectric under high voltage excitation. The proposed method has the possibilities of evaluating the capacitance, dielectric dissipation factor (DDF) and conduction current from the measurand with reasonable accuracy as compared to the other conventional methods. The present method has inherent noise immunity in acquiring the current in picoampere (pA) range. The principle of the data acquisition technique of the present scheme and its implementation is discussed in detail in this paper. A comparative study between the proposed scheme and the conventional method has been also discussed. The result shows a good agreement between the proposed method and conventional complex setup.

In this contribution, a novel technique to accurately sense the thermal aging of the epoxy alumina nano-composites employing complex electric modulus is proposed. To sense the thermal ageing of epoxy nano-composites accurately, knowledge of relaxation behavior is necessary. However, relaxation behavior cannot be understood completely due to electrode polarization and charge transport effects at high temperatures and at low frequencies. To overcome this problem, electric modulus which is defined as the inverse of the complex permittivity, is being proposed in this study to analyze the aging behavior of the epoxy alumina nano-composites quantitatively. For this purpose, three epoxy resin samples mixed with alumina (Al ₂ O ₃ ) nano-fillers with different filler concentrations were prepared and thermal aging of the same samples was done for 100 hours, 200 hours, 300 hours and 400 hours, respectively. For each sample, the complex dielectric modulus (M*( ω)) was computed using frequency domain spectroscopy measurement to observe their frequency dependent relaxation behaviors. The variation of the real (M ^' ( ω)) and imaginary (M ^'' ( ω)) part of ( M*( ω)) over the frequency range from 1 mHz to 10 kHz was further fitted using Cole-Cole (C-C) model. From the nature of variation of M ^'' ( ω) spectrum and the fitting parameters of the C-C model, two characteristic parameters were extracted to quantitatively describe the thermal aging of the epoxy nano-composite samples. Investigations revealed that the extracted parameters can be accurately used to sense the aging condition of the epoxy nano-composites.

In this article, an advanced technique is applied to estimate the aging state of epoxy–alumina nanocomposite insulation. For this purpose, several epoxy–alumina nanocomposite samples are prepared in the laboratory. Samples are undergone into artificial electrothermal aging conditioning up to duration of 600 h. After each 100-h instant, the relaxation current is measured under 1-kV/mm dc stress through the developed experimental setup in the laboratory. From the experimentally measured current data, relaxation frequency distribution (RFD) function of the test samples is computed. Based on the relaxation distribution function, two aging sensitive parameters are evaluated. In order to correlate these two aging sensitive parameters with the aging state of insulation, two empirical relationships are proposed based on the experimental investigation. The effectiveness of these two empirical relationships is validated with the laboratory prepared epoxy nanocomposites (EPNCs) insulation having a definite aging state. The experimental investigation shows that the proposed method can effectively be applied for estimating the aging state of nanocomposites insulation used in real-life high-voltage equipment.

This paper investigate the dielectric properties of mixed oils (i.e. blending of mineral and vegetable oil) and their nanofluids at different temperatures by Time and Frequency Domain Spectroscopy. Semiconducting type nanoparticle, TiO ₂ (Titania) and insulating type nanoparticle, Al ₂ O ₃ (Alumina) are selected for dispersing within mixed oils to prepare mixed-oil based nanofluids. The quality of the prepared samples with different oils, are investigated by comparing the corresponding experimental results obtained through AC breakdown voltage, viscosity, and dielectric spectroscopy. From time domain spectroscopy, nonlinearity and activation energy are estimated of those prepared samples. Thereafter, a statistical regression model is proposed to investigate the temperature dependency of the dielectric parameters like real part of complex permittivity and tan δ. Breakdown voltage can be enhanced upto 27.13% with dispersion of TiO ₂ in the mixed oil. Again by dispersing Al ₂ O ₃ in mixed oil, viscosity and nonlinearity in the conduction current can be reduced nearly 34% and 54%, respectively whereas activation energy can be enhanced by 22.7%.