With the development of intelligent sensing technology, a large amount of partial discharge (PD) time-domain waveform images are generated in the on-site detection of gas insulated switchgear (GIS) partial discharge. Traditional pattern recognition methods are mostly aimed at structured data and cannot directly identify defect types of such data.At the same time, the deep learning method for GIS PD pattern recognition is generally faced with the problem of small samples.In order to solve the above problems, this paper proposes a PD pattern recognition method based on transfer learning and DenseNet model.Firstly, the time-domain waveform images are processed by image enhancement, normalization, image compression and other image processing techniques.The FDTD method was used to simulate GIS PD, and the time domain waveform image database of four PD defects are established.Using CNN and transfer learning, the recognition accuracy of the model is increased to 95%, with better robustness.The recognition performance of different CNN structures is studied. The results show that DenseNet model has higher accuracy than other structures and shorter training time. This study can be used to diagnose the insulation status of GIS equipment in-site.

There are many metal and epoxy resin interfacial structures in power equipments, which can generate interfacial stress concentrations and may induce accidents due to epoxy curing. In this paper, through simplifying interface structure into a coaxial cylindrical model, the formation mechanism of interfacial stress concentration of this interface structure was studied during epoxy resin curing process. The strain and temperature at the interface were measured by the strain and temperature measurement system. Then a model for calculating the interface stress distribution was established, including the heat conduction module, curing dynamics module and curing deformation module. The validity of the model was verified by comparing the calculated results with the measured results. In addition, the air gap defects at the interface, appearing after the curing process, were simulated. The results show that, after the curing process, the first principal stress at the centre of the interface gradually increases from the centre to the edge, reaching 41.06 and 40.20 MPa at the upper and lower edges respectively. The existence of air gap leads to partial discharge at low voltage. The calculation model can be extended to the stress prediction between the metal and epoxy resin in other power equipments.

The electrothermal synergistic aging effect on a cross-linked polyethylene (XLPE) cable system can cause strong deterioration of the cable insulation over prolonged service conditions. It is widely accepted that space charge can be the cause and the effect of aging of XLPE insulation under dc stresses. Until now, reports on the correlation between space charge characteristics and the aging effect mainly focus on the XLPE flat samples, and an insight into this correlation for XLPE coaxial cables is still scarce. In this article, space charge in XLPE coaxial cables with different aging durations was measured under polarization and depolarization conditions. The influences of cable coaxial structure and insulation thickness were analyzed. The mobility and trap density were extracted with the depolarization charge profiles. The results show that heterocharge, trap-controlled mobility and shallow trap first increase (0–30 days) and then decrease (30–90 days) with the aging time, while the deep trap increases from 30 to 90 days aging, and for the thicker coaxial cable insulation, due to more deep traps blocking charge migration and extraction, a fraction of residual charge retains near the outer semiconductor after depolarization. In addition, the residual charge density increases with aging time. These phenomena have significant impacts on long-term cable operations and should attract more attentions. The space charge characteristics in coaxial XLPE cable during aging are likely to invoke a modification of the life model for a real cable.

Non-destructive inspection methods that use broadband reflecting signals to identify microscopic flaws and aging characteristics in power cables are often employed, however they suffer from limited detection distances and a single detection target. This study proposes a scheme for harnessing power line communication (PLC) devices to accomplish the detection and identification of multiple cable aging. A model of the propagation of a high-frequency carrier signal through a cable is first combined with mathematical models of various types of aging, such as water-tree aging, electric-tree aging, and thermal aging, to simulate the network channel state in the process of different insulation degradation of the cable. The channel frequency domain response (CFR) at the receiver can then reflect the incidence of cable aging, and the proposed approach, in conjunction with machine learning algorithms, can recognize different types and severity of aging under interference. Computer simulations and field experiments were conducted to validate the viability of the suggested approach, and the results shown that 98% correctness could be attained in on-line monitoring despite interference considerations. In theory, the range is doubled when compared to the reflected signal approach with the same bandwidth and power.

Atmospheric Ar/NH₃ dielectric barrier discharge (DBD) is a type of uniform dielectric barrier discharge that has potential applications in surface treatment, thin film surface deposition, hydrogen storage, etc. The characteristics and the application effects of Ar/NH₃ DBD are strongly dependent on dielectric materials, electrode structures, and gas atmospheres. In this paper, a one-dimensional fluid numerical simulation model was established to investigate the effects of dielectric constant and secondary electron emission coefficient (SEEC) of the barrier dielectric material on the discharge characteristics and product distributions in Ar/NH₃ gas mixture. The results show that increasing dielectric constant makes the discharge moment slightly earlier (discharge phase 17.5°–5°) and has a greater effect on the discharge intensity (discharge current), plasma parameters, and discharge products as well as their yields. While increasing SEEC makes the discharge moment significantly earlier (discharge phase 27.5° to −5°), it has less influence on the discharge intensity (discharge current), plasma parameters, and discharge products and their yields. On this basis, a possible strategy was proposed to describe the effect of the two dielectric parameters on the discharge characteristics and products.

Exploiting Power Line Communication (PLC), a distinctive non-invasive sensing system has been developed to detect insulation defects in distribution networks, providing early alerts for impending insulation failures and averting system disruptions. Despite this progress, existing detection algorithms fall short of the precision necessary for real-time grid monitoring due to variations introduced by load dynamics and grid configurations. In this study, we delve into the evolution of cable parameters from water tree degradation to ultimate insulation failure, formulating a comprehensive dielectric model that mirrors the aging process of cable insulation. We introduce an innovative approach that hinges on anomalies classification within feature images. This approach harnesses the Markov transition field(MTF) to encode Channel Frequency Response (CFR) data visually and employs a residual neural network, enhanced with an attention mechanism, to pinpoint various cable defects. Remarkably, our proposed methodology boasts a perception accuracy exceeding 98% for minute parameter fluctuations within intricate networks.