Partial discharge (PD) is one of the major causes of insulation accidents in oil-immersed transformers, generating a large number of signals that represent the health status of the transformer. In particular, acoustic signals can be detected by sensors to locate the source of the partial discharge. However, the array, type, and quantity of sensors play a crucial role in the research on the localization of partial discharge sources within transformers. Hence, this paper proposes a novel sensor array for the specific localization of PD sources using COMSOL Multiphysics software 6.1 to establish a three-dimensional model of the oil-immersed transformer and the different defect types of two-dimensional models. “Electric-force-acoustic” multiphysics field simulations were conducted to model ultrasonic signals of different types of PD by setting up detection points to collect acoustic signals at different types and temperatures instead of physical sensors. Subsequently, simulated waveforms and acoustic spatial distribution maps were acquired in the software. These simulation results were then combined with the time difference of arrival (TDOA) algorithm to solve a system of equations, ultimately yielding the position of the discharge source. Calculated positions were compared with the actual positions using an error iterative algorithm method, with an average spatial error about 1.3 cm, which falls within an acceptable range for fault diagnosis in transformers, validating the accuracy of the proposed method. Therefore, the presented sensor array and computational localization method offer a reliable theoretical basis for fault diagnosis techniques in transformers.

The application of machine learning method for oil-immersed transformer fault diagnosis and with monitoring dissolved gas analysis (DGA) is an effective engineering method. This paper proposes a novel framework for improving accuracy of power transformer incipient fault diagnosis. Firstly, this paper reconstructs the DGA primitive data with Adaptive Synthetic Sampling (ADASYN) synthesizing minority class samples and Kernel Principal Components Analysis (KPCA) extracting features. Furthermore, we propose a novel GD-AHBA optimization method for enhancing SVM performance and build the GD-AHBA-SVM model. On the one hand, we improve the control parameters of population motion and enhance the ability to escape from local optimum in the later periods. On the other hand, Good Point Set theory and Differential Evolution (DE) are incorporated to optimize the spatial distribution of the population, which improves the convergence accuracy and speed of Honey Badger Algorithm (HBA), and reduces the computational overhead of invalid populations. Various diagnostic methods are evaluated by experimental comparison and results show that the framework proposed in this paper significantly improves the accuracy of transformer DGA fault diagnosis.

Insulating oil plays a crucial role in internal insulation of oil-impregnated transformers. It has been demonstrated in a variety of experimental studies that mineral oil (MO) and vegetable oil (VO) can be blended in different ratios to improve insulation properties; however, the mechanisms underlying this phenomenon remain unclear. In this study, a molecular dynamics (MD) simulation approach was used to investigate diffusion of water molecules in VO/MO blends and dielectric constants of a mixture. The results show that the diffusion coefficient of water molecules is negatively correlated with the proportion of VO; thus, addition of VO helps to improve the insulation properties of a mixture. Due to introduction of strong polar functional groups, a decrease in the diffusion behavior of water molecules can be attributed to an increase in the interaction energy and formation of hydrogen bonds between water molecules and the mixed oil system. There is a direct correlation between the dielectric constant of a mixture and VO content; however, it is very sensitive to water content. The presence of strong polar water molecules or functional groups in a mixture leads to an increase in the dielectric constant, which results in a reduction in insulating properties. Accordingly, presence of polar groups plays an important role in determining the insulating properties of a mixture. To increase the insulation performance of a mixture, it is important to consider the diffusion-inhibiting and dielectric effects of the stronger polar groups in vegetable oil compared to those in mineral oil.

Ferroelectrics with a high dielectric constant are ideal materials for the fabrication of miniaturized and integrated electronic devices. However, the dielectric constant of ferroelectrics varies significantly with the change of temperature, which is detrimental to the working stability of electronic devices. This work demonstrates a new strategy to design a ferroelectric dielectric with a high temperature stability, that is, the design of a multilayer relaxor ferroelectric thin film with a composition gradient. As a result, the fabricated up-graded (Pb,La)(Zr_0.65,Ti_0.35)O₃ multilayer thin film showed a superior temperature stability of the dielectric constant, with variation less than 7% in the temperature range from 30 °C to 200 °C, and more importantly, the variation was less than 2.5% in the temperature range from 75 °C to 200 °C. This work not only develops a dielectric material with superior temperature stability, but also demonstrates a promising method to enhance the temperature stability of ferroelectrics.

In the present study, the friction and wear behaviors of Si₃N₄-hBN(Hexagonal boron nitride) ceramic composites against polyether-etherketone in artificial seawater were investigated, and the sliding speed was varied from 0.52 to 1.73 m/s to study the effect. It was found that the friction coefficients and wear rates decreased with the increase in sliding speed for Si₃N₄-hBN (with the hBN content ranging from 5% to 20%) against PEEK(Polyether-ether-ketone); the sliding pairs represented an upward trend as the speed increased for Si₃N₄-hBN (with an hBN content of 0 and 30%) against PEEK. This result was mainly attributed to complex friction resistance sources, such as mechanical meshing and the shear strength of soft tribofilm. On the other hand, both the hBN content and sliding speed had a significant effect on the tribological performance of the Si₃N₄-hBN/PEEK pairs. Meanwhile, the lowest friction coefficient of 0.07 and wear rates below 10⁻⁶ mm³/Nm were obtained from the Si₃N₄-20%hBN/PEEK pair at the speed of 1.73 m/s.