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Micro-arc oxidation (MAO) is a promising technology for enhancing the wear resistance of engine cylinders by growing a high hardness alumina ceramic film on the surface of light aluminum engine cylinders. However, the positive and negative pulse coordination, voltage characteristic signal, hardness distribution characteristics of the ceramic film, and their internal mechanism during the growth process are still unclear. This paper investigates the synergistic effect mechanism of cathodic and anodic current on the growth behaviour of alumina, dynamic voltage signal, and hardness distribution of micro-arc oxidation film. Ceramic film samples were fabricated under various conditions, including current densities of 10, 12, 14, and 16 A/dm2, and current density ratios of cathode and anode of 1.1, 1.2, and 1.3, respectively. Based on the observed characteristics of the process voltage curve and the spark signal changes, the growth of the ceramic film can be divided into five stages. The influence of positive and negative current density parameters on the segmented growth process of the ceramic film is mainly reflected in the transition time, voltage variation rate, and the voltage value of different growth stages. Enhancing the cathode pulse effect or increasing the current density level can effectively shorten the transition time and accelerate the voltage drop rate. The microhardness of the ceramic film cross-section presents a discontinuous soft-hard-soft regional distribution. Multiple thermal cycles lead to a gradient differentiation of the Al2O3 crystal phase transition ratio along the thickness direction of the layer. The layer grown on the outer surface of the initial substrate exhibits the highest hardness, with a small gradient change in hardness, forming a high hardness zone approximately 20-30 µm wide. This high hardness zone extends to both sides, with hardness decreasing rapidly.
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Electromagnetic wire is the carrier of energy and signal transmission. With the rapid development in aerospace, atomic energy, and other industrial fields, there is an increasing demand for the high-temperature-resistance of electromagnetic wires. In using traditional electromagnetic wires, it is difficult to meet the current temperature-resistance requirements. Therefore, the development of high-temperature-resistant electromagnetic wire has extremely important application value. In this paper, high-temperature-resistant electromagnetic wires are divided into organic insulated high-temperature-resistant electromagnetic wires, organic-inorganic insulated composite high-temperature-resistant electromagnetic wires, and inorganic insulated high-temperature-resistant electromagnetic wires. The method of improving the temperature-resistance level of organic insulated high-temperature-resistant electromagnetic wire is introduced. The selection principle of organic-inorganic and inorganic insulation high-temperature-resistant electromagnetic-wire conductor materials is analyzed. The current research status of organic-inorganic and inorganic insulated high-temperature-resistant electromagnetic wires is reviewed. The technical routes for preparing inorganic insulated high-temperature-resistant electromagnetic wire are compared. Finally, the challenges faced by the current high-temperature-resistant electromagnetic wire are pointed out, and the future development direction of organic-inorganic-composite insulation and inorganic insulation high-temperature-resistant electromagnetic wire is proposed.
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Deep learning methods, especially convolutional neural networks (CNNs), have achieved good results in the partial discharge (PD) diagnosis of gas-insulated switchgear (GIS) in the laboratory. However, the relationship of features ignored in CNNs and the heavy dependance on the amount of sample data make it difficult for the model developed in the laboratory to achieve high-precision, robust diagnosis of PD in the field. To solve these problems, a subdomain adaptation capsule network (SACN) is adopted for PD diagnosis in GIS. First, the feature information is effectively extracted by using a capsule network, which improves feature representation. Then, subdomain adaptation transfer learning is used to accomplish high diagnosis performance on the field data, which alleviates the confusion of different subdomains and matches the local distribution at the subdomain level. Experimental results demonstrate that the accuracy of the SACN in this study reaches 93.75% on the field data. The SACN has better performance than traditional deep learning methods, indicating that the SACN has potential application value in PD diagnosis of GIS.
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Several deep learning partial discharge (PD) diagnostic approaches have been developed in recent years to guarantee the security and stability of gas-insulated switchgear (GIS). The centralized training method requires multiple clients to jointly obtain as much data as possible to train the model to achieve excellent performance, which is impractical due to conflicts of interest and privacy protection. Furthermore, because of differences in the distribution of client data and the presence of a small sample, achieving high-precision and robust diagnosis for each client is an urgent problem. To that end, a novel personalized federated meta-learning (FML) is proposed in this paper to address the aforementioned issues. It develops reliable and personalized PD diagnosis models by collaborating with multiple clients and solves the problem of small sample diagnosis through scenario training under the premise of protecting data privacy. The experimental results show that the FML proposed can diagnose GIS PD with high precision and robustness for each client while maintaining privacy. The diagnostic accuracy of the FML proposed in this paper, especially for on-site unbalanced small sample clients, is 93.07%, which is significantly higher than that for other methods. It serves as a model for the collaborative development of an effective GIS PD diagnostic model.
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Data-driven diagnosis methods have been systematically investigated for the diagnosis of gas-insulated switchgear (GIS) partial discharge (PD). However, because of the scarcity of samples on-site, an operational gap exists between the diagnostic methods and their actual application. To settle this issue, a novel metric-based meta-learning (MBML) method is proposed. First, a hybrid self-attention convolutional neural network is constructed for feature extraction and trained through supervised learning. Then, the episodic MBML is used to train other parts, and the metric classifier is employed for diagnosis. The proposed MBML exhibits an accuracy of 93.17% under 4-way 5-shot conditions, which is a significant improvement over traditional methods. When the number of support sets is small, the benefits of MBML are more prominent, providing a viable solution for the on-site diagnosis of PD in GISs.
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The study on the mechanism of the effect of temperature on the decomposition reaction of SFn (n = 1-6) under discharge conditions is very important in studying the potential fault of SF6 high voltage switch equipment and perfecting the chemical kinetic model of SFn discharge. In this paper, structural optimizations, thermal correction to Gibbs free energy for the reactants, and products were performed at the B3LYP/6-311 + + G(d,p) theory level. The single-point energies of all species were collected at the CCSD(T)/aug-cc-PVTZ level. The electric and thermal decomposition mechanism of SFn under discharge conditions of 298-10,000 K were studied, respectively. The conclusion drawn was that in the temperature range of 298-10,000 K, the thermal decomposition homopolytic reaction â³G began to decline from 200 kJ/mol, while the â³G of the other two heterogenous reactions began to decrease from 1000 kJ/mol and 2000 kJ/mol, showing a downward trend of an almost similar slope. The electrolysis of SFn is related to electron energy. When the electron energy is low, SFn + e â SFn- series reactions occur, and â³G of R12, R20, R28, R36, and R44 increases with temperature rise, while â³G of R4 decreases with temperature. When the electron energy is high, one of SFn- â SFn-1- + F, SFn- â SFn-1 + F-, and SFn- â SFn-1 + F + e will occur, and the reactions that occur at various temperature ranges as the temperature rises vary. When the second electron hits the SFn-, the SFn- + e â SFn-1- + F reaction will occur. The â³G of this reaction slowly decreases with an increase in temperature. This study in clearer terms explains the decomposition process and mechanism of SFn at different temperatures.
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The transverse magnetic field (TMF) contacts make the vacuum arcs deviate from the axisymmetric structure, so complete spatiotemporal evolution information of the plasma cannot be obtained by adopting one- or two-dimensional (2D) diagnostic methods. To address the issues, computer tomography was introduced in this paper. First, a multi-angle diagnostic imaging system based on split fiber bundles was proposed, which used a high-speed camera to simultaneously acquire eight angles of the arc image over time. In addition, a tomography algorithm called the maximum likelihood expectation maximum with Split Bregman denoising was proposed to reconstruct the dynamic spatiotemporal characteristics of the arc under complex conditions. Then, the three-dimensional (3D) distribution of Cu i and Cr i particles inside the contact gap was obtained by adopting optical filters. The 3D distribution of the vacuum arc had shown an obvious asymmetrical pattern under the TMF contacts, and there was a ring-like aggregation zone inside the arc, which can cause severe ablation on the anode contacts. According to the reconstructed 3D distribution of Cu i and Cr i, it is found that the metal vapor was mainly concentrated near the electrode surface and showed a clear distribution of non-uniform aggregates, while the concentration of particles in the gap was low. Moreover, on the cathode surface, the cathode spots moved in the form of groups driven by the TMF, while the anode surface was ablated by the electric arc, and the metal vapor existed in the form of bands.
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Extensive attempts have been made to enable the application of deep learning to 3D plasma reconstruction. However, due to the limitation on the number of available training samples, deep learning-based methods have insufficient generalization ability compared to the traditional iterative methods. This paper proposes an improved algorithm named convolutional neural network-maximum likelihood expectation maximization-split-Bergman (CNN-MLEM-SB) based on the combination of the deep learning CNN and an iterative algorithm known as MLEM-SB. This method uses the prediction result of a CNN as the initial value and then corrects it using the MLEM-SB to obtain the final results. The proposed method is verified experimentally by reconstructing two types of vacuum arcs with and without transverse magnetic field (TMF) control. In addition, the CNN and the proposed algorithm are compared with respect to accuracy and generalization ability. The results show that the CNN can effectively reconstruct the arcs between a pair of disk contacts, which has specific distribution patterns: its structural similarity index measurement (SSIM) can reach 0.952. However, the SSIM decreases to 0.868 for the arc between a pair of TMF contacts, which is controlled by the TMF and has complex distribution patterns. Compared with the CNN reconstruction method, the proposed algorithm can achieve a higher reconstruction accuracy for any arc shape. Compared with the iterative algorithm, the proposed algorithm's reconstruction efficiency is higher by 38.24% and 35.36% for the vacuum arc between the disk and the TMF contacts, respectively.
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Sufficiently high voltage applied between two metal electrodes, even in ultra high vacuum conditions, results in an inevitable discharge that lights up the entire gap, opening a conductive channel through the vacuum and parasitically consuming large amounts of energy. Despite many efforts to understand the processes that lead to this phenomenon, known as vacuum arc, there is still no consensus regarding the role of each electrode in the evolution of such a momentous process as lightning. Employing a high-speed camera, we capture the entire lightning process step-by-step with a nanosecond resolution and find which of the two electrodes holds the main responsibility for igniting the arc. The light that gradually expands from the positively charged electrode (anode), often is assumed to play the main role in the formation of a vacuum arc. However, both the nanosecond-resolution images of vacuum arc evolution and the corresponding theoretical calculations agree that the conductive channel between the electrodes is built in the form of cathodic plasma long before any significant activity develops in the anode. We show evidently that the anode illumination is weaker and plays a minor role in igniting and maintaining the conductive channel.
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N2 has been introduced to gas-insulated switchgears (GIS) as an environmentally friendly insulation medium for SF6 gas. Considering the low breakdown strength of N2, it's essential to apply a dielectric coating on part of the electrode in GIS. In the current work, yttria-stabilized zirconia (YSZ) and zirconia-toughened alumina (ZTA) ceramics are tactfully applied as dielectric coating materials and successfully coated on the surface of rod electrode via high efficiency supersonic atmospheric plasma spraying method. It is demonstrated that both YSZ and ZTA have excellent thermal stability. In the measured frequency range 10-2-106 Hz, the dielectric constant (ε') decreases with the increase in frequency. At measured temperatures (0-140 °C), ε' increases from 15.45 to 16.31 for YSZ ceramic at 1 kHz, while it varies between 30.23 and 39.34 for ZTA ceramic. As compared to that of bare electrode, the 50% BDV (U50) of solid/gas composite insulation system is significantly improved when using YSZ and ZTA as coating materials. Furthermore, the U50 increases with the increase of coating thickness. For the electrode coated with ZTA ceramic (500 µm), the U50 value reaches to 86 kV, which is enhanced about 21.13% in comparison with that of bare electrode. With respect to YSZ ceramic coating (500 µm), a higher U50 value of 88 kV is obtained. This is mainly due to that YSZ has a lower permittivity, which can generate a more uniform electric field distribution between the rod and plane electrodes, bringing about a higher breakdown voltage.
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An innovative array of magnetic coils (the discrete Rogowski coil-RC) with the advantages of flexible structure, miniaturization and mass producibility is investigated. First, the mutual inductance between the discrete RC and circular and rectangular conductors are calculated using the magnetic vector potential (MVP) method. The results are found to be consistent with those calculated using the finite element method, but the MVP method is simpler and more practical. Then, the influence of conductor section parameters, inclination, and eccentricity on the accuracy of the discrete RC is calculated to provide a reference. Studying the influence of an external current on the discrete RC's interference error reveals optimal values for length, winding density, and position arrangement of the solenoids. It has also found that eccentricity and interference errors decreasing with increasing number of solenoids. Finally, a discrete RC prototype is devised and manufactured. The experimental results show consistent output characteristics, with the calculated sensitivity and mutual inductance of the discrete RC being very close to the experimental results. The influence of an external conductor on the measurement of the discrete RC is analyzed experimentally, and the results show that interference from an external current decreases with increasing distance between the external and measured conductors.
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This paper presents a new configuration of H-bridge cascaded static synchronous compensator (STATCOM) based on auto disturbance rejection control and modulation wave shifting control intended for installation in industrial and utility power distribution systems. It proposes an improved controller that devotes itself not only to satisfying the requirement of dynamic reactive power compensation but also to achieving the balance of DC capacitor voltage. The new configuration is prominent in having no restriction on the number of power module. Simulation results show the proposed controller exhibits better performance in terms of response time and transient stability compared with the proportion integration controller. Two actual H-bridge cascaded STATCOMs are constructed at 10 kV, 2 MVA and 380 V, 6.5 kVA, respectively, and a series of verifications test are executed in 380 V, 6.5 kVA STATCOM experimental system. The experiment results further prove that H-bridge cascaded STATCOM with the proposed controller has excellent dynamic performance and strong robustness.