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Table of Content

    30 August 2024, Volume 0 Issue 8
    Overview
    Review of Research on Low Voltage DC Arc Detection and Interruption Technology
    LI Xingwen, ZHANG Zhaozi, YANG Haowen, MENG Yu, WANG Qian, CHEN Silei
    2024, 0(8):  1-10.  doi:10.16628/j.cnki.2095-8188.2024.08.001
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    DC arc accurate detection and fast breaking technology is the key to ensure the safe,stable and reliable operation in low voltage DC power distribution systems.Firstly,DC arc fault detection technologies in the new scenario of transport are summarized.The multiple kinds of time-frequency domain feature construction algorithms and intelligent models based on the machine learning are analyzed,and a variety of DC arc fault localization techniques using electromagnetic and current signals are introduced.Secondly,the fast interruption technology for low voltage DC systems is reviewed,and the traditional mechanical circuit breaker interruption methods are summarized.The research progress of hybrid circuit breakers and solid-state circuit breakers based on the development of power electronic devices is introduced.Finally,the development direction of low voltage electrical design field is finally proposed,which helps provide references for relevant researchers.

    Review of Research on Low Voltage DC Series Fault Arc
    WANG Yao, SHENG Dejie, LI Teng, LIU Yuying, LAN Tianle, BAO Zhizhou, ZHU Tongwei
    2024, 0(8):  11-20.  doi:10.16628/j.cnki.2095-8188.2024.08.002
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    DC series fault arc has high concealment and difficult detection,which is an important cause of fire in low voltage DC system.The model of low voltage DC series fault arc,arc characteristics and detection methods,detection standards and devices are reviewed mainly.Based on the arc simulation model is used to understand the arc evolution process,the principle,application range and limitations of the common fault arc model are analyzed.Based on the physical and electrical signal features geuerated by fault arc,construction and detection methods the advantages and disadvantages of different feature detection methods are exponded.Finally,the relevant standards of DC series fault arc are interpreted,and the current research status and shortcomings of the test platform and products are discussed.The future research direction of low voltage DC fault arc is prospected.

    Research & Analysis
    Factors Affecting Near Pole Voltage Drop of Ferromagnetic Grating in Low Voltage Circuit Breakers
    SUN Hao, ZHOU Liujun, ZHANG Xinyu, WU Yi, NIU Chunping
    2024, 0(8):  21-26.  doi:10.16628/j.cnki.2095-8188.2024.08.003
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    The arc voltage in low voltage circuit breakers is directly related to the arc extinguishing performance of the circuit breaker,and the near pole voltage drop of the grating directly affects the magnitude of the arc voltage.Therefore,studying the near pole voltage drop of the grating can provide the assistance for optimizing the performance of the circuit breaker.A simplified experimental device is established,the voltage during the stable arc burning process at a DC current level of 250 A is measured,and the relationship curve between arc voltage and grating spacing is drown.The near pole voltage drop is obtained by fitting the curve.The factors affecting the near pole voltage drop are also explored from the aspects of grating material,thickness and current grade. The experimental results show that the near pole voltage drop of different materials will be different,but the grid thickness and current grade will not affect that.

    Research on Arc Re-strike Phenomenon and Cause During DC Contactor with Splitter Plates Breaking
    ZHAO Yifan, LI Jing, PENG Shidong
    2024, 0(8):  27-33.  doi:10.16628/j.cnki.2095-8188.2024.08.004
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    To investigate the physical mechanism of arc re-strike during DC contactor breaking,a magnetohydrodynanmic(MHD) model is established considering the inhomogeneous magnetic field distribution,and two kinds of arc re-strike phenomena were found under different copper vapor concentrations,including arc re-strike between splitter plates and arc re-strike between contacts.Arc re-strike between contacts will increase the arc duration significantly,so it should be avoided in the practical applications.The arc re-strike is related to the airflow vortex in the extinguishing chamber and the difference of gas velocity between the dynamic and static contacts.This study can provide the theoretical guidance and the quantitative data reference for the design of DC contactor.

    Study on Arc Reignition Influencing Factors of HVDC Relay Under Permanent Magnet Field
    LIN Zhen, ZHANG Peng, LIU Xiangjun
    2024, 0(8):  34-44.  doi:10.16628/j.cnki.2095-8188.2024.08.005
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    Arc reignition is an important reason for prolonging the disconnection time of highvoltage DC relays.In order to study the reignition characteristics inside the arc extinguishing chamber,the permanent magnet magnetic field was simulated by Comsol Multiphysics and the result was used as the basic condition for arc simulation.The dynamic characteristics of the arc and the reasons for arc reignition under the influence of magnetic field strength,breaking speed,contact shape,and atmospheric pressure were simulated and analyzed.A four factor three-level orthogonal experiment was designed,and the degree of influence of each factor on arc reignition was studied through simulation.The results indicate that the heat dissipation conditions in the arc gap and the electric field strength exerted by the arc voltage on the arc gap are important reasons for arc reignition.The worse the heat dissipation conditions or the higher the electric field strength exerted by the reverse arc voltage,the easier it is for arc reignition to occur.The degree of influence of four factors on arc reignition is in the order of breaking speed,gas pressure,contact shape,and magnetic field strength.

    Electric Field Analysis and Optimization Design of Low Voltage Circuit Breakers
    ZHANG Wei, ZHANG Yu, MA Yulin, HU Chen
    2024, 0(8):  45-49.  doi:10.16628/j.cnki.2095-8188.2024.08.006
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    Due to the uneven distribution of electric fields in low voltage circuit breakers,the partial discharge and even fire accidents are prone to occur during the actual operation.Therefore,the analysis and optimization design of electric fields in circuits breaker have become an important research topic.The low voltage circuit breakers on the DC side of inverters in a certain photovoltaic power station are taken as the research objects.The finite element software is used to analyze the electric field distribution under different voltage levels and different contact gaps.The structural optimization design is conducted on the weak insulation points of the circuit breaker.Finally,by comparing the maximum electric field intensity before and after optimization,it is demonstrated that the proposed optimized structure reduces the possibility of breakdown discharge.

    Detecting & Equipment
    Photovoltaic DC Arc Fault Detection Method Based on Attention Mechanism Optimizing Arc Characteristics
    XIE Zhenhua, LIU Yuying, HOU Linming, WANG Yao, ZHOU Jiawang, SHENG Dejie
    2024, 0(8):  50-56.  doi:10.16628/j.cnki.2095-8188.2024.08.007
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    The DC series arc fault in photovoltaic systems caused by insulation aging or loose wiring is highly prone to electrical fires.Therefore,the arc fault detection devices must be installed in photovoltaic systems.However,the arc fault detection devices easily malfunction due to DC-side high-frequency noise caused by shadow occlusion and inverter startup.A novel arc fault detection method is proposed based on attention weight screening of arc features by combining the attention mechanism with the 1d convolutional neural network.By visualizing the contribution weight of arc features,the critical feature bands of 8~18 kHz and 28~38 kHz are extracted,and the interference arc features bands in the 8~23 kHz frequency band are removed.It has been verified that the arc fault detection model trained with the key arc features can successfully avoid the false activation caused by shadow occlusion and inverter startup,and the an arc detection accuracy of 99.33%is ultimately achieved.

    Series Arc Fault Detection Based on Improved Dung Beetle Optimizer Optimized CNN-BiLSTM-Attention
    LI Haibo
    2024, 0(8):  57-68.  doi:10.16628/j.cnki.2095-8188.2024.08.008
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    Aiming at the problems of insufficient arc fault feature extraction and low detection accuracy,a multi-feature fusion improved dung beetle optimizer (IDBO) optimized fusion of the attention mechanism of convolutional neural network (CNN) and bidirectional long short term memory (BiLSTM) neural network series arc fault detection method is proposed.The current time-domain,frequency-domain,time-frequency domain,and signal autoregressive parameter model features are extracted through an experimental platform.The kernel principal component analysis (KPCA) is used to reduce the dimensionality of the features,and they are fused to obtain the feature vectors as the input vectors for CNN-BiLSTM-Attention.The cubic chaotic mapping,the spiral search strategy,the dynamic weight coefficients,and the gaussian cauchy mutation strategy are introduced to improve the dung beetle optimizer.An improved dung beetle optimizer is used to optimize the hyperparameters of CNN-BiLSTM-Attention for the series arc fault diagnosis.The results show that the proposed method can achieve an accuracy of 97.92% in detecting fault arcs and efficiently identify the series arc faults.

    Method for Diagnosing Line Fault Arcs in Three-Phase AC Motors Under Vibration Conditions
    SUN Yifan, LIU Yujun, ZHANG Shuwang, QI Dongqian, CHEN Guanghua, GUO Fengyi
    2024, 0(8):  69-76.  doi:10.16628/j.cnki.2095-8188.2024.08.009
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    Arc fault is an air breakdown phenomenon caused by loose electrical connections or aging insulation in wiring or equipment,which can lead to equipment damage,circuit faults,and even electrical fires.To accurately identify three-phase AC motor vibration fault arcs,a fault diagnosis model based on one-dimensional convolutional neural networks (1D CNN) combined with the pelican optimization algorithm (POA)is proposed.The proposed model can be trained directly using one-dimensional temporal current data output from the host computer.The POA is utilized to find the optimal hyperparameters of the 1D CNN,enhancing the model’s recognition capability.After analyzing the feature extraction effectiveness of the model using the t-SNE algorithm,its validity is confirmed.Test results show that the fault arc identification accuracy reaches 99.72%.The proposed method is more convenient and superior in performance compared to existing technologies.

    Design of Photovoltaic Series Arc Fault Detection Device Based on Lightweight CNN and Feature Threshold
    WANG Zhaorui, HE Jiantao, LI Zhitong, BAO Guanghai
    2024, 0(8):  77-85.  doi:10.16628/j.cnki.2095-8188.2024.08.010
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    To ensure the safe and stable operation of photovoltaic (PV) systems,a PV series arc fault detection algorithm based on a lightweight convolutional neural network combined with feature threshold methods is proposed.To addresses the impacts of inverter abnormal conditions and the time-varying nature of PV arrays on signal characteristics,as well as the signal characteristic differences caused by varying arc lengths (0.05~10.00 mm),by utilizing high-frequency coupled signals as feature signals and combining neural network algorithms with feature threshold methods,the algorithm detects series arc faults in PV circuits.Finally,a prototype of a PV series arc fault detection device is created.The experimental tests show that the prototype cuts off arc faults in an average time of 177.1 ms and does not produce false positives under the inverter abnormal conditions.

    DC Arc Fault Detection Device Based on Time-Frequency Domain Parametric Analysis
    ZHOU Xue, DAI Wenxin
    2024, 0(8):  86-90.  doi:10.16628/j.cnki.2095-8188.2024.08.011
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    However,the existing DC arc fault detection methods are easy to be affected by the environment,which can not achieve accurate detection,and the core algorithm is complex,difficult to deploy in practical applications. To solve these problems,a DC arc fault detection algorithm based on short-time Fourier transform is designed. By analyzing the amplitude variation trend of arc current signal in frequency domain,the spectrum is divided into different stages,the ratio of different stages is calculated,and the threshold value is obtained according to a large number of experimental results to realize arc fault detection. Finally,a suitable single chip microcomputer is selected to implement the detection algorithm and tested to verify the effectiveness of the proposed algorithm. The results show that the algorithm is not only able to detect arcing faults in a timely manner with multiple types of loads connected in parallel,the accuracy of detection can reach 94.4%. In addition,the algorithm has a misjudgment rate of 0 in the face of normal state currents,which indicates that it has good robustness.

    Application
    Research and Application of Detection and Protection Techniques for DC Arc Faults in ESSs
    ZHANG Xian, ZHANG Ran, HOU Peng, LIU Xiaoyu
    2024, 0(8):  91-99.  doi:10.16628/j.cnki.2095-8188.2024.08.012
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    To investigate arc detection & protection methods in ESSs,an arc detection algorithm based on time-domain triggering and frequency-domain protection is proposed.Firstly,an arc detection hardware was designed,including self-check,gain,and filtering circuits.This hardware amplifies the peak-to-peak current variation difference after an arc occurs in the energy storage system,through the use of a TMR current sensor.Secondly,time-domain features are extracted using a delayed sliding window method for use as trigger conditions in the arc detection algorithm.Once triggered,frequency-domain features are calculated and input into the arc detection model.If it is determined to be a genuine arc event,the ESS immediately performs a shutdown action.The test results of the algorithm show that this method can accurately identify the arc faults in the energy storage system under series circuits,with a detection accuracy rate of 100%,meeting the requirements for practical applications.