Due to prolonged exposure to pulsed voltage during operation, the insulation systems of power electronic equipment are susceptible to premature degradation and failure, which seriously affect the operation of the power system. Therefore, it is important to explore the accelerated degradation mechanisms of electrical insulation under the pulsed electric field. In this paper, the influence of different pulse voltage parameters on insulation degradation characteristics is first summarized. Then, the research progress of the degradation mechanisms of insulation under a pulsed electric field is explored from microscopic and macroscopic perspectives. Three degradation mechanisms, including the dynamic behavior of charges, electromechanical stress and air gap discharge, are revealed, whose microscopic essence is the difference in the response time of charged particles of different scales under a pulsed electric field. In addition, the weaknesses of the current research are also identified and discussed. This review provides a theoretical reference and guidance for the optimal design of insulation materials.

The partial discharge occurring in the weak part of the insulation of a converter transformer results in the formation of a large number of bubbles in the insulating oil. The migration, deformation, and other dynamic behaviors of bubbles in the region of a strong electric field can cause them to easily accumulate into “small bridges” of impurities that can lead to breakdown of the oil gap. The authors of this study experimentally investigate and discuss the mechanisms of migration and deformation of bubbles in oil during partial discharge under composite AC/DC voltage to clarify their dynamic behaviors. The influence of the initial position of the bubbles on their trajectory of migration and velocity as well as the morphological changes occurring in them are analyzed using numerical simulations. The results show that the bubbles move away from the strong electric field due to the action of the dielectrophoretic force. The interface of the bubbles is longitudinally stretched under the action of the electrostrictive force and the vertical component of the drag force and gradually recovers to assume a spherical shape under the influence of surface tension and the horizontal component of the drag force.

Dissolved gas analysis (DGA) occupies an extremely important position in transformer condition assessment, and many conventional methods have been proposed based on dissolved gas in oil analysis. In this paper, a combined deep neural network (CDNN) is proposed to combine the characteristics of dissolved gas in oil and conventional methods for transformer condition assessment. First, the sample data are normalized according to the characteristics of the conventional method parameters. Then, the normalized parameters are used as input parameters of the deep neural network. Multiple deep neural network models are built separately. Next, the prediction results of multiple deep neural network models are weighted according to the accuracy of different models. Finally, the one with the largest weight is selected as the final prediction result of the combined deep neural network. In this paper, we show the necessity of data normalization through data statistics and demonstrations. The comparison between setting up the normal state data and not considering the normal state data proves that the normal state is easy to misclassify with other states when predicting, which leads to a decrease in the prediction accuracy. By comparing with the composition method of this paper and the classification method commonly used in MATLAB software, it is confirmed that the method in this paper combines the advantages of other methods and corrects the prediction results, thus having higher accuracy.