Utilizing liquid rubber to toughen epoxy resin is one of the most mature and promising methods. However, the dielectric relaxation characteristics of the epoxy/liquid rubber composites have not been studied systematically, while the relaxation behaviours are a critical factor for both micro and macro properties. In this paper, hydroxyl-terminated liquid nitrile rubber (HTBN) is employed to reinforce a kind of room-temperature-cured epoxy resin. The dielectric spectrum is measured and analysed. Results show that two relaxation processes are introduced in the binary composites. The α relaxation of HTBN shows a similar temperature dependence with the β relaxation of epoxy resin. The interfacial polarization leads to an increase of complex permittivity, which reaches its maximum at 70 °C. In addition, affected by interfacial polarization, the thermionic polarization is inhibited, and the samples with filler ratios of 15% and 25% show lower DC-conductivity below 150 °C. In addition, the α relaxation and thermionic polarization of epoxy resin obey the Vogel‒Fulcher‒Tammann law, while the interfacial polarization and DC-conductivity satisfy with the Arrhenius law. Furthermore, the fitting results of the Vogel temperature of α relaxation, glass transition temperature, apparent activation energy of interfacial polarization and DC-conductivity all decline with HTBN content. These results can provide a reference and theoretical guidance for the assessment of dielectric properties and the improvement of the formulation of liquid-rubber-toughened epoxy resin.

In response to the problems of time-consuming simulation of temperature field of resin impregnated paper (RIP) bushings on valve side of converter transformer, which result from a high degree of freedom and complex coupling mechanism, a deep learning surrogate model (DLSM) suitable for fast prediction of bushing temperature is proposed. The model consists of two parts: proper orthogonal decomposition (POD) reduced order model based on back propagation neural network (BPNN) and convolutional neural network (CNN), which predict the outside and inside part of bushing respectively to improve the accuracy. Subsequently, the temperature fields calculated by the finite element method (FEM) and DLSM are compared, and the average relative error is less than 0.7%. In addition, the influence of different train sizes and input perturbations on the precision of the model is analyzed, which is good when at least 108 samples are trained. The prediction results of the model with input perturbation within 5% are similar to the FEM results. Finally, the time for the model to predict 30 test conditions is 1.5s, which is much less than the time of finite element simulation. The research results show that DLSM has high precision, robustness and prediction speed. The method in this paper provides a feasible solution for online monitoring and digital twins of bushings.

As an important electrical connection component in electrical equipment, the strap contact is directly related to the long-term operation stability of equipment. The electrical contact resistance (ECR) of the electrical connection structure is an important indicator for evaluating the reliability of the electrical contact system. In this research, the theoretical calculation of ECR of the strap contacts used by the G-W theoretical model and the fractal theoretical model is improved and compared. After comparison with the experimental measurement values, the rationality and accuracy of the two theoretical models are discussed. The results show that when the load is small (1–3 N), the maximum error of G-W model is 41%, while the fractal model method has a maximum error of 9%. When the load is large (4–10 N), the results of the two are almost the same, and the errors are both within 14%. In summary, the error range of the fractal model is smaller and the change trend is closer to the experimental value, which is suitable for a relatively better ECR analytical calculation theory. The research results can provide a theoretical basis for research on the electrical contact performance of the electrical contact structure of electrical equipment.

In high voltage direct current (HVDC) gas-insulated switchgear (GIS) and gas-insulated transmission lines (GIL), epoxy resin and its composites serve as the primary insulating components. However, the inherent mismatch in mechanical properties at the interface between the metal conductor and epoxy-based insulator gives rise to weak points during production and operation, leading to potential issues like physical cracking or electric field distortion. In this work, these challenges are addressed by introducing a semi-conductive buffer layer with rationally modulated electrical and mechanical properties at the conductor/insulator interface. Carbon black nanofillers are incorporated into a polyether-amine cured epoxy matrix for optimal performance. The application of such a buffer layer significantly improves the bonding strength between the epoxy-based insulator and the aluminum conductor, which also effectively inhibits charge injection from metal conductors to GIS/GIL insulators. To ensure industrial applicability, storage modulus, glass transition temperature and coefficient of thermal expansion of the semi-conductive materials are systematically investigated. The design of semi-conductive buffer layer provides a feasible approach to enhancing the mechanical and electrical performance of HVDC GIS/GIL systems.