Convolutional Neural Network-Based Pattern Recognition of Partial Discharge in High-Speed Electric-Multiple-Unit Cable Termination.

Partial discharge detection is considered a crucial technique for evaluating insulation performance and identifying defect types in cable terminals of high-speed electric multiple units (EMUs). In this study, terminal samples exhibiting four typical defects were prepared from high-speed EMUs. A cable discharge testing system, utilizing high-frequency current sensing, was developed to collect discharge signals, and datasets corresponding to these defects were established. This study proposes the use of the convolutional neural network (CNN) for the classification of discharge signals associated with specific defects, comparing this method with two existing neural network (NN)-based classification models that employ the back-propagation NN and the radial basis function NN, respectively. The comparative results demonstrate that the CNN-based model excels in accurately identifying signals from various defect types in the cable terminals of high-speed EMUs, surpassing the two existing NN-based classification models.

Study on the aging status of insulators based on hyperspectral imaging technology.

The acidic environment is one of the main factors leading to the aging of silicone rubber (SiR) insulators. Aging can reduce the surface hydrophobicity and pollution flashover resistance of insulators, threatening the safe and stable operation of the power grid. Therefore, evaluating the aging state of insulators is essential to prevent flashover accidents on the transmission line. This paper is based on an optical hyperspectral imaging (HSI) technology for pixel-level assessment of insulator aging status. Firstly, the SiR samples were artificially aged in three typical acidic solutions with different concentrations of HNO3, H2SO4, and HCl, and six aging grades of SiR samples were prepared. The HSI of SiR at each aging grade was extracted using a hyperspectral imager. To reduce the calculation complexity and eliminate the interference of useless information in the band, this paper proposes a joint random forest- principal component analysis (RF-PCA) dimensionality reduction method to reduce the original 256-dimensional hyperspectral data to 7 dimensions. Finally, to capture local features in hyperspectral images more effectively and retain the most significant information of the spectral lines, a convolutional neural network (CNN) was used to build a classification model for pixel-level assessment of the SiR's aging state of and visual prediction of insulators' defects. The research method in this paper provides an important guarantee for the timely detection of safety hazards in the power grid.

Engineering a Dynamic Solvent-Phobic Liquid Electrolyte Interphase for Long-Life Lithium Metal Batteries.

The heterogeneity, species diversity, and poor mechanical stability of solid electrolyte interphases (SEIs) in conventional carbonate electrolytes result in the irreversible exhaustion of lithium (Li) and electrolytes during cycling, hindering the practical applications of Li metal batteries (LMBs). Herein, this work proposes a solvent-phobic dynamic liquid electrolyte interphase (DLEI) on a Li metal (Li-PFbTHF (perfluoro-butyltetrahydrofuran)) surface that selectively transports salt and induces salt-derived SEI formation. The solvent-phobic DLEI with C-F-rich groups dramatically reduces the side reactions between Li, carbonate solvents, and humid air, forming a LiF/Li3PO4-rich SEI. In situ electrochemical impedance spectroscopy and Ab-initio molecular dynamics demonstrate that DLEI effectively stabilizes the interface between Li metal and the carbonate electrolyte. Specifically, the LiFePO4||Li-PFbTHF cells deliver 80.4% capacity retention after 1000 cycles at 1.0 C, excellent rate capacity (108.2 mAh g-1 at 5.0 C), and 90.2% capacity retention after 550 cycles at 1.0 C in full-cells (negative/positive (N/P) ratio of 8) with high LiFePO4 loadings (15.6 mg cm-2) in carbonate electrolyte. In addition, the 0.55 Ah pouch cell of 252.0 Wh kg-1 delivers stable cycling. Hence, this study provides an effective strategy for controlling salt-derived SEI to improve the cycling performances of carbonate-based LMBs.

A Novel Anti-Flashover Superhydrophobic Coating with Self-Assembly Characteristic of Surface Energy Differences.

Because of the versatility of superhydrophobic materials, they have attracted a lot of attention even in power electronics, transportation, engineering, and other fields. The volume fraction of fluorinated silicon oxide nanoparticles in superhydrophobic materials is one of the most important factors. Increasing the volume fraction will decrease the stability between the coating and the hydrophobic surface. Especially, the flashover voltage of the coating gradually decreases from 10 to 35 vol.%. Meanwhile, the flashover voltage dispersion of the coating increases drastically after 30 vol.%. In order to improve the electrical properties of the superhydrophobic coating, self-assembly of surface energy differences strategy is proposed in this work. A binary filling phase of the coating is introduced by 2D boron nitride nanosheets and silicon oxide nanoparticles. Although Hexagonal boron nitride with high surface energy and low roughness, it will be spontaneously assembled and wrapped by silicon oxide nanoparticle based on surface energy differences, which forming a low surface energy filled phase. Experiment results prove that the flashover voltage of the superhydrophobic coating is optimized by the binary filling phase coating. This method offers new ideas for the selection of filling phase and application of superhydrophobic materials.

Ladderphane copolymers for high-temperature capacitive energy storage.

For capacitive energy storage at elevated temperatures1-4, dielectric polymers are required to integrate low electrical conduction with high thermal conductivity. The coexistence of these seemingly contradictory properties remains a persistent challenge for existing polymers. We describe here a class of ladderphane copolymers exhibiting more than one order of magnitude lower electrical conductivity than the existing polymers at high electric fields and elevated temperatures. Consequently, the ladderphane copolymer possesses a discharged energy density of 5.34 J cm-3 with a charge-discharge efficiency of 90% at 200 °C, outperforming the existing dielectric polymers and composites. The ladderphane copolymers self-assemble into highly ordered arrays by π-π stacking interactions5,6, thus giving rise to an intrinsic through-plane thermal conductivity of 1.96 ± 0.06 W m-1 K-1. The high thermal conductivity of the copolymer film permits efficient Joule heat dissipation and, accordingly, excellent cyclic stability at elevated temperatures and high electric fields. The demonstration of the breakdown self-healing ability of the copolymer further suggests the promise of the ladderphane structures for high-energy-density polymer capacitors operating under extreme conditions.

State of charge estimation for lithium-ion battery based on whale optimization algorithm and multi-kernel relevance vector machine.

Lithium-ion batteries are key elements of electric vehicles and energy storage systems, and their accurate State of Charge (SOC) estimation is momentous for battery energy management, safe operation, and extended service life. In this paper, the Multi-Kernel Relevance Vector Machine (MKRVM) and Whale Optimization Algorithm (WOA) are used to estimate the SOC of lithium-ion batteries under different operating conditions. In order to better learn and estimate the battery SOC, MKRVM is used to establish a model to estimate lithium-ion battery SOC. WOA is used to automatically adjust and optimize weights and kernel parameters of MKRVM to improve estimation accuracy. The proposed model is validated with three lithium-ion batteries under different operating conditions. In contrast to other optimization algorithms, WOA has a better optimization effect and can estimate the SOC more accurately. In contrast to the single kernel function, the proposed multi-kernel function greatly improves the precision of the SOC estimation model. In contrast to the traditional method, the WOA-MKRVM has a higher precision of SOC estimation.

Self-cleaning of superhydrophobic nanostructured surfaces at low humidity enhanced by vertical electric field.

Self-cleaning is the key factor that makes superhydrophobic nanostructured materials have wide applications. The self-cleaning effect, however, strongly depends on formations and movement of water droplets on superhydrophobic nanostructured surfaces, which is greatly restricted at low humidity (< 7.6 g·kg-1). Therefore, we propose a self-cleaning method at low humidity in which the pollution is electro-aggregated and driven in the electric field to achieve the aggregation and cleaning large areas. The cleaning efficiency of this method is much higher than that of water droplet roll-off, and will not produce "pollution bands". A simplified numerical model describing pollution movements is presented. Simulation results are consistent with experimental results. The proposed method realizes the self-cleaning of superhydrophobic nanostructured surfaces above dew point curve for the first time, which extends applications of superhydrophobic nanostructured materials in low humidity, and is expected to solve self-cleaning problems of outdoor objects in low humidity areas (< 5.0 g·kg-1). Electronic Supplementary Material: Supplementary material (experimental procedures, computational details, modeling process, supplementary figures, tables, and videos) is available in the online version of this article at 10.1007/s12274-022-4093-0.

Investigation of the Impacts of Thermal Shock on Carbon Composite Materials.

Carbon composite is widely used in various fields, including the aerospace industry, electrical engineering, transportation engineering, etc. For electrified railways, the pantograph strip utilizes carbon composite as the current collector, which might bear multiple impacts from electrical, mechanical, or thermal aspects, from unwanted arcing, rain, and other diverse operation conditions. In this paper, a thermal shock damage experiment on the carbon composite of a pantograph strip was carried out. The thermal shock processes were realized by the adoption of muffle furnace heating and water cooling. The effect of thermal shock processes on carbon strip porosity, compressive strength, electrical resistivity, and surface topography were studied. In order to verify the mechanism of thermal shock damage to the pantograph strip, the porosity of the pantograph strip is discussed in detail. The results showed that the thermal shock process increased the porosity of the carbon strip and caused reductions in compressive strength and electrical resistivity. The multiple thermal shock processes caused irreversible damage to the pantograph strip, which was attributed to the spillover and scouring of large quantities of water vapor in the pores.

"Thermal Stabilization Effect" of Al2O3 nano-dopants improves the high-temperature dielectric performance of polyimide.

Insulation performance of the dielectrics under extreme conditions always attracts widespread attention in electrical and electronic field. How to improve the high-temperature dielectric properties of insulation materials is one of the key issues in insulation system design of electrical devices. This paper studies the temperature-dependent corona resistance of polyimide (PI)/Al2O3 nanocomposite films under high-frequency square-wave pulse conditions. Extended corona resistant lifetime under high-temperature conditions is experimentally observed in the 2 wt% nanocomposite samples. The "thermal stabilization effect" is proposed to explain this phenomenon which attributes to a new kind of trap band caused by nanoparticles. This effect brings about superior space charge characteristics and corona resistance under high temperature with certain nano-doping concentration. The proposed theory is experimentally demonstrated by space charge analysis and thermally stimulated current (TSC) tests. This discovered effect is of profound significance on improving high-temperature dielectric properties of nanocomposites towards various applications.

The influence of background music on recognition processes of Chinese characters: an ERP study.

In this paper, we employed RSS (rapid stream stimulation) paradigm to study the recognition processes of Chinese characters in background music. Real Chinese characters (upright or rotated) were used as target stimuli, while pseudo-words were used as background stimuli. Subjects were required to detect real characters while listening to Mozart's Sonata K. 448 and in silence. Both behavioral results and ERP results supported that Mozart's music mainly served as a distracter in the recognition processes of real Chinese characters in the experiment. The modulation of Mozart's music on RP (recognition potential) was different across different orientations of Chinese characters; in particular, the modulation of RP elicited by upright Chinese characters was more significant, suggesting that the music factor and orientation factor interact to affect the RP component. In brief, the simultaneous playing of Mozart's music did not improve subjects' performance in the detection of real Chinese characters.

An ERP study on whether semantic integration exists in processing ecologically unrelated audio-visual information.

In the present study, we used event-related potentials (ERPs) to examine whether semantic integration occurs for ecologically unrelated audio-visual information. Videos with synchronous audio-visual information were used as stimuli, where the auditory stimuli were sine wave sounds with different sound levels, and the visual stimuli were simple geometric figures with different areas. In the experiment, participants were shown an initial display containing a single shape (drawn from a set of 6 shapes) with a fixed size (14cm(2)) simultaneously with a 3500Hz tone of a fixed intensity (80dB). Following a short delay, another shape/tone pair was presented and the relationship between the size of the shape and the intensity of the tone varied across trials: in the V+A- condition, a large shape was paired with a soft tone; in the V+A+ condition, a large shape was paired with a loud tone, and so forth. The ERPs results revealed that N400 effect was elicited under the VA- condition (V+A- and V-A+) as compared to the VA+ condition (V+A+ and V-A-). It was shown that semantic integration would occur when simultaneous, ecologically unrelated auditory and visual stimuli enter the human brain. We considered that this semantic integration was based on semantic constraint of audio-visual information, which might come from the long-term learned association stored in the human brain and short-term experience of incoming information.

Semantic integration of differently asynchronous audio-visual information in videos of real-world events in cognitive processing: an ERP study.

In the real world, some of the auditory and visual information received by the human brain are temporally asynchronous. How is such information integrated in cognitive processing in the brain? In this paper, we aimed to study the semantic integration of differently asynchronous audio-visual information in cognitive processing using ERP (event-related potential) method. Subjects were presented with videos of real world events, in which the auditory and visual information are temporally asynchronous. When the critical action was prior to the sound, sounds incongruous with the preceding critical actions elicited a N400 effect when compared to congruous condition. This result demonstrates that semantic contextual integration indexed by N400 also applies to cognitive processing of multisensory information. In addition, the N400 effect is early in latency when contrasted with other visually induced N400 studies. It is shown that cross modal information is facilitated in time when contrasted with visual information in isolation. When the sound was prior to the critical action, a larger late positive wave was observed under the incongruous condition compared to congruous condition. P600 might represent a reanalysis process, in which the mismatch between the critical action and the preceding sound was evaluated. It is shown that environmental sound may affect the cognitive processing of a visual event.

Cognitive processing of traffic signs in immersive virtual reality environment: An ERP study.

The virtual reality environment can provide an immersive feeling as in the real word. So, using virtual reality technology to construct realistic experimental scenarios, the mechanism of cognitive processing in the human brain could be better studied. In this paper, we have designed an experiment, where through the presentation of traffic signs with correct or incorrect background colors in a virtual reality traffic environment, and studied the cognitive processing in the human brain using event-related potential (ERP) method. The results showed that whether the background colors of traffic signs were correct or not, the degrees of familiarity to these traffic signs in the human brain were similar, and the degree of contrast between the background colors and foreground colors of traffic signs would influence the degree of difficulty in cognitive processing. The degree of complexity in contents of traffic signs appears related to the cognitive speed in the human brain. In sum, simpler contents and larger contrast between the background colors and foreground colors of traffic signs would make the human brain respond faster.