Analysis and Research on 10kV Distribution Network Faults

The 10kV distribution network is an essential component of the power system,and its stable operation is crucial for ensuring reliable power supply.However,various factors can lead to faults in the distribution network.In order to enhance the safety and reliability of power distribution,this paper focuses on the analysis of faults in the 10kV distribution network caused by natural factors,operational factors,human factors,and equipment factors.It elucidates the various hazards resulting from distribution network faults and proposes corresponding preventive measures for different types of faults in the 10kV distribution network.The aim is to mitigate or reduce the impact of distribution network faults,ensuring the safe and stable operation of the distribution system.

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.

Online One-End Fault Location Algorithm for Parallel Transmission Lines

Fault location and distance protection are essential smart grid technologies ensuring reliability of the power system. This paper describes an accurate algorithm for locating faults on double-circuit transmission lines. The proposed approach is capable of identifying the faulted circuit of a parallel transmission line by checking the estimated fault location and fault resistance. Voltage and current measurements from only one of the terminals of the faulty line are used. No pre-fault data are required for the estimation. The lumped parameter line model considering shunt capacitance is utilized for the derivation of the algorithm. It’s assumed that line parameters are known and transmission lines are fully transposed. The method is applicable to all types of faults. It’s evinced by evaluation studies that the proposed algorithm can correctly determine the faulted circuit in most cases. For exceptional cases, the current waveforms during the fault can be used to help identify the faulted circuit. The proposed algorithm generates quite accurate fault location estimates, and may be suitable for distance relaying.

Fault Locator Using Two-end Unsynchronized Measurements for UHV Series Compensated Double-circuit Lines

An accurate fault location algorithm for double-circuit series compensated lines is presented.Use of two-end unsynchronized measurements of current and voltage signals is considered.The algorithm applies two subroutines,designated for locating faults on particular line sections,and additionally the procedure for selecting the valid subroutine.The subroutines are formulated with use of the generalized fault loop model and the distributed parameter line model is applied.Performed ATP-EMTP based evaluation has shown the validity of the derived fault location algorithm and its high accuracy.

Application of DC component to select fault branch in arc suppression coil grounding system

When single phase earth fault occurs in the arc suppression coil grounding system, the amplitude of the transient capacitance current is high and decays fast, but the attenuation of the transient inductance current is much slower. This paper analyses the DC component of fault branch, and has found it is much bigger than that of the normal branches in transient state. All the simulation results obtained from three compensation types, different fault time and different wave cycles show that the DC component of fault branch is much higher than that of those normal branches. These results verify the effectiveness of taking the DC component as the method of fault line selection in the arc suppression coil grounding system.

Application of Atomic Sparse Decomposition to Feature Extraction of the Fault Signal in Small Current Grounding System

Applying the atomic sparse decomposition in the distribution network with harmonics and small current grounding to decompose the transient zero sequence current that appears after the single phase to ground fault occurred. Based on dictionary of Gabor atoms and matching pursuit algorithm, the method extracts the atomic components iteratively from the feature signals and translated them to damped sinusoidal components. Then we can obtain the parametrical and analytical representation of atomic components. The termination condition of decomposing iteration is determined by the threshold of the initial residual energy with the purpose of extract the features more effectively. Accordingly, the proposed method can extract the starting and ending moment of disturbances precisely as well as their magnitudes, frequencies and other features. The numerical examples demonstrate its effectiveness.

A cumulative standard deviation sum based method for high resistance fault identification and classification in power transmission lines

High resistance fault poses an enormous challenge to the existing algorithms of fault detection and fault classification.In this paper,the standard deviation and accumulation method are employed to perform the fault detection and classification.It is primarily built in two stages.Firstly,the standard deviations for the measured current’s signals of the local and remote terminals is computed to extract the fault feature.Secondly,the cumulative approach is used to enlarge the fault feature to perform the high resistance fault.The proposed scheme is known as Standard Deviation Index(SDI),and it is obtained for the three phases and zero sequence.The proposed algorithm has been tested through different fault circumstances such as multiple faults locations,fault resistances,and fault inception time.Moreover,far-end faults with high-resistance,faults happened nearby the terminal,faults considering variable loading angle,sudden load change,different sampling frequency,bad signaling and a fault occurred in the presence of series compensation are also discussed.The results show that the proposed scheme performed remarkably well regarding the fault with resistance up to 1.5kΩand can be detected within a millisecond after the fault inception.Additionally,the computational simplicity that characterizes the processes makes it more efficient and suitable for domain applications.

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。

基于信号图像化和CNN-ResNet的配电网单相接地故障选线方法

配电网发生单相接地故障时,零序电流呈现较强的非线性与非平稳性,故障选线较为困难,针对此问题,提出一种基于信号图像化和卷积神经网络-残差网络的配电网单相接地故障选线方法。首先,利用排列熵优化变分模态分解算法的参数,将零序电流信号分解成一系列固有模态函数;其次,引入新的数据预处理方式,将固有模态函数转成二维图像,获得零序电流信号的时频特征图;最后,利用一维卷积神经网络提取零序电流信号的相关性和特征,利用残差网络提取时频特征图的特征,将两个网络融合,构建混合卷积神经网络结构,实现故障选线。仿真与实验结果表明,该方法能够在高阻接地、采样时间不同步、强噪声等情况下准确地选择出故障线路,可满足配电网对故障选线准确性和可靠性的需求。

YOLO网络配电网故障选线方法

针对现有的配电网故障选线困难的问题,提出一种基于YOLO网络的配电网故障选线方法。首先,使用三相电流构建极坐标二维图像,充分提取特征信息;之后,对各线路三相电流的极坐标图像进行像素级融合,并以不同颜色区分,在进一步加强图像特征的同时,降低原始图像的冗余度;最后,使用YOLO神经网络对融合图像进行特征提取,训练得到最优的模型文件,利用该模型实现故障选线。将该方法与现有的故障选线结果进行对比,结果表明,该方法选线准确率可以达到99.95%,选线时间12.9 ms,明显优于其他故障选线方案,且该方案不受故障时刻、故障类型和过渡电阻等因素的影响,可满足配电网故障选线的准确度和可靠性需求。

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。

基于IHHT‑RF的配电网单相接地故障选线方法

小电流系统发生单相接地故障时故障特征易受高接地过渡电阻、小初相角等弱故障条件影响而导致选线准确率低。为此,提出一种基于改进希尔伯特黄变换—随机森林(improved Hilbert⁃Huang transform⁃random forest,IHHT⁃RF)的配电网单相接地故障选线方法。首先,提取每条线路在故障发生时的电流暂态信号,通过IHHT提取纯净的暂态电气量,构造标准差、能量熵和幅值畸变度3类特征向量;然后,将特征向量输入RF分类器建立故障选线模型,把故障选线问题转化为二分类问题;最后,将测量数据输入RF分类器中得出分类结果,实现故障线路的自动识别。仿真结果表明,该选线方法综合利用暂态信号的幅值、频率和能量等特征信息,不受弱故障条件、馈线结构等因素的影响,能有效提高故障选线的准确率,具有较强的适应性和可靠性。

基于BA-MKELM的微电网故障识别与定位

提出一种基于贝叶斯算法优化多核极限学习机的微电网故障识别和定位方法。针对极限学习机输入参数和隐含层节点数随机选取导致回归能力不足的问题,引入核函数,将多项式与高斯径向基核函数加权组合构成多核极限学习机建立故障识别与定位模型,并采用贝叶斯算法对多核极限学习机相关参数进行优化,进一步提高模型的逼近能力。为了验证所提模型的故障识别与定位性能,选用极限学习机和多核极限学习机分别建立故障诊断模型进行比较分析。实验结果表明,所提方法能够高性能地识别和定位微电网中任何类型的故障,识别和定位精度更高。

10kV配电线路单相树线故障选相研究

针对配电网三相电压不平衡发生树-线故障难以准确判别故障相而引发山火的问题,提出了一种基于电压信号突变值和变化趋势相结合的选相方法。在传统三相电压平衡配电网单相接地故障选相理论基础上,搭建了10 kV单相树-线故障试验平台。首先,在中性点不接地、消弧线圈接地欠补偿和过补偿3种情况下进行了多种树木、不同残余电流单相树-线故障试验研究。然后,根据树-线故障时零序电压和各相电压幅值变化特征,定义了故障时刻及故障相识别码,建立了某一相电压有效值的幅值增加最大则其滞后相为故障相的判据。通过故障时刻前后0.1 s内的各相电压平均有效值改变大小对比及相应判据,将生成对应的识别码进行匹配,实现精确快速判别故障相。最后,通过试验验证该方法能够有效解决单相树-线故障相识别问题且不受电压不平衡影响,具有较好的工程应用价值。

混合数据驱动的轻量化YOLOv5故障选线方法

针对传统选线方法精度低、实时性差、易受噪声干扰的问题,提出一种混合数据驱动的轻量化YOLOv5选线方法,简记为MSE-YOLOv5。首先,以零序电流作为区分故障线路与非故障线路的判断依据,为了增强二者间数据对比差异性,利用小波变换将零序电流信号映射为二维时频图;其次,为了扩充样本数量,利用搭建的小电流接地系统仿真模型,通过改变故障点位置、初相位以及接地电阻等参数生成仿真数据,与真实数据构成混合数据集;然后,为了减少选线时背景噪声对微弱故障信号特征的影响,在所搭建检测网络的颈部网络中引入通道注意力模块,从而增强故障特征的表达能力;最后,为了提高选线实时性,在网络中引入轻量化网络以减少其参数量与运算量。为了验证所提出方法的优势,利用某变电站真实故障数据进行测试,并与4种经典算法进行比较。结果表明:所提混合数据驱动的轻量化YOLOv5故障选线方法具有较高精度,其选线精度可达95.2%,即使在噪声干扰条件下,选线精度依然可以保持在90%以上;具有更轻的体量及更快的选线速度,参数量下降至原网络的1/5,计算量下降至1/7,检测速度可达7.7 ms。因此,混合数据驱动的轻量化YOLOv5故障选线方法具有体量小、速度快、精度高的优点,有利于后期将其部署到现场设备中。

融合稳态和暂态特征量的接地故障选线方法研究

单相接地故障是配电网运行时发生概率最高的故障,但接地时存在电气故障特征弱、外界干扰大的情况,使得配电网存在接地选线困难的问题。对此,提出基于稳态和暂态故障特征量相融合的配电网接地选线方法,分析单相接地时接地故障线路与非故障线路对地电容电流突变量五次谐波分量在幅值和相位上的差异性,利用经验小波变换提取故障零序电流的低频分量综合相关系数和高频分量相对权重系数,并利用模糊神经网络实现融合特征量与故障的非线性映射诊断。建立配电网接地故障仿真模型,通过多重干扰下的选线对比分析,验证了本文方法的有效性和优越性。

基于变压器故障量的分布式小电流接地选线方法

提出并分析了一种基于变压器工频故障量的分布式选线方法和一种基于变压器暂态故障量的分布式选线方法。分析表明,工频故障量选线方法可用于中性点经消弧线圈接地系统欠补偿状态的一部分场景;暂态故障量选线方法比较的是变压器负荷侧暂态故障参考量与被保护线路暂态故障量,这两个暂态故障量具有近距离串行的电流关系,两者的幅值与波形变化都很小,因此具有受干扰小和可靠性高的优点。

并阻尼谐振接地系统对地参数测量与高阻故障选线新方法

针对谐振接地系统对地参数测量不准确以及高阻接地故障选线困难的问题,提出了并阻尼谐振接地系统对地参数测量与高阻故障选线新方法。基于消弧线圈并联阻尼电阻的投切特性,利用投切阻尼电阻前后零序电压幅值与相位的变化关系,测量系统对地电导与电容参数。在系统正常运行时,通过调整并联阻尼电阻值大小,测量各馈线零序导纳值。在发生接地故障后退出阻尼电阻,测量故障后各馈线零序导纳值,可消除系统参数不对称、互感器采样不同步的影响,主动构造故障前后各馈线的零序导纳相角差,实现故障线路的准确判别。在PSCAD仿真环境下对所提方法进行验证,结果表明:所提方法能够精确测量对地电导与电容参数,测量相对误差不高于0.6%;在不同馈线、不同过渡电阻下发生接地故障时,能准确判别故障线路,耐过渡电阻能力达10 kΩ。

灵活接地系统小电阻退出暂态特征及对暂态故障选线方法影响

灵活接地系统发生高阻接地故障时,中性点小电阻并联阶段保护往往拒动,仍需考虑小电阻退出时的暂态特征对已有暂态故障选线方法的影响。并联小电阻退出时伴随一定的暂态过渡过程,文中针对此过程建立灵活接地系统单相接地故障暂态等值电路。分析了并联小电阻退出时系统各暂态电气量特征、不同故障情况对暂态电气量幅值的影响、暂态零序电流与暂态零序电压的约束关系,明确并联小电阻退出后暂态量对暂态故障选线方法稳定性的影响。当灵活接地系统高阻接地故障小电阻退出时,产生的暂态电气量将导致暂态幅值比较法、暂态极性选线法、暂态功率方向法误动,但暂态零序电流投影法选线正确,仿真验证了分析结果的正确性。