Analysis of Fault Arc in High-Speed Switch Applied in Hybrid Circuit Breaker

The behavior of fault arc in a high-speed switch （HSS） has been studied theoretically and experimentally. A simplified HSS setup is designed to support this work. A two-dimensional arc model is developed to analyze the characteristics of fault arc based on magnetic-hydrodynamic （MHD） theory. The advantage of such a model is that the thermal transfer coefficient can be determined by depending on the numerical method alone. The influence of net emission coefficients （NEC） radiation model and P1 model on fault arc is analyzed in detail. Results show that NEC model predicts more radiation energy and less pressure rise without the re-absorption effect considered. As a consequence, P1 model is more suitable to calculate the pressure rise caused by fault arc. Finally, the pressure rise during longer arcing time for different arc currents is predicted.

Simulation of Fault Arc Based on Different Radiation Models in a Closed Tank

This paper focuses on the simulation of a fault arc in a closed tank based on the magneto-hydrodynamic （MHD） method, in which a comparative study of three radiation models, including net emission coefficients （NEC）, semi-empirical model based on NEC as well as the P1 model, is developed. The pressure rise calculated by the three radiation models are compared to the measured results. Particularly when the senti-empirical model is used, the effect of different boundary temperatures of the re-absorption layer in the semi-empirical model on pressure rise is concentrated on. The results show that the re-absorption effect in the low-temperature region affects radiation transfer of fault arcs evidently, and thus the internal pressure rise. Compared with the NEC model, P1 and the semi-empirical model with 0.7 〈 α 〈 0.83 are more suitable to calculate the pressure rise of the fault arc, where is an adjusted parameter involving the boundary temperature of the re-absorption region in the semi-empirical model.

Arc Grounding Fault Identification Using Integrated Characteristics in the Power Grid

Arc grounding faults occur frequently in the power grid with small resistance grounding neutral points.The existing arc fault identification technology only uses the fault line signal characteristics to set the identification index,which leads to detection failure when the arc zero-off characteristic is short.To solve this problem,this paper presents an arc fault identification method by utilizing integrated signal characteristics of both the fault line and sound lines.Firstly,the waveform characteristics of the fault line and sound lines under an arc grounding fault are studied.After that,the convex hull,gradient product,and correlation coefficient index are used as the basic characteristic parameters to establish fault identification criteria.Then,the logistic regression algorithm is employed to deal with the reference samples,establish the machine discrimination model,and realize the discrimination of fault types.Finally,simulation test results and experimental results verify the accuracy of the proposed method.The comparison analysis shows that the proposed method has higher recognition accuracy,especially when the arc dissipation power is smaller than 2×10^(3) W,the zero-off period is not obvious.In conclusion,the proposed method expands the arc fault identification theory.

Research on Low Voltage Series Arc Fault Prediction Method Based on Multidimensional Time-Frequency Domain Characteristics

The load types in low-voltage distribution systems are diverse.Some loads have current signals that are similar to series fault arcs,making it difficult to effectively detect fault arcs during their occurrence and sustained combustion,which can easily lead to serious electrical fire accidents.To address this issue,this paper establishes a fault arc prototype experimental platform,selects multiple commonly used loads for fault arc experiments,and collects data in both normal and fault states.By analyzing waveform characteristics and selecting fault discrimination feature indicators,corresponding feature values are extracted for qualitative analysis to explore changes in timefrequency characteristics of current before and after faults.Multiple features are then selected to form a multidimensional feature vector space to effectively reduce arc misjudgments and construct a fault discrimination feature database.Based on this,a fault arc hazard prediction model is built using random forests.The model’s multiple hyperparameters are simultaneously optimized through grid search,aiming tominimize node information entropy and complete model training,thereby enhancing model robustness and generalization ability.Through experimental verification,the proposed method accurately predicts and classifies fault arcs of different load types,with an average accuracy at least 1%higher than that of the commonly used fault predictionmethods compared in the paper.

Study on the Detection Methods of DC Series Arc Fault

It is difficult to detect and extinguish direct current(DC)arc in power electronics systems,and the arc could easily lead to a fire and cause great damage to surrounding equipment.A DC arc generation simulation unit is established,in which DC series arcs are generated by dragging the moving electrode away from the fixed one with the help of the stepper motor.In addition,a ferrite rod antenna is used to receive the electromagnetic radiation signals induced by the arcs.Based on experiments using the unit,the general characteristics of DC arc,including the pulse characteristics of arc current and source output in corresponding time window,and the frequency-domain characteristics of arc current,are studied.With discussion on three detection methods,it is concluded that the variation of current and voltage of arc,the spectrum of the arc current during the discontinuous intervals and the radiating electromagnetic signal are all features that can be adopted for detecting DC series arc.Therefore,a synthetic judgment method is suggested for further study.

Control method based on DRFNN sliding mode for multifunctional flexible multistate switch

To address the low accuracy and stability when applying classical control theory in distribution networks with distributed generation,a control method involving flexible multistate switches(FMSs)is proposed in this study.This approach is based on an improved double-loop recursive fuzzy neural network(DRFNN)sliding mode,which is intended to stably achieve multiterminal power interaction and adaptive arc suppression for single-phase ground faults.First,an improved DRFNN sliding mode control(SMC)method is proposed to overcome the chattering and transient overshoot inherent in the classical SMC and reduce the reliance on a precise mathematical model of the control system.To improve the robustness of the system,an adaptive parameter-adjustment strategy for the DRFNN is designed,where its dynamic mapping capabilities are leveraged to improve the transient compensation control.Additionally,a quasi-continuous second-order sliding mode controller with a calculus-driven sliding mode surface is developed to improve the current monitoring accuracy and enhance the system stability.The stability of the proposed method and the convergence of the network parameters are verified using the Lyapunov theorem.A simulation model of the three-port FMS with its control system is constructed in MATLAB/Simulink.The simulation result confirms the feasibility and effectiveness of the proposed control strategy based on a comparative analysis.

基于可控串联电抗的新能源送出线路故障性质识别方法

针对不带并联电抗器的新能源送出线,能否正确区分故障性质是确保新能源系统安全可靠运行的重中之重。为降低新能源送出线故障对电力设备和系统的冲击,提高重合闸成功率,减少新能源发电系统脱网的发生,提出基于可控串联电抗限流信息的新能源送出线路故障性质识别方法。在送出线路上设计串联可控限流电抗器,在线路故障相跳闸时基于等值阻抗的预判投入可控电抗,在串联电抗限流特性下分析瞬时性故障和永久性故障时的线路电流差异,建立电流突变量比较判据,并能够确定故障电弧熄灭时刻,进而构建新能源场站送出线路基于故障性质识别的自适应重合闸策略。仿真结果表明,所提方法能快速、可靠地识别结果,应用在新能源场站送出线路工程上能有效提高线路的重合率。

基于发电机定子接地燃弧电阻变化特征的主动降压熄弧机理与抑制方法

定子接地燃弧故障是制约大型发电机安全可靠运行的技术瓶颈。为解决定子绕组接地电弧引发的绝缘击穿与铁心损伤问题,该文提出基于发电机定子接地燃弧电阻变化特征的柔性接地主动降压熄弧方法。该文建立了定子接地燃弧电阻与弧道电压的解析关系,揭示了定子接地故障点电压降低过程中,接地燃弧电阻增大的变化规律;明确了将接地故障点电压抑制在电弧重燃电压以下时,可实现定子绕组击穿间隙回归正常绝缘状态的抑制方法。根据大型发电机定子柔性接地方式降压熄弧策略,在接地故障燃弧阶段,中性点电压源输出故障点工频与3次谐波反向电势,主动降低故障点电压,将故障点电压降低至燃弧电压以下,使定子接地电弧熄灭。利用注入信号感知定子接地燃弧状态,实时监测定子阻尼率矢量角变化趋势,判定降压消弧过程中接地燃弧故障是否消除。所提方法无需将故障点电压降低至零,可有效降低非故障相电压抬升,降低定子非故障相绕组槽内击穿风险。在PSCAD/EMTDC仿真环境模拟定子接地故障燃弧工况,对所提方法进行试验验证。试验结果表明,基于定子接地燃弧电阻变化特征的柔性接地消弧方法可主动降低定子接地故障点电压,助增故障点间隙绝缘电阻回归,抑制接地电弧重燃,降低定子非故障相绝缘击穿风险,实现定子接地燃弧故障的可靠消弧。

基于改进EfficientNetV2算法的三相串联故障电弧检测

串联电弧故障在电气火灾中的成因中占据着重要的比例,在具有变频器的三相电机线路中,由于多种电弧电流复杂行为以及多种负载的存在,很难准确识别变频器后部线路中发生的串联电弧故障。为了解决这一问题,提出了一种改进的EfficientNetV2算法。搭建低电压三相电弧故障数据采集平台,采集了所需要的正常状态和故障状态的电流信号。为了充分利用机器视觉的优势,采用马尔可夫变迁场(MTF)将采集到的时域电流信号编码为图像。将MTF图像送入模型中进行训练和测试,该模型具有轻量级高效的通道注意力和双池化空间注意力,更加专注于电弧特征,提高网络性能。实验结果表明,该方法的准确率可达98.99%。

风电场集电线路单相接地故障柔性电压消弧与动态保护新原理

为解决风电场集电系统长期存在的线路单相接地故障处理难题,提出风电场中性点柔性接地方式,理论推导获得柔性接地风电场三序等效电路,与集电线路单相接地故障复合序网模型,建立风电场零序电压柔性调控理论,提出集电线路单相接地故障柔性电压消弧方法,可主动将故障点电压及故障残流迅速抑制到零,实现单相接地故障电弧的可靠消除。进一步提出基于零序电流动态增量的高阻故障辨识与保护方法,通过柔性调控零序补偿电流,逐渐放大故障残流,实现永久性单相接地故障的灵敏感知与保护。利用PSCAD/EMTDC搭建含双馈异步风电机(doubly-fedinduction generator,DFIG)的规模化风电场模型,模拟多种运行与故障条件对所提方法进行验证,仿真结果表明,该方法能够在考虑系统不平衡电压的情况下精准调控集电线路故障点电压,实现瞬时性单相接地故障快速可靠消弧,动态感知并持续抑制永久性单相接地故障残流,实现集电线路高阻接地故障的灵敏保护。该技术有望提升风电场运行灵活性与故障防御能力,有力保障新能源电能外送的持续性、可靠性。

基于1DCNN和D-S多信息融合的光伏系统直流母线串联电弧故障检测

直流母线是光伏系统输出能源的主干道,由于长期曝晒、风化等作用,电缆、连接器等组件劣化,光伏系统直流母线中发生电弧的可能性急剧上升,极易引发火灾、触电等事故。在光伏系统中,串联电弧故障将使回路电流下降,传统的过流保护无法识别。因此,本文提出基于深度学习和证据理论(D-S)的方法来识别串联电弧故障,该方法基于并联电容器电流和电压信号,采用一维卷积神经网络(1DCNN)对检测数据进行电弧识别;在此基础上将基于单个传感数据的识别结果作为证据,运用D-S多信息合成法则计算得到信度分配,最后利用决策规则判断是否发生串联电弧故障。搭建多参数可调模型获取数据进行测试,结果表明:使用1DCNN识别方法,基于并联电容器电流和电压信号的串联电弧识别准确率分别为97.19%和94.98%,而基于1DCNN和D-S多信息融合的光伏系统直流串联电弧故障检测的识别准确率可提升至99%以上。

典型神经网络串联型电弧故障检测及选线方法研究

为解决传统特征分析加机器学习的电弧故障检测方法的准确率和实时性受到特征参数选取的主观性及特征分析过程的影响问题,搭建了三相多负载并联的串联型电弧故障实验系统,对不同支路、不同相发生电弧故障时的干路电流信号时间序列进行分析。将电流信号进行分类、分段、标准化处理并作为检测模型样本;对深度卷积神经网络模型、长短期记忆网络模型、普通神经网络模型进行架构及训练;通过差分处理对网络模型在线分类结果进行优化分析;以准确度和损失函数值、在线测试速度、优化后多分类识别准确率为评价指标,对比分析了3种模型故障检测及选线效果。研究结果表明:基于深度卷积神经网络的串联型电弧故障检测及选线模型对电机类负载故障检测及选线准确率可达96.77%,对变频器类负载故障检测及选线准确率可到98%,准确率高于近几年其他三相回路电弧故障检测模型。

电动汽车驱动电机与负载模拟系统建模及电弧故障仿真研究

电弧故障是引发电动汽车电气火灾的重要原因之一。电动汽车行驶工况复杂,电机及其驱动系统电压高、电流大,且其电弧故障随机性强、隐蔽性高导致真车故障实验难以开展,因此提出一种借助小功率电机与负载系统模拟故障的方法,以便快速开展大量实验,研究电弧故障特性。首先,在电动汽车负载转矩计算和等效缩放的基础上搭建了模拟实验平台,采集三相永磁同步电机线路串联电弧故障电流。其次,运用MATLAB软件构建空间矢量脉宽调制控制的电动汽车驱动电机与负载模拟系统,引入Cassie电弧故障模型并进行改进,对电动汽车三相永磁同步电机线路串联电弧故障展开仿真分析。最后,采用基于平肩宽度占比和小波包分解能量占比的特征提取方法,将仿真数据与实测数据进行比较并定量评价。结果表明,所提出的高斯电弧故障复合模型的相对平均误差最小,仅为7.6%。所构建的仿真系统可有效模拟实际线路的电弧故障,对电动汽车电气火灾的防控具有重要意义。

基于零序特征参量差异的谐振接地系统熄弧时刻判别方法

在谐振接地系统中,熄弧时刻的准确判别对于接地故障的选线、消弧效果评估以及熄弧后暂态信息提取和利用具有指导意义。该文针对谐振接地系统单相接地故障,分析了故障点工频电流熄弧前后的故障电气特征,并利用母线零序电压和消弧线圈电流,通过求取其信号的一阶导数及其平方信号的二阶导数,构造两种特征参量。研究发现该特征参量在熄弧前为常数,熄弧后发生明显变化,据此可形成相应的熄弧时刻判别的基本判据。考虑到系统实际运行中各种因素的影响,在此基础上形成熄弧时刻判别的附加判据,从而最终提出基于零序特征参量差异的谐振接地系统熄弧时刻判别方法。通过Mat Lab/Simulink仿真和现场实测,验证了该熄弧时刻判别方法的有效性和准确性。

低压直流电弧检测与分断技术研究综述

低压直流电弧精准检测与快速分断技术是保障低压直流配用电系统安全、稳定、可靠运行的关键。首先综述轨道交通新场景下发展的直流故障电弧检测技术,分析多类基于时频域特征构建算法和基于机器学习的智能模型设计方式,介绍采用电磁、电流信号等特征的多种直流故障电弧定位技术。其次综述面向低压直流系统的快速分断技术,概述传统机械断路器开断方法,介绍基于电力电子器件发展的混合式断路器和固态断路器研究进展。最终提出低压电器设计领域发展方向,以期为相关研究人员提供参考。

低压直流串联故障电弧研究综述

直流串联故障电弧发生时隐蔽性较高、检测难度大,是低压直流系统发生火灾的重要原因。主要从低压直流串联故障电弧模型、电弧特征及其检测方法、检测标准及装置等3个方面进行综述。基于电弧仿真模型了解电弧演化过程,分析常用故障电弧模型的原理、适用范围和局限性;基于故障电弧的物理、电气信号特征构建及检测方法,阐述不同特征检测方法的优缺点;最后对直流串联故障电弧相关标准进行解读,并讨论试验平台及产品的研究现状和不足,展望未来低压直流故障电弧相关领域的研究方向。

基于多卷积和结构搜索的电弧故障检测模型

串联型电弧故障主要由电路中电气接触点接触不良引起,是引发电动汽车电气火灾的主要原因之一,直接威胁着车内人员的生命安全。为对其进行研究,论文搭建了电动汽车直流串联型电弧故障实验平台,获取了系统处于不同工作状态下的电源端电压信号,分析了电弧故障对电源端电压的影响。在构建检测模型时,论文使用了卷积神经网络,引入轻量型的卷积操作并考虑了其在实际应用中的局限性。将常规卷积和轻量化卷积操作结合,构建了电弧故障检测的初步模型。接着以网络的规模和准确率为评估指标,通过具有精英保留策略的遗传算法对模型的外部结构和内部参数进行搜索。最终建立了适合电动汽车的电弧故障检测(arc fault detection,AFD)的检测模型AFDNet。该模型的检测准确率达到93.73%,在嵌入式设备Jetson Nano(JN)中的运行时间为10.82 ms。模型建立后,论文在网络的规模、准确性及实时性方面,将搜索算法的搜索结果与其他的网络结构进行比较,验证了搜索算法所得结果的合理性。并通过与其他检测方法对比,证明了电动汽车电弧故障检测模型AFDNet性能的优越。

基于时频域参数分析的直流电弧故障检测装置

针对现有的直流电弧故障检测方法容易受到环境影响,无法实现精准检测,且算法结构复杂,难以部署于实际应用,基于短时傅里叶变换设计一种直流电弧故障检测算法。通过分析电弧电流信号在频域上的幅值变化趋势,将频谱划分为不同的阶段,计算不同阶段频率积分的比值,根据大量实验结果得到判定的阈值从而实现电弧故障检测。最后,选择合适的单片机实现检测算法并进行测试,验证所提算法的有效性。结果表明,所提算法不仅能在多种类型负载并联的情况下及时检测出电弧故障,检测的准确率可达94.4%,而且在面对正常状态电流时的误判率为0,证明其具有较好的稳健性。

基于电源端电压的电动汽车电弧故障检测

当电动汽车主电路的电气接触点接触不良时,极易产生电弧故障,直接威胁车内人员的生命安全。论文提出了一种基于自定义差分阈值滤波-分段最大值标准化-统计数值规律的电弧故障检测方法。首先围绕真实电动汽车吉利帝豪EV450搭建了电动汽车电弧故障实验平台,开展了电动汽车在不同工作模式下的电弧故障实验。然后以电源端电压为对象,对信号进行小波分解,对小波分解得到的低频系数进行自定义差分阈值滤波和分段最大值标准化。最后统计标准化后数据相同值个数,并通过阈值法检测串联型电弧故障。论文对模型样本长度、差分阈值比例、最大值标准化分段数、预处理方法选择等进行了深入分析,并对参数进行了优化设置,进一步提升模型性能。结果表明,所建模型对电动汽车电弧故障检测准确率为98.35%,且实时性较好。通过对模型进行泛化性分析、算法时间复杂度分析及与其他电弧故障检测模型对比分析,证明论文所建模型对电动汽车电弧故障检测具有较好的适用性。

基于全息希尔伯特频谱分析的交流串联电弧故障电流特征研究

针对交流串联电弧故障信号特征提取和分析的难题,提出采用循环掩码经验模态分解(itEMD)进行信号分解以获取多层内涵模态分量(IMF),结合全息希尔伯特变换(HHT)展现高维幅度调制频率调制(AM-FM)谱图的分析方法,从而获取高区分度的电弧故障信号特征谱图。采用基于GB/T 31143—2014的电弧故障实验平台,采集7种屏蔽负载模式下电弧故障电流信号,分析研究不同环境下电弧故障特征。所提方法对于电弧故障检测技术的开发具有一定的理论指导意义。