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SSRN 2023年

作者： Yang, Jingmin Gao, Li Wang, Yajun Rao, Bo Yang, Chengyu Chen, Zhipeng International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Guangzhou Power Supply Bureau Guangdong Power Grid Co. Ltd China

A multi-chord interferometer system is established on the HUST Field Reversed Configuration (HFRC). Configured as a Mach-Zehnder heterodyne interferometer with seven probe chords distributed in the HFRC middle plane, the system is designed to estimate the integral density within the collisional-merging and compression region of the device. With a spatial resolution of 2cm and a radial coverage range from -7 cm to 31 cm, it provides a density measurement range of 1×1019~1×1020 m-2 . A 10.6μm CO2 laser serves as the laser source, while an acoustic-optic modulator (AOM) generates a modulation frequency of 40 MHz corresponding to a temporal resolution of 25 ns. To reduce frequency drift and vibration noise, a laser interferometric reference chord and a fitting vibration compensation method are implemented. Preliminary experimental results show that the phase resolution within has been achieved, which corresponds to a minimum measurable density of ~2×1018 m-2 . Beyond the density profile measurements, the multi-chord interferometer system plays a pivotal role in observing instabilities of FRC plasmas. © 2023, The Authors. All rights reserved.

关键词： Interferometers

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First-Principles Study on the Cu-Cr Interfaces in Vacuum Circuit Breaker Contacts

SSRN

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SSRN 2023年

作者： Gui, Xianxian Wu, Chuanqi Yuan, Zhao Liu, Liming Wang, Cao Chen, Lixue Liu, Xueliang Ye, Jingjing Liu, Shan State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology Hubei Wuhan430074 China Key Lab Pulsed Power Technol Minist Educ School of Electrical and Electronic Engineering Hubei Wuhan430074 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430077 China

Based on the experimental phenomenon that the breakdown position of the CuCr contact head in a vacuum circuit breaker may be the Cu-Cr phase boundary, the Cu-Cr interfacial binding models have been developed. The work function, density of states, work of interfacial separation, interfacial energy, charge transfer, band energy, and charge density distribution of Cu, Cr and Cu-Cr phase boundaries are calculated from the first principles. The internal properties of CuCr contact breakdown have been analyzed from the microscopic atomistic point of view. The CuCr interface junction was found to have a smaller work function than pure copper and pure chromium. The reduced work function is prone to field emission, thus increasing the possibility of electrical breakdown. Three interfacial binding models Cu(110)/Cr(110), Cu(111)/Cr(111) and Cu(110)/Cr(100) are established in this paper. By comparing the separation work and interfacial energy, it is found that Cu(111)/Cr(111) is relatively stable. In this paper, we provide a theoretical basis for increasing the voltage strength of vacuum circuit breakers. © 2023, The Authors. All rights reserved.

关键词： Timing circuits

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Probing dynamics and ion structuring of imidazolium ionic liquid confined at charged graphene surfaces using graphene colloid probe AFM

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摩擦（英文版） 2025年第6期13卷 151-161页

作者： Muqiu Wu Zhongyang Dai Fan Zhang Faiz Ullah Shah Enrico Gnecco Yijun Shi Braham Prakash Rong An School of Materials Science and Engineering/Herbert Gleiter Institute of Nanoscience Nanjing University of Science and TechnologyNanjing 210094China State Key Laboratory of Solid Lubrication Lanzhou Institute of Chemical PhysicsChinese Academy of SciencesLanzhou 730000China Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai 264006China High Performance Computing Department National Supercomputing Center in ShenzhenShenzhen 518055China Department of Engineering and Design School of Engineering and InformationUniversity of SussexBrighton BN1 9RHUK Chemistry of Interfaces Luleå University of TechnologyLuleå 97187Sweden Marian Smoluchowski Institute of Physics Jagiellonian UniversityKrakow 30-348Poland Division of Machine Elements Luleå University of TechnologyLuleå 97187Sweden School of Materials Science and Engineering/Herbert Gleiter Institute of Nanoscience Nanjing University of Science and TechnologyNanjing 210094China Shandong Laboratory of Advanced Materials and Green Manufacturing at Yantai Yantai 264006China

Driven by the potential applications of ionic liquid(IL)flow for charging graphene-based surfaces in many emerging technologies,recent research efforts have focused on understanding ion dynamics and structuring at IL-graphene ***,graphene colloid probe(GrP)atomic force microscopy(AFM)was used to probe the dynamics and ion structuring of 1-butyl-3-methylimidazolium tetrafluoroborate at graphene surfaces under various bias *** particular,the AFM-measured nanofriction provides a good measure of the dynamic properties of the ILs at graphene *** with the IL at the unbiased graphene surface(0 V),the charged graphene surfaces with either negative(-1,-2 V)or positive(+1,+2 V)voltages favor a reduction in the friction coefficient by the IL.A higher magnitude of the bias voltage applied on the graphene surface with either sign(-2 or+2 V)results in a smaller friction coefficient than that at-1 and+1 *** combination with the AFM-probed contact stiffness,adhesion forces,and ion structuring force curves with an ion orientational distribution according to molecular dynamics(MD)simulations,we discovered that the unbiased graphene surface(0 V)possesses randomly structured IL ions and that the graphene colloid probe is more likely to become stuck,resulting in more energy dissipation to contribute to a larger friction *** of the graphene surface under either negative or positive voltages resulted in uniformly arranged ions,which produced a more ordered ion structure and,thus,a smoother sliding plane to reduce the friction *** impedance spectroscopy(EIS)for the IL with graphene as an electrode demonstrated a greater ionic conductivity in the IL paired with the biased graphene than in the unbiased one,implying faster ion movement at the charged graphene,which is beneficial for reducing the friction coefficient.

关键词： nanofriction graphene charge ionic liquid(IL) IL arrangement simulation

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Photonic Jet with Tunable Focus Based on Water Droplets Freezing from the Outside in 10

Photonic Jet with Tunable Focus Based on Water Droplets Free...

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Nano-optoelectronics and Micro/Nano-photonics X 2024

作者： Minin, Oleg V. Zhou, Song Minin, Igor V. Li, Renxian Jiao, Yuexiao Xiao, Li Shi, Zhuoyuan Zhang, Yuan Wang, Zhe Yang, Ruiping Tang, Huan Siberian State University of Geosystems and Technologies Novosibirsk630108 Russia Jiangsu Key Laboratory of Advanced Manufacturing Technology Faculty of Mechanical and Material Engineering Huaiyin Institute of Technology Huai'an223003 China Novosibirsk Branch of Rzhanov Institute of Semiconductor Physics SBRAS Technological Design Institute of Applied Microelectronics Lavrentieva ave. 2/1 Novosibirsk630090 Russia School of Physics Xidian University Xi'an710071 China School of Physics and Electrical Engineering Weinan Normal University Weinan714099 China

ISBN: (纸本)9781510682160

Water droplets are a highly abundant phase-change material to realize tunable optical lenses. We demonstrated for the first time that freezing mesoscale water droplets could be used as a tunable optical lens, such that freezing becomes an asset despite the low absolute values of the refractive indices of the shell and core materials and their optical contrast. It is shown that the dielectric shell of mesoscale water droplets in solid ice allows controlling both the maximum field intensity and the focus position of the formed photonic nanojet. The ice formation with air bubbles during the freezing of a water droplet is appropriate for a dynamic increase in the range of change of the focal position compared to solid ice. The proposed concept of a tunable spherical lens based on a freezing water drop can be used for microscopy and optical trapping in "green"mesotronics. © 2024 SPIE.

关键词： Refractive index

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Insulator Defect Detection Based on Improved YOLOv5 Model

Insulator Defect Detection Based on Improved YOLOv5 Model

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Software engineering and Artificial Intelligence (SEAI), IEEE International Conference on

作者： Yongxin Chen Zhenan Du Hengxuan Li Kanjun Zhang Pei Wen State Grid Hubei Electric Power Research Institute Wuhan Hubei China State Key Laboratory of Advanced Electromagnetic Engineering and Technology (HUST) Wuhan Hubei China State Grid Hubei Electric Power Co. Ltd Wuhan Hubei China NR Electric Co. Ltd Nanjing Jiangsu China

ISBN: (数字)9798350374346

ISBN: (纸本)9798350374353

In the power system, insulator is a very important type of power equipment. The defects in insulator will directly affect the stability of the system and the safety of workers. In order to quickly and accurately detect the defect of insulators in the high-voltage lines of the power grid, a detection method based on improved YOLOv5 model is proposed. For the convolutional layers in CSPLayer modules which are associated with feature output, self attention mechanism is introduced to improve the feature extraction ability of the model; and deep separable convolution is introduced to improve the detection efficiency. The experimental study is performed by comparing the proposed model with Faster R-CNN, YOLOv3, YOLOv4, and YOLOv5s. The experimental results show that the average detection accuracy of the proposed model is 94.79%, and the detection speed is 63.9FPS, which is superior to other methods under the same conditions.

关键词： YOLO Accuracy Attention mechanisms Convolution Power system stability Insulators Feature extraction

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Fine Tune or not? Evaluation of Transfer Methods for Power System Transient Stability Analysis

Fine Tune or not? Evaluation of Transfer Methods for Power S...

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International Conference on Power System technology (POWERCON)

作者： Zhouping Li Wei Yao Yanhao Huang Zhongtuo Shi Yong Tang Jinyu Wen State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665407380

Many methods based on deep learning (DL) have been developed to handle the power system stability analysis problem with satisfactory results. A well-trained DL model on the source domain may encounter the performance reduction problem when applied to a target domain of different distributions. Thus, transfer learning is needed to transfer knowledge learned from the source domain to the target domain which can reduce the cost of collecting more data and labels. As a commonly used scheme, fine-tuning is thought to be able to share the ability to extract high-level features to the target model by reusing the parameters of the pretrained model. Several fine-tune schemes are compared to evaluate the benefits brought by fine-tuning. The results show that freezing the layers of the DL model may restrict the tuning process in terms of convergence speed and test accuracy. A simple but effective mixed dataset training scheme is demonstrated to work better than fine-tune schemes.

关键词： Training Deep learning Analytical models Simulation Transfer learning Power system stability Feature extraction

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From a design strategy for metamaterial sensors to interdigitated metamaterial sensors

From a design strategy for metamaterial sensors to interdigi...

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International Conference on Infrared and Millimeter Waves

作者： Lei Cao Fanqi Meng Yannik Loth Merle Richter Anna Katharina Wigger Maira Pérez Sosa Alaa Jabbar Jumaah Shihab Al-Daffaie Peter Haring Bolívar Hartmut G. Roskos State Key Laboratory of Advanced Electromagnetic Technology Huazhong University of Science and Technology Wuhan China Physikalisches Institute Goethe-Universität Frankfurt Frankfurt am Main Germany Institute for High Frequencies and Quantum Electronics University of Siegen Siegen Germany Department of Electrical Engineering Eindhoven University of Technology AE Eindhoven Netherlands

ISBN: (数字)9798350370324

ISBN: (纸本)9798350370331

We present a strategy for the design of metamaterial sensors to be used for detecting small amounts of dielectric materials by the shift of the metamaterial’s resonance frequency. The amount of frequency shift depends on intrinsic properties (electric field distribution, Q-factor, and mode volume) of the metamaterial’s bare resonators, as well as the overlap volume of its high-electric-field zone(s) and the analyte. Guided by the simplified dielectric perturbation theory, interdigitated electric split-ring resonators (ID-eSRR) are devised to significantly enhance the detection sensitivity compared to eSRRs without interdigitated fingers. The ID-eSRR's fingers redistribute the electric field, creating strongly localized field enhancement which boosts interaction with the analyte. Furthermore, the periodic change of field direction in the region of the interdigitated fingers significantly reduces radiation loss, leading to a higher Q-factor. Experiments with ID-eSRR sensors operating at around 300 GHz demonstrate a remarkable 33.5-GHz frequency shift upon deposition of a 150-nm-thick SiO 2 layer as an analyte simulant, with a figure of merit (FOM) improvement of over 50 times compared to structures without interdigitated fingers. This rational design offers a promising avenue for the development of highly sensitive detectors of thin dielectric films and trace amounts of biomolecules.

关键词： Q-factor Silicon compounds Time-frequency analysis Sensitivity Fingers Resonant frequency Metamaterials Sensors Resonators Electric fields

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Short-Circuit Current Calculation Method for Power System with Renewable Energy Source and HVDC System

Short-Circuit Current Calculation Method for Power System wi...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Yuxi Wang Kun Qian Minghao Wen State Key Laboratory of Advanced Electromagnetic Engineering and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665434263

The short-circuit currents characteristics of renewable energy source (RES) and high voltage direct current (HVDC) system are different from the short-circuit currents characteristics of traditional synchronous generator (SG). According to the fault-ride-through (FRT) requirements of different types of RES, the steady-state short-circuit currents calculation models of doubly-fed induction generator (DFIG) and inverter-interface renewable energy source (IIRES) such as photovoltaic (PV) and direct-drive permanent magnetic synchronous generator (D-PMSG) are built in this paper. Based on the converter switching function model and the HVDC control system, the calculation model of HVDC system steady-state short-circuit currents is built. Combined with RES and HVDC system short-circuit currents calculation models, a short-circuit currents calculation method for power system with multiple types RES and HVDC under ac power system fault is proposed. Finally, the effectiveness of the proposed method of short-circuit currents calculation is verified by simulation cases.

关键词： Renewable energy sources Short-circuit currents HVDC transmission Doubly fed induction generators System integration Synchronous generators Mathematical models

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Prediction of Transformer Oil Temperature Based on Feature Selection and Deep Neural Network

Prediction of Transformer Oil Temperature Based on Feature S...

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IEEE International Conference on Software engineering and Service Sciences (ICSESS)

作者： Yongxin Chen Shiping E Zhenan Du Kanjun Zhang Gaofeng Zhang State Grid Hubei Electric Power Research Institute Wuhan Hubei China State Key Laboratory of Advanced Electromagnetic Engineering and Technology (HUST) Wuhan Hubei China State Grid Hubei Electric Power Co. Ltd Wuhan Hubei China NR Electric Co. Ltd Nanjing Jiangsu China

ISBN: (数字)9798350389166

ISBN: (纸本)9798350389173

The prediction of transformer oil temperature is a time series prediction task, which is of great significance for the maintenance of substation equipment. In the process of multivariate prediction, not all variables are strongly correlated with the prediction of the transformer oil temperature. This paper proposes a transformer oil temperature prediction method based on Feature Selection and Smooth Residual blocks, named FSSR. A two-stage feature selection method is applied to select the related features. Residual connection is applied to convolutional neural network in smooth residual blocks. Experimental study is performed on the ETTh1 and ETTh2 datasets. With different number of selected features, the best one can reduce the average MSE (mean squared error) by at least 20% than the worst one. Compared to the baseline models, FSSR has the best accuracy. Compared with LSTM (Long Short-Term Memory network), the average MSE of FSSR decreased by 27.62% on the ETTh1 dataset, and 39.34% on the ETTh2 dataset.

关键词： Temperature distribution Substations Oils Time series analysis Oil insulation Predictive models Transformers Feature extraction Convolutional neural networks Long short term memory

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Cross-Tokamak Deployment Study of Plasma Disruption Predictors Based on Convolutional Autoencoder

arXiv

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arXiv 2023年

作者： Ai, X.K. Zheng, W. Zhang, M. Ding, Y.H. Chen, D.L. Chen, Z.Y. Shen, C.S. Guo, B.H. Wang, N.C. Yang, Z.J. Chen, Z.P. Pan, Y. Shen, B. Xiao, B.J. Team, J-Text State Key Laboratory of Advanced Electromagnetic Technology International Joint Research Laboratory of Magnetic Confinement Fusion and Plasma Physics School of Electrical and Electronic Engineering Huazhong University of Science and Technology Wuhan430074 China Institute of Plasma Physics Chinese Academy of Sciences Hefei230031 China

In the initial stages of operation for future tokamak, facing limited data availability, deploying data-driven disruption predictors requires optimal performance with minimal use of new device data. This paper studies the issue of data utilization in data-driven disruption predictor during cross tokamak deployment. Current predictors primarily employ supervised learning methods and require a large number of disruption and non-disruption shots for training. However, the scarcity and high cost of obtaining disruption shots for future tokamaks result in imbalanced training datasets, reducing the performance of supervised learning predictors. To solve this problem, we propose the Enhanced Convolutional Autoencoder Anomaly Detection (E-CAAD) predictor. E-CAAD can be only trained by normal samples from non-disruption shots and can also be trained by disruption precursor samples when disruption shots occur. This model not only overcomes the sample imbalance in supervised learning predictors, but also overcomes the inefficient dataset utilization faced by traditional anomaly detection predictors that cannot use disruption precursor samples for training, making it more suitable for the unpredictable datasets of future tokamaks. Compared to traditional anomaly detection predictor, the E-CAAD predictor performs better in disruption prediction and is deployed faster on new devices. Additionally, we explore strategies to accelerate deployment of E-CAAD predictor on the new device by using data from existing devices. Two deployment strategies are presented: mixing data from existing devices and fine-tuning the predictor trained on existing devices. Our comparisons indicate that the data from existing device can accelerate the deployment of predictor on new device. Notably, the fine-tuning strategy yields the fastest deployment on new device among the designed strategies. Copyright © 2023, The Authors. All rights reserved.

关键词： Anomaly detection

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