Single Phase-to-Ground Fault Line Identification and Section Location Method for Non-Effectively Grounded Distribution Systems Based on Signal Injection

A diagnostic signal current trace detecting based single phase-to-ground fault line identifica- tion and section location method for non-effectively grounded distribution systems is presented in this paper.A special diagnostic signal current is injected into the fault distribution system,and then it is detected at the outlet terminals to identify the fault line and at the sectionalizing or branching point along the fault line to locate the fault section.The method has been put into application in actual distribution network and field experience shows that it can identify the fault line and locate the fault section correctly and effectively.

Fault Line Selection Method Considering Grounding Fault Angle for Distribution Network

In the distribution network system with its neutral point grounding via arc suppression coil, when single-phase grounding fault occurred near zero-crossing point of the phase voltage, the inaccuracy of the line selection always existed in existing methods. According to the characteristics that transient current was different between the fault feeder and other faultless feeders, wavelet transformation was performed on data of the transient current within a power frequency cycle after the fault occurred. Based on different fault angles, wavelet energy in corresponding frequency band was chosen to compare. The result was that wavelet energy in fault feeder was the largest of all, and it was larger than sum of those in other faultless feeders, when the bus broke down, the disparity between each wavelet energy was not significant. Fault line could be selected out by the criterion above. The results of MATLAB/simulink simulation experiment indicated that this method had anti-interference capacity and was feasible.

Faulty Feeder Identification in Resonant Grounding Distribution Networks Based on Deep Learning and Transfer Learning

Identification of faulty feeders in resonant grounding distribution networks remains a significant challenge dueto the weak fault current and complicated working conditions.In this paper, we present a deep learning-based multi-labelclassification framework to reliably distinguish the faulty feeder.Three different neural networks (NNs) including the multilayerperceptron, one-dimensional convolutional neural network (1DCNN), and 2D CNN are built. However, the labeled data maybe difficult to obtain in the actual environment. We use thesimplified simulation model based on a full-scale test field (FSTF)to obtain sufficient labeled source data. Being different frommost learning-based methods, assuming that the distribution ofsource domain and target domain is identical, we propose asamples-based transfer learning method to improve the domainadaptation by using samples in the source domain with properweights. The TrAdaBoost algorithm is adopted to update theweights of each sample. The recorded data obtained in the FSTFare utilized to test the domain adaptability. According to ourvalidation and testing, the validation accuracies are high whenthere is sufficient labeled data for training the proposed NNs.The proposed 2D CNN has the best domain adaptability. TheTrAdaBoost algorithm can help the NNs to train an efficientclassifier that has better domain adaptation. It has been thereforeconcluded that the proposed method, especially the 2D CNN, issuitable for actual distribution networks.

Protection of Zero-Sequence Power Variation in Mountain Wind Farm Collector Lines Based on Multi-Mode Grounding

The arc-suppression coil(ASC)in parallel low resistance(LR)multi-mode grounding is adopted in the mountain wind farm to cope with the phenomenon that is misoperation or refusal of zero-sequence protection in LR grounding wind farm.If the fault disappears before LR is put into the system,it is judged as an instantaneous fault;while the fault does not disappear after LR is put into the system,it is judged as a permanent fault;the single-phase grounding fault(SLG)protection criterion based on zerosequence power variation is proposed to identify the instantaneous-permanent fault.Firstly,the distribution characteristic of zero-sequence voltage(ZSV)and zero-sequence current(ZSC)are analyzed after SLGfault occurs in multi-mode grounding.Then,according to the characteristics that zero-sequence power variation of non-fault collector line is small,while the zero-sequence power variation of fault collector line can reflect the active power component of fault resistance,the protection criterion based on zero-sequence power variation is constructed.The theoretical analysis and simulation results show that the protection criterion can distinguish the property of fault only by using the single terminal information,which has high reliability.

基于有源消弧暂态衰减信息的配电网接地故障跟踪检测方法

现有配电网单相接地故障检测技术缺乏消弧过程的故障程度跟踪与选线能力。该文充分利用消弧过程中的暂态信息,建立考虑线路参数不对称的集总参数等效模型,论证消弧过程零序电压衰减直流特征的物理意义,提出考虑快速消弧的配电网故障检测新方法,对于低、高阻接地故障均有较高的检测灵敏度。实际应用中,仅需观测消弧全补偿电流投切过程中零序电压及零序电流波形,即可实现过渡电阻测量、故障程度跟踪与故障选线,具有瞬时性故障实时跟踪、故障检测与故障消弧同步进行的优势,操作简单、易于实现。仿真实验证实了方法的有效性,为配电网故障检测提供了新的理论依据。

基于单端分支系数的四端柔直环网线路保护方案

为提升直流线路保护在柔性直流环网中的速动性与可靠性,提出一种基于单端分支系数的保护方案。分析了不同故障位置下各换流阀提供的故障电流分量,并根据单端换流阀极线电流突变量构造分支系数,结果表明线路保护区内故障下的单端分支系数小于区外故障,利用该特征构造区内、外故障识别元件。在线路高阻接地致使主保护失效时,根据桥臂最大允许电流决定线路纵联后备保护的开放时限,以优化现有主、后备保护方案之间的时序配合方案,保障各种直流线路故障工况下,换流阀不间断运行。保护方案不依赖线路边界元件,算法简单,大量仿真结果表明,该方案有良好的耐受过渡电阻能力,能在换流阀闭锁前实现故障区域可靠识别。

Faulty Feeder Identification and Fault Area Localization in Resonant Grounding System Based on Wavelet Packet and Bayesian Classifier

Accurate fault area localization is a challenging problem in resonant grounding systems(RGSs).Accordingly,this paper proposes a novel two-stage localization method for single-phase earth faults in RGSs.Firstly,a faulty feeder identification algorithm based on a Bayesian classifier is proposed.Three characteristic parameters of the RGS(the energy ratio,impedance factor,and energy spectrum entropy)are calculated based on the zero-sequence current(ZSC)of each feeder using wavelet packet transformations.Then,the values of three parameters are sent to a pre-trained Bayesian classifier to recognize the exact fault mode.With this result,the faulty feeder can be finally identified.To find the exact fault area on the faulty feeder,a localization method based on the similarity comparison of dominant frequency-band waveforms is proposed in an RGS equipped with feeder terminal units(FTUs).The FTUs can provide the information on the ZSC at their locations.Through wavelet-packet transformation,ZSC dominant frequency-band waveforms can be obtained at all FTU points.Similarities of the waveforms of characteristics at all FTU points are calculated and compared.The neighboring FTU points with the maximum diversity are the faulty sections finally determined.The proposed method exhibits higher accuracy in both faulty feeder identification and fault area localization compared to the previous methods.Finally,the effectiveness of the proposed method is validated by comparing simulation and experimental results.

Fault protection method of single-phase break for distribution network considering the influence of neutral grounding modes

With the rapid development of modern distribution network and the access of distributed generation,the network structure is becoming increasingly complex.Frequent single-phase break faults have seriously affected equipment and personal safety and stable operation of the power system.However,with the development and application of the composite neutral grounding modes,the protection of single-phase break fault is facing new challenges.This paper proposes a protection method of single-phase break fault for distribution network considering the influence of neutral grounding modes.The characteristics of neutral voltage and sequence current are analyzed under normal operation and single-phase break fault with different grounding modes.Following this,the protection criterion based on neutral voltage and sequence current variation is constructed.The protection method of singlephase break fault for distribution network is proposed,which is applicable for various neutral grounding modes.Theoretical analysis and simulation results show that the protection method is less affected by system asymmetry,fault location and load distribution.The method has higher sensitivity,reliability and adaptability.

Multiple Random Forests Based Intelligent Location of Single-phase Grounding Fault in Power Lines of DFIG-based Wind Farm

To address the problems of wind power abandonment and the stoppage of electricity transmission caused by a short circuit in a power line of a doubly-fed induction generator(DFIG) based wind farm, this paper proposes an intelligent location method for a single-phase grounding fault based on a multiple random forests(multi-RF) algorithm. First, the simulation model is built, and the fundamental amplitudes of the zerosequence currents are extracted by a fast Fourier transform(FFT) to construct the feature set. Then, the random forest classification algorithm is applied to establish the fault section locator. The model is resampled on the basis of the bootstrap method to generate multiple sample subsets, which are used to establish multiple classification and regression tree(CART) classifiers. The CART classifiers use the mean decrease in the node impurity as the feature importance,which is used to mine the relationship between features and fault sections. Subsequently, a fault section is identified by voting on the test results for each classifier. Finally, a multi-RF regression fault locator is built to output the predicted fault distance. Experimental results with PSCAD/EMTDC software show that the proposed method can overcome the shortcomings of a single RF and has the advantage of locating a short hybrid overhead/cable line with multiple branches. Compared with support vector machines(SVMs)and previously reported methods, the proposed method can meet the location accuracy and efficiency requirements of a DFIG-based wind farm better.

基于车体接地电流的故障列车精准识别方法

针对专用回流轨直流牵引供电系统发生正极与车体短路故障时,故障列车不能被精准识别的问题,首先给出了双边供电牵引系统等值模型,其次分析了故障列车和正常列车的车体接地电流特征差异,最后提出了一种基于车体接地电流的故障列车精准识别方法。所提方法采用列车的车体接地电流幅值和方向作为故障识别判据,当其幅值小于整定值时判断列车正常,当大于等于整定值时继而采用其方向进行判断,电流为正方向的列车被判为故障车,电流为反方向的列车被判为正常车。该方法原理简单,实现方便,实用性强,采用双重判据能有效避免误判,可靠性高。通过Matlab仿真和现场试验录波验证了方法的可行性和有效性。

基于前后逐段逼近的含多分支配电网单相接地故障测距方法

随着配电网的发展,T接分支的大量接入使得配电网结构复杂,传统测距算法通常忽略分支,测距精度变低,研究含多分支配电网单相接地故障测距具有重要意义。为此,提出一种基于前后逐段逼近的含多分支配电网单相接地故障精确测距方法。首先,分析分布参数模型,根据零序电压、电流关系,提出故障区段判别系数,判别最小故障区段。其次,利用最小故障区段两端的零序电压、电流建立故障测距函数,采用梯度下降法求解精确故障点。最后,在PSCAD仿真平台上对所述方法进行验证。结果表明,所提方法可确定故障最小区段,精确计算出故障点,并具有较强的耐受过渡电阻能力,能较好适应高渗透率分布式电源的接入。

基于ICEEMDAN的近断层地震动识别方法改进

近断层地震动严重威胁重要结构的安全,然而地震动波形复杂导致近断层地震动识别困难,经典Baker识别方法因采用小波技术,而存在母小波与潜在脉冲差异过大,致使识别产生误差。针对该问题,利用具有自适应噪声的完全集成经验模式分解(ICEEMDAN)对Baker方法进行改进,改进方法利用白噪声对信号分解过程进行辅助,从而增强了信号分析方法对地震动这类复杂信号的应对能力。为提高改进方法的识别效率和质量,对太平洋工程抗震中心数据库中的3 655条地震动进行识别。结果表明,由于ICEEMDAN技术的加入改进后的地震动识别方法共识别出192条近断层地震动,比经典Baker方法则识别出的163条近断层地震动多识别出17.79%的近断层地震动。为进一步探讨识别出的近断层地震动的近断层特性,从地震反应谱和地震损伤入手进行分析,两种方式获得近断层地震动反应谱长周期反应剧烈、峰值周期高,有明显的近断层特征;在地震损伤方面,以一典型钢管混凝土拱桥为案例,对两种方式识别出的近断层地震动及远场地震动进行地震响应计算。结果表明,两种方法获得的近断层地震动有着极为相似的地震损伤规律,且均较远场地震动的损伤有明显的增加。综上,两种方法识别出的近断层地震动均有明显的近断层特性,而基于ICEEMDAN技术的改进方法增加了17.79%的近断层地震动数量,将ICEEMDAN技术引入可以利用该技术较强的鲁棒性,从而对波形较为复杂的速度脉冲进行识别,进而在不降低近断层地震动识别质量的前提下,提升Baker识别方法的识别效率和准确性。

电压型配电网接地故障自动隔离方法研究

当发生接地故障时,必须及时发出跳闸命令、断开路由器,以保证配电网安全运行。提出了一种电压型配电网接地故障自动隔离方法。首先,通过小波分析和公共空间模式算法分解零序电压,提取时-频-空域特征,以更好地刻画故障的本质特征、提高故障自动隔离的准确性。然后,构建电压型配电网接地故障特征向量,采用多域特征和相关向量机(RVM)展开特征选择,确定接地故障发生区段。最后,通过零序电压判断是否启动保护。如需保护,则采用分布式处理方式完成故障自动隔离。试验结果表明,采用该方法得到的故障定位距离和实际故障距离更接近,说明该方法定位误差较小。根据故障定位结果可以准确判别故障区段,实现接地故障自动隔离,并且隔离过程中不需要借助额外设备。该方法的接地故障隔离自动化水平较高,具有实际应用价值。

电力电缆接地故障特征及识别方法研究

针对当前电力系统中电缆故障频发对供电可靠性和经济效益造成的严重影响,文章通过深入分析电缆接地故障的电气特性,结合高精度的信号处理技术,提出了基于电缆零序电流和电压波形分析的故障识别方法,能够有效区分电缆的持续性接地故障和间歇性接地故障,提高电力系统的故障检测效率和准确性,为电力电缆故障的快速诊断提供了理论依据和技术支持。

基于电压-电流分区的灵活接地系统故障保护

为进一步提升灵活接地(中性点经消弧线圈并联小电阻接地)系统接地保护方案的故障处理水平,保障供电系统安全稳定运行。本文通过分析灵活接地系统不同故障状态(并联小电阻投入前后)下健全区段和故障区段零序电压-电流分布特征,提出了基于电压-电流分区的灵活接地系统单相接地故障保护算法。当线路零序阻抗检测值低于定值时,保护动作,并考虑互感器测量精度的影响,设置最小动作电流及拐点电压,提高了该保护算法的灵敏度和可靠性。本方法原理简单,可有效识别系统所处状态并完成故障定位,不易受线路参数及过渡电阻影响,仿真分析和现场试验均验证了该方法的正确性和实用性。

含限流电抗器的环形直流微电网暂态对地高频电压保护方法

直流线路保护是直流微电网技术发展的关键,限流电抗器为直流故障保护算法的设计提供了便利。针对含限流电抗器的环形直流微电网,利用限流电抗器的故障暂态高频电压特性,提出一种基于限流电抗器两端对地高频电压的保护方法。该方法利用故障后线路两侧限流电抗器两端对地高频电压的差异性识别区内外故障,对于线路背侧阻抗呈容性将导致保护无法正确动作的情况,通过选择合适的频率能够很好地解决。仿真结果表明,该保护方法可快速、准确地判断故障线路和故障极,灵敏度与可靠性高,阈值易设定且无须相邻线路保护配合,对双端数据同步要求较低。该方法在不同的故障场景和运行方式下均能可靠进行故障识别和故障选极,并且耐受过渡电阻能力较强。

中低速磁浮交通接地系统研究综述

中低速磁浮交通系统单设回流轨结构虽然解决了常规走行轨道的杂散电流问题,但处于悬浮电位的接地系统使得其保护接地、防雷接地及电磁兼容等方面都不能简单沿用普通轮轨列车的接地方式,需要单独展开研究。首先介绍了中低速磁浮交通接地回流系统的特点;进一步从保护接地、防雷接地、以及接地系统的电磁兼容性三个方面对中低速磁浮交通接地系统进行分析,归纳总结其接地系统在接地故障点识别、雷击车体过电压、车体放电等方面存在的问题;最后探讨了针对以上问题的解决方案,包括64D接地保护装置改进及故障点识别优化、雷击车体过电压抑制方法以及电流泄放的电磁兼容性提升等,对中低速磁浮交通接地系统的设计方案提供全面参考。

近断层地震动的一种随机模型及其参数识别

近断层脉冲型地震动是一类特殊的破坏性地震动,但此类地震动记录数量匮乏,难以满足近场工程结构抗震分析需求。本文首先从PEER强震数据库中选取典型近断层脉冲型地震动记录,形成近断层脉冲型地震动数据库。然后,将基于小波包变换的随机地震动模型与速度脉冲模型相结合,建立了一种考虑竖向分量的脉冲型地震动随机模型。在此基础上,利用非线性规划方法进行了模型参数识别,并选取走滑断层和逆断层地震动记录各一条,将实际地震动与模拟结果进行对比,验证了模型有效性。结果表明:本文模型可对近断层区域的水平向及竖向地震动进行有效模拟,文中给出的模型参数识别方法可在时域上和频域上均获得与目标地震动相匹配的结果。

基于波形匹配电流源注入的网源直连系统接地故障有源消弧技术

海洋核动力平台中压系统采用网源直连拓扑结构,系统接地故障频发且常产生电弧。该文首先分析现有消弧方法的作用机理和消弧效果不佳的主要原因。从保障定子铁芯及系统安全出发,提出一种在发电机中性点注入波形匹配电流源的有源消弧技术方案。该方案通过主动降低故障相基波和3次谐波对地电压,将故障点电压抑制在电弧重燃电压以下,保证接地故障电弧可靠熄灭并避免重燃。最后,提出一种基于故障风险的接地故障辨识判据,能够与现有接地保护有效配合,在保证系统安全的前提下可靠辨识接地故障类型。PSCAD/EMTDC仿真结果证实了所提方法的有效性。

综合多域特征及融合算法的配电网单相接地故障类型识别

区分配电网中发生的单相接地故障类型,能够有针对性地制定故障检修策略,提升故障处置效率。配电自动化设备作为配电网故障快速辨识与处理的重要载体,对故障分类的原理及效果差异性较大,准确率无法满足电力系统工作需求,为此提出一种基于分类回归树与多核残差网络(classfication and regression tree and multi-core ResNet, CART-MRN)的树状结构故障类型识别方法。首先,建立树状故障分类结构,利用Fourier变换、经验模态分解(empirical mode decompsition, EMD)分解等方法提取故障点电压电流的多域故障特征;其次,结合特征分析与信息增益建立适应不同小电流接地系统的融合算法模型,并引入粒子群算法优化网络超参数;最后,通过现场录波数据验证与对比实验,证明该方法能快速、有效地完成单相接地故障分类识别,且更具有适应性。