The condition of oil-paper insulation is closely related to the life expectancy of a transformer. The accurate results of oil-paper have not been obtained due to the impact of influencing factors. Therefore, in order to improve the evaluation accuracy of oil-paper insulation, in this paper, oil-paper samples which were prepared with different aging and moisture content were analyzed by frequency domain spectroscopy (FDS). Results show that when the moisture content is less than 2%, the range of 10¹~10³ Hz is mainly affected by moisture and aging has little effect. However, with the increase of moisture content, the effect of aging degree on this band became increasingly prominent. S_m, which represents the integral value from 10⁻¹ to 10⁻³ Hz, and S_DP, which represents the integral value from 10¹ to 10³ Hz, were extracted as characteristic parameters of moisture content and aging degree respectively. Compensation factors γ which represents the influence ratio of moisture on S_DP and φ which represents the influence ratio of aging on S_m were introduced to compensate for the influence of moisture content and aging degree on characteristics respectively. Then, a new method was proposed to evaluate the condition of oil-paper based on compensation factors. Through this method, the influence in characteristics can be eliminated by the obtained actual compensation factors, thus distinguishing the internal influence between moisture content and aging degree on FDS. Finally, this method was verified by field test.

Partial discharge (PD) measurement and its pattern recognition are vital to fault diagnosis of transformers, especially to those traction substation transformers undergoing repetitive traction load shocks. This paper presents the primary factors induced by traction load shocks including high total harmonics distortion (THD), transient voltage impulse and high-temperature rise, and their effects on the feature parameters of PD. Experimental tests are conducted on six artificial PD models with these factors introduced one by one. Results reveal that the maximum PD quantity and the PD repetitive rate are favorable to be enlarged when the oil temperature exceeds 80 °C or the THD is higher than 16% with certain orders of harmonic. The decline in PD inception voltage can mainly be attributed to the transient voltage impulse. The variation in central frequency of the fast Fourier transformation (FFT) spectra transformed from ultra-high frequency signals can mainly be attributed to high THD, especially when it exceeds 20%. The temperature rise has no significant influence on the FFT spectra; the transient voltage impulse, however, can result in a central frequency shift of the floating particle discharge. With the rapid development of high-speed railways, the study presented in this paper will be helpful for field PD detection and recognition of traction substation transformers in the future.

This study explores the influence of hydrostatic pressure on the discharge along the oil-paper interface under AC voltage, especially for the normal operating condition and breakdown. In this paper, an experimental platform was set up to record the partial discharge (PD) parameters of the test sample under different hydrostatic pressures, while the applied AC voltage was increased to final flashover voltage step by step. Experimental results showed that higher hydrostatic pressure had different effects on PD under different voltages. Higher pressure decreased the PD energy and increased the flashover voltage. Furthermore, under higher hydrostatic pressure, discharge traces (white mark) were found on the surface of the samples after intense discharging on the oil-paper interfaces, indicating that the hydrostatic pressure can affect the gas generation and dissipation process underneath the surface of the pressboards. Finally, the mechanism of how hydrostatic pressure influences the PD, flashover voltage, and white mark was interpreted based on the bubble theory. The results derived in this paper can be helpful for an optimal design and reasonable operation of oil-paper insulation systems, especially for power transformers.

The detection of defect severity in cable terminations plays a critical role in ensuring the safe and stable operation of high-speed trains (HST). However, the partial discharge (PD) characteristics of the same type of defect can appear similar across different severities, posing challenges for accurate insulation defect degree identification. Consequently, this paper proposes an image transformation method, named the equivalent bandwidth matrix of relative locations (EBMRL), coupled with the Self-guided Transformer (SG-Former) algorithm, which is more effective for fine-grained image recognition, to accurately identify different degrees of defects with similar PD characteristics. In the proposed approach, the original PD signals are first converted into images using EBMRL. This transformation embeds the characteristic and bandwidth information from the original PD data into the images, thereby reducing the similarity of information between classes in the transformed images and enhancing their distinguishability. Subsequently, local and global features of the transformed EBMRL images are extracted to train the SG-Former model. The model is finally utilized to identify the severity of defects in cable terminations. The results demonstrate that the method proposed in this paper achieves better performance compared with some of the state-of-the-art methods.

The pressboard surface is the electric weak link of the oil-paper insulation in transformers, and long-term partial discharge (PD) erosion is the dominant cause of degradation in pressboard. To explore the development processes of surface tracking under the effect of tip curvature, the typical needle-plate model was selected to initiate an electric field with a high tangential component on pressboard surface under needle tip curvature of 4~42 μm. With the help of a high-speed camera and a PD detecting system, the development processes of surface tracking and PD were recorded under a sustained AC voltage. A profound difference between surface tracking under different curvatures was discussed. Pressboard surfaces after tests were observed under a scanning electron microscope (SEM), and the damage degree of cellulose fibers was dependent on the tip curvature.

High-speed train cable terminals are prone to insulation defects under complex operating conditions. However, traditional detection methods are limited by the scarcity of labeled data and the challenges in extracting features from non-stationary signals. To address this issue, this paper proposes a novel semi-supervised diagnostic paradigm. First, to account for the time-shift characteristics of partial discharge (PD) signals, an adjacency matrix is constructed using dynamic time warping (DTW), converting PD signals into a spatiotemporal graph representation. Second, a fusion architecture combining a Variational Graph Autoencoder (VGAE) and Gated Recurrent Unit (GRU) is designed, where the GRU captures the dynamic evolution characteristics of PD signals. Furthermore, a k-nearest neighbor (KNN) temporal graph reconstruction strategy is proposed to enhance the model’s robustness. In addition, an auxiliary strategy is introduced, integrating the TimeGAN-based temporal augmentation technique, achieving an accuracy of 90.73% with only 10% labeled data. Finally, interpretability analysis using GNNExplainer verifies the effectiveness of the proposed method in detecting cable terminal insulation defects under conditions of limited data availability.