Series arc faults (SAFs) are a primary cause of electrical fires in building distribution systems, requiring reliable detection to mitigate fire risks and ensure operational safety. Existing methods, though effective under simplified scenarios, suffer from false alarms and missed detections in complex scenarios involving diverse load types, circuit topologies, arc-generating modes, and arc-like conditions. To address the issues, this paper proposes a SAF detection method that integrates a dual-signal multi-timescale feature extraction framework, an arc fault decision tree, and arc fault decision criteria. Firstly, an experimental platform simulating SAFs in building distribution systems is established, and the arcing characteristics are analyzed through key electrical parameters—arc voltage, current, and zero-sequence current coupling signal—across four stages: arc-igniting, arcing, arc-extinguishing, and zero-current under different arc-generating modes. Secondly, based on these characteristics, with a particular focus on the current and zero-sequence current coupling signal, a comprehensive arc fault feature set is constructed by extracting features from both single-cycle and multi-cycle perspectives. Thirdly, the arc fault decision tree is trained using the constructed feature set, and the arc fault decision criteria are designed based on the persistence characteristics of arc faults. Finally, the superiority of the proposed method is demonstrated through offline tests, online verification on a self-developed prototype, anti-interference experiments, and a comparative analysis. Experimental results show that the proposed method achieves a detection accuracy of 99.20 % under test conditions encompassing thirteen loads, four circuit topologies, and two arc-generating modes, outperforming five representative benchmark methods under identical test conditions. Furthermore, during 360 online tests covering both training conditions and previously unseen real-world operating conditions, the proposed method satisfied standard detection time requirements in 354 tests while maintaining zero false alarms under arc-like conditions such as load switching.

Series arc fault (SAF) detection is crucial for mitigating electrical fires in building distribution systems and ensuring occupant and property safety. However, existing methods can only detect SAFs in limited or idealized scenarios, often suffering from false alarms and missed detections in more complex real-world systems. To address these issues, this article proposes a novel SAF detection method combining physics-guided feature extraction with an entropy-enhanced weighted decision tree. First, an experimental platform simulating SAFs in building distribution systems is constructed, and the effectiveness of the zero-sequence current coupling signal (ZSCCS) as a detection signal is verified through the systematic analysis and comparison. Second, qualitative characteristics of pulses-including positional distribution, amplitude trends, and occurrence frequency-are analyzed, while theoretical correlations between arcing processes and ZSCCS waveform variations are established through RLC-based circuit modeling. Third, four time-frequency domain features are extracted to quantify fault characteristics in ZSCCS, and a detection algorithm is designed based on these features, an entropy-enhanced weighted decision tree, and arc fault decision criteria. Finally, the method's effectiveness is validated through both offline and online experiments, as well as anti-interference tests. The results show that the proposed method achieves 98.91% detection accuracy (24960 samples) under complex scenarios involving 13 loads, four circuit topologies, and two arc-generating modes, outperforming existing methods. Furthermore, it has been implemented in a self-developed arc fault detection device (AFDD), demonstrating its potential for engineering applications in building distribution systems.

Series arc fault (SAF) is one of the main causes of electric fire hazards. However, the arcing current features are different under different load types, which makes SAF detection challenging. This article proposes a method for detecting SAFs in low-voltage AC distribution networks based on load classification and convolutional neural network (CNN). Firstly, an experimental platform is constructed to simulate arc faults according to the standard IEC 62606. The data is collected from eight different loads, and eight loads are divided into four categories by K-means clustering. Then, a detection method is designed by fusing the CNN and arc fault detection criterion. Finally, an online arc fault detection device (AFDD) is developed by deploying the proposed method to an embedded device, and the accuracy, applicability, and stability of the proposed method are evaluated by the AFDD. The results show that the detection accuracy of the proposed method under trained loads and untrained loads can reach 95% and 96.67%, respectively. Thus, this work can provide a reference for developing AFDD.

Series arc faults (SAFs) are important safety issues in low-voltage ac distribution networks. However, the load type and circuit topology are increasing, and arcing current features are easily affected by these factors, which makes SAF detection very challenging. This article proposes a new method for detecting SAFs in real scenarios containing multiple load types and complex circuit topologies. The proposed method uses the branch voltage coupling signal (BVCS) as the detection signal. First, the wavelet transform is employed to preprocess the BVCS to simplify the representation of fault components. Second, the absolute value sum of wavelet transform detail coefficients (AVSDCs) is utilized to characterize them. Then, the complete detection algorithm is designed based on the difference of AVSDC between normal conditions and arc faults and the continuity of SAFs. Finally, an online arc fault detection device (AFDD) is developed to validate the accuracy of the proposed method and its generalization ability. The results show that the proposed method has good detection accuracy under complex working conditions with multiple load types and circuit topologies.

Arc fault is one of the main causes of electrical fire hazards. Most research is mainly based on current signals to detect arc faults. However, load type and other factors easily affect the current signal, which easily causes missed judgment and misjudgment of switches. To solve this issue, this paper proposes a detection method based on the high-frequency zero-sequence current coupling signal for low-voltage series AC arc faults. First, an experimental platform was built for low-voltage series AC arc fault according to the IEC 62606 standard, and the high-frequency zero-sequence current coupling signal of ten typical loads under different line conditions was collected. Then, the characteristics of zero-sequence current under different loads and line conditions were analyzed, and the arc fault detection algorithm was developed according to the different characteristics of the line’s normal and arc-fault states. Finally, the effectiveness, stability, and applicability of the arc fault detection method were verified by various experimental conditions with a prototype. The result shows that the detection accuracy of the presented method is about 98%, which has a certain guiding significance for the development of arc fault detection devices.

故障电弧是影响配电网络安全可靠运行的主要问题之一,研制故障电弧保护开关能够有效降低因故障电弧引发的电气火灾事故,从而保障配电网络安全可靠运行.但故障电弧辨识因线路负载、燃弧条件等原因而存在一定复杂性,该文在分析电弧燃烧特点的基础上介绍了一种多特征融合的故障电弧辨识方法.首先,借助高速相机在微小时间尺度内分析了燃弧特性,电弧不稳定性影响回路电流谐波含量;然后,从时域、频域和信号无序度三个方面分析了回路电流在线路正常和发生电弧故障时的差异,并提取了电流平均值、谐波幅值、小波能量熵三个特征量,结合各种负载特征变化的共同点确定了线路正常与故障的特征量阈值范围;最后,通过制作样机对故障电弧辨识方法的有效性和稳定性进行了验证.结果表明,所述方法在实际工程应用中的平均辨识准确率为 90％,满足工程应用中的准确性和稳定性要求.

作为一种新兴的环保绝缘气体,七氟异丁腈(C4F7N)因其优异的绝缘性能和远低于六氟化硫(SF6)的全球增温潜势引起了研究人员的广泛关注.在C4F7N电子碰撞截面等基础数据不完整的情况下,文中基于气体流注放电判据,提出了C4F7N?CO2混合气体有效电离反应系数的计算方法,分析了C4F7N?CO2混合气体的临界击穿场强及绝缘性能的变化规律.研究结果表明,当C4F7N气体的热化系数为1.2或1.4时,C4F7N?CO2混合气体的临界击穿场强计算值与实验数据吻合较好.此外,C4F7N?CO2混合气体临界击穿场强随气体比例的变化曲线均存在明显的非线性特点.当C4F7N含量较低时,混合气体的临界击穿场强随C4F7N含量增加迅速增大,之后则近似呈线性增长趋势.

为探究干燥空气和N2从温升角度替代SF6气体的可能性,针对KYN28-12型中压开关柜,通过有限元分析法,构建三维温度场-流场的耦合模型,并在3种绝缘气体下,研究中压开关柜温升特性.研究结果表明,在3种绝缘气体下,具有相似的温度场分布,采用干燥空气和N2开关柜的温升要高于SF6气体,高出约5～9℃;3种气体中流场分布规律也基本一致,但SF6气体流速较慢,最高0.12 m/s,干燥空气和N2的流速类似,最高流速0.16 m/s.研究结果认为从温升方面考虑,满足绝缘条件下,干燥空气和N2替代SF6可行,但需要注意较SF6更高的温升,流场分布特性可为干燥空气和N2开关柜降低温升研究提供一定的理论依据.

In order to master the arc characteristics of high-voltage SF6 circuit breaker during the breaking process, the arc characteristics detection method of SF6 circuit breaker is studied. The physical parameters such as arc voltage, current, nozzle dynamic pressure, and after arc current are tested. The relationship between arc quenching peak, after arc current value, nozzle throat pressure change and expected short-circuit current and charging pressure is analyzed. The results show that the increase of short-circuit current is expected to reduce the dielectric recovery strength of the arc gap when the arc current crosses zero, and the increase of charging pressure is conducive to the improvement of dielectric recovery strength of the arc gap when the arc current crosses zero.

低压断路器是低压交直流配电系统控制和保护的主要器件,电气寿命是衡量其性能的指标之一.相较于低压交流断路器,直流断路器没有电流过零灭弧特征,其灭弧及寿命评估不易.本文研究无极性直流微型断路器电寿命评估,阐述了无极性直流微型断路器结构,搭建试验平台进行电弧特性分析和电寿命试验,并建立电寿命预测模型.试验结果表明,电弧对触头的稳定燃烧是断路器电寿命减少的主要原因,本文所提出的静触头烧蚀量与燃弧能量积累量的线性模型可有效预测断路器电寿命.

由电弧烧蚀触头材料而引起触头失效是导致微型断路器电寿命劣化的主要原因,研究微型断路器电寿命评估方法对提高用电网络安全性和可靠性具有重要意义.该文以额定电流16A的微型断路器作为电寿命试验对象,利用高速摄像机观察触头间电弧的运动过程,从电弧电压中提取反映断路器电寿命退化过程的特征量,研究累积燃弧能量、跌落时间与触头烧蚀量之间的对应关系,并利用这两个趋势特征量构建微型断路器电寿命评估模型.研究结果表明,电压跌落时间具有随开断次数增加而增大的趋势.利用试验数据对模型评估准确度进行测试,测试结果表明,该文提出的方法适用不同开断电流的情形,可以用于微型断路器电寿命的评估,评估误差在可接受范围内.

搭建了小型断路器电寿命试验装置,对试验样品开展了电寿命试验.利用试验过程中采集的电弧电压和电弧电流信号,提取反映小型断路器电寿命退化过程的特征量,并以提取的特征量为输入参数,利用BP神经网络构建了小型断路器电寿命评估模型.研究结果表明,提取的相对合闸时间和跌落时间能够显著反映小型断路器电寿命劣化过程;经过试验数据测试,利用趋势特征量构建的电寿命评估模型具有较好的评估效果.

Fluorides and their CO $$_{2}$$ mixtures are considered as promising choices of SF $$_{6}$$ -substitute gases due to their low global warming potentials and high dielectric strength. This paper investigates physical and chemical properties and breakdown characteristics of different common fluorides and their mixtures for application in the power system. For dielectric characteristics of fluorides and their mixtures, their power frequency breakdown voltages are determined experimentally, and the results are compared with SF $$_{6}$$ . For physical and chemical characteristics, the saturated vapor pressures, global warming potentials (GWPs), toxicities, prices of fluorides and their mixtures are calculated, and the corresponding characteristic curves are obtained. Based on characteristics mentioned above, a method for obtaining the optimal gas parameters of a specific fluoride mixture is proposed, which can be used as a feasible solution to find an SF $$_{6}$$ alternative gas that meets the application requirements. The results show that CO $$_{2}$$ mixtures with hexafluoropropene (HFP), 1,1,1,2-tetrafluoroethane (HFC-134a) and perfluoroisobutyronitrile (C4-PFN) are three types of good SF $$_{6}$$ -substitute gases. HFP/CO $$_{2}$$ mixtures with HFP component ratios 0.32 to 0.6 are suitable for applications at 0.1 to 0.3 MPa. HFC-134a/CO $$_{2}$$ mixtures (HFC-134a component ratios from 0.16 to 0.2) are suitable for applications at 0.3 to 0.5 MPa. C4-PFN/CO $$_{2}$$ mixtures (C4-PFN component ratios from 0.09 to 0.12) could be used for the applications at 0.1 to 0.5 MPa, regardless of the gas price.

直流微型断路器是低压配电系统最为关键的保护和控制设备,其分断灭弧快慢对系统及本身有重要的影响.在目前有极性磁吹灭弧研究基础上进行优化,提出了无极性磁吹灭弧方案,解决了微型断路器正反向使用的难题,并研究了32 A电流等级下不同磁场强度对无极性直流微型断路器灭弧的影响研究.结果 表明,无极性磁吹灭弧对断路器正反向使用效果良好,在4种不同磁场强度下,2片120 mT磁场强度时灭弧时间最短,提高了微型断路器的使用效率,延长了直流断路器的电寿命.

CO2气体作为潜在的SF6替代气体,近年来受到了业界的广泛关注,电击穿特性是评估其性能的关键指标.文中基于气体流注放电判据,推导出了计算击穿电压和临界电子温度的数学表达式,建立了描述CO2与O2混合气体电击穿的宏观变量(击穿电压、击穿电场与压力比)与基本微观变量(临界电子温度)之间的关系,从微观层面研究了混合气体在不同压力和电极间距下的电击穿特性.结果表明,随压力与电极间距乘积值增大,临界电子温度先迅速减小,而后趋于一定值;CO2-O2混合气体的临界电子温度随CO2体积分数增大而明显减小,并表现出较明显的协同效应,纯CO2的临界电子温度约为2.34 eV,约为纯O2(3.54 eV)的66.1％,在CO2中加入一定比例O2能有效提高其电击穿特性.此外,通过文中CO2-O2混合气体击穿电场与压力比的计算值与文献实验值对比,验证了文中计算方法与参数选取的有效性.

Due to the increasing short-circuit current levels in modern power grids, it is planned to install a current limiter based on a high-coupling split reactor in the 500kV power grid to limit the short-circuit current. However, the connection of the current limiter will cause the transient recovery voltage of the main circuit breaker to exceed the standard envelope, making the breaking conditions of the main circuit breaker very severe. This paper establishes a mathematical model and finds that the oscillating circuit formed by the current-limiting inductance and the stray capacitance of the system is the reason why the overvoltage rise rate of the main circuit breaker is greatly increased. Establish a single-phase equivalent circuit model for simulation. It is found that the rate of rise of the transient recovery voltage decreases with the increase of the stray capacitance of the system. When the stray capacitance takes the estimated value of the system parameters, the transient recovery voltage rise rate reaches its peak when the current-limiting inductance is 9mH.

Environmental friendly fluorinated compounds and their mixtures with CO2 are considered as a typical category of SF6-substitute gases, due to their low global warming potentials and good dielectric strength. This paper investigates the breakdown characteristics and physical and chemical properties of HFC-134a (1,1,1,2,3,3,3-Heptafluor-opropane) and its mixtures theoretically and experimentally, which are compared with those of SF6. The results show that the HFC-134a mixtures with CO2 are suitable for the power application with pressure 0.1 MPa, such as medium voltage power equipment. Espeically for the power application with 0.3MPa, the HFC-134a/CO2 mixtures with the same pressure or a higher pressure could have equivalent insulation as SF6, whose GWPs are almost one twenty-third that of SF6. For the power application with 0.5 MPa, the HFC-134a/CO2 mixtures with the liquefaction temperature -15°C and pressure 0.6 MPa could have equivalent insulation as SF6.

高耦合分裂电抗限流器(HCSR)具有良好的经济性和广泛的适用性,在高压、超高压领域受到关注.然而,电力系统在引入高耦合分裂电抗限流器后,其工作过程会涉及电流的转移和主断路器回路短时串入限流器电感等过程,可能引起较高的过电压.基于此,文中针对500 kV系统在90 kA短路电流等级的典型场景下,计算分析不同短路故障工况下引入限流器的过电压情况,对比分析不同过电压保护方案对主断路器及真空快速断路器TRV幅值与陡度的抑制效果.结果 表明,在限流器模块两端并联电容的基础上对两串联真空快速断路器并联电容或阻容,均可进一步明显降低主断路器的TRV陡度和幅值,且并联电容对陡度抑制效果更有效,而并联阻容对抑制幅值效果更显著,研究结果可为限流器的过电压保护方案及参数选取提供依据.

通过激光阴影技术、激光补光高速摄影技术、栅片电压测量技术等数字化精密测量技术定量分析小型断路器短路分断过程中的起弧、弧根转移及栅片灭弧的电弧行为及参数.通过分析电弧运动行为,诊断出影响电弧运动的因素,进而提供精准的优化方案.以某型号YL小型断路器为例,先进行数字化精密测量电弧运动各项参数,然后通过诊断和分析,给出优化引弧角、扩大引弧板两种优化方案,通过改进优化使得小型断路器分断能力获得快速提升.结果 表明,数字化精密测量技术能为小型断路器分断性能提升给出快速的精密诊断,具有重大的工程实践意义.

六氟化硫(SF6)具有优良的绝缘与灭弧性能,广泛应用于电力设备中,但该气体是一种强温室气体,已被要求限制甚至停止使用.二氧化碳(CO2)灭弧性能优良,作为一种潜在的灭弧介质替代SF6气体具有较大应用前景.针对CO2气体在高压气体断路器中的应用,围绕弧后击穿特性这一核心问题,通过磁流体动力学电弧仿真和弧后热击穿与电击穿特性评估,分析了CO2气体电弧的弧后介质恢复特性,及其与SF6气体的差距,进而探讨了采用CO2气体作为高压气体断路器灭弧介质的可行性与关键问题.结果 表明:文中断路器结构下CO2电弧热开断能力约为SF6气体的28.7％;弧后CO2气体介质恢复速度较SF6气体慢,动触头前端的区域电击穿发生概率较高;提高CO2气体充气压力可有效提高电弧热开断能力和弧后介质恢复强度;该研究可为环保型高压气体断路器的研制与优化提供参考.