The long-term safe and stable operation of oil-impregnated paper (OIP) bushings is of great significance to the operation of power systems. With the growth of OIP bushing, its internal insulation will gradually decay. Aramid insulation paper has excellent thermal aging characteristics and its insulation performance can be improved by using nano-modification technology. In this paper, the nano-SiO2 particles were used as the modified additives, and the modified aramid insulation paper was prepared through four steps: ultrasonic stirring, fiber dissociation, paper sample copying and superheated calendering. The microscopic physical morphology and chemical components of the insulation specimens before and after modification were analyzed by atomic force microscopy (AFM), scanning electron microscopy (SEM) and X-ray photoelectron spectroscopy (XPS), and an OIP bushing model based on the modified aramid insulation paper was constructed and its electric field distribution was analyzed. The simulation results show that the use of SiO2-modified aramid insulation paper can improve the electric field distribution of OIP bushings and increase the operating life of power transformers.

Transformers are an essential part of power production. Insulating paper began to be widely used in transformers in the 1990s. The superior aramid nanofiber as the matrix gives the aramid nano-insulating paper excellent mechanical properties, insulation performance, temperature resistance, and flexibility. At first, the heat resistance and service life of insulating paper should be satisfied for use in electrical equipment. With the continuous development of power equipment, people have put forward higher requirements on the properties of insulating paper, especially heat resistance and electrical properties. Insulation paper made of aramid fibers have better thermal stability and more advantages in electrical and mechanical properties, which can significantly improve the service life and safety of electrical appliances. The purpose of this article is to study the use of aramid nanopaper-based insulating materials in transformers to explore the effect of transformer discharge mechanism on aramid nanopaper-based insulating materials. This paper proposes to design multiple deep learning models to identify the discharge mode of the voltage transformer, find the characteristic signal, and carry out related tests on the discharge signal of different modes, and find the maximum temperature value of the aramid nanopaper-based insulating material for industrial use. The experimental results in this paper show that the aramid nanopaper-based insulating material can be used in transformers discharge detection well, and the safety rate is increased by 20%.

The poly-m-phenyleneisophthalamide (PMIA) is widely used in the electrical field due to its numerous favorable characteristics, but its poor thermal conductivity limits its application. In this study, PMIA was modified with nano-silica (SiO2) to improve its thermal and mechanical properties. Using iso-phthalic acid and m-phenylenediamine as monomers, the changes in the thermodynamic properties and microstructure parameters of SiO2-modified PMIA were analyzed using molecular dynamics before and after modification in the temperature range of 250~450 K. It was found that adding SiO2 improves the Young’s modulus and Shear modulus of PMIA, and the mechanical properties of PMIA, and SiO2/PMIA composites deteriorate with increasing temperature, but the mechanical properties of SiO2/PMIA composites are always better than those of pure PMIA in the temperature range of electrical equipment. Meanwhile, after doping SiO2 with the radius of 8 Å, the glass transition temperature of PMIA increases by 27.11 K, and its thermal conductivity increases from 0.249 W m−1 K−1 to 0.396 W m−1 K−1. When SiO2 is added to PMIA, the thermal expansion coefficient of PMIA will decrease in both glass and rubber states, and its thermal stability will improve. In terms of microstructure parameters, the free volume distribution of the SiO2/PMIA model is less easily dispersed than that of the PMIA model, indicating that the addition of SiO2 can improve the related properties of PMIA by hindering the movement of molecular chains.

Transformer bushing is one of the key equipment of transmission system, and its performance directly affects the stability and safety of transmission system. This paper is aimed at studying the safety detection of electrical properties of aramid shells with molecular dynamics and deep learning algorithms. The bushing needs to withstand AC voltage, DC voltage, and polarity reversal voltage during operation. The complexity of operating conditions leads to the improvement of bushing requirements for insulation performance, and bushing accident is a common type of transformer accident, accounting for 30% of the total number of transformer accidents. Therefore, it is necessary to detect the electrical performance and safety of the bushing to ensure the safe and stable operation of the power system. Aramid casing is a kind of casing with many advantages, such as high strength, high tensile breaking force, high stability, and high temperature resistance. Molecular dynamics is helpful to deeply analyze the micro mechanism of various complex phenomena, so as to explain the relationship between material microstructure and macroproperties, so it is very helpful to analyze the structure and properties of aramid casing. Deep learning is an important research direction in the field of machine learning. It can extract important features and simplify casing analysis steps. In this paper, an electrical performance test system of aramid casing is designed. It is proved that the reliability of casing is generally greater than 1 and the reliability is high. The current performance of the bushing is tested. 400 A is a dividing point, and no matter how large the current is, the maximum temperature is no more than 130°, which proves that the current performance of the bushing is stable and the temperature resistance is good. Finally, the radial field strength distribution of bushing capacitor core under different initial moisture content is tested, and it is concluded that the moisture can not be greater than 6%.

SF6 gas is an arc extinguishing medium that is widely used in gas insulated switchgear (GIS). When insulation failure occurs in GIS, it leads to the decomposition of SF6 in partial discharge (PD) and other environments. The detection of the main decomposition components of SF6 is an effective method to diagnose the type and degree of discharge fault. In this paper, Mg-MOF-74 is proposed as a gas sensing nanomaterial for detecting the main decomposition components of SF6. The adsorption of SF6, CF4, CS2, H2S, SO2, SO2F2 and SOF2 on Mg-MOF-74 was calculated by Gaussian16 simulation software based on density functional theory. The analysis includes parameters of the adsorption process such as binding energy, charge transfer, and adsorption distance, as well as the change in bond length, bond angle, density of states, and frontier orbital of the gas molecules. The results show that Mg-MOF-74 has different degrees of adsorption for seven gases, and chemical adsorption will lead to changes in the conductivity of the system; therefore, it can be used as a gas sensing material for the preparation of SF6 decomposition component gas sensors.