基于多代理系统的含DG辐射状配电网故障定位

分布式电源(DG)的高渗透接入使配电网结构发生了改变,导致传统的配电网故障定位算法失效.基于含多DG辐射状配电网拓扑结构的特点,构建多代理系统的配电网故障定位框架,该系统主代理根据子代理上传的电源端故障量测量建立RBF神经网络故障测距模型,估计各电源端到故障点的故障距离,考虑上传量测量误差对相邻线路分界点故障定位的影响...     

基于多种群遗传算法的含分布式电源的配电网 故障区段定位算法

构建了可动态适应多个分布式电源投切的开关函数,同时针对遗传算法的早熟收敛问题,引入多种群遗传算法,提出基于多种群遗传算法的含分布式电源配电网故障区段定位方法。该算法在故障区段定位时规定以系统电源指向用户的方向为馈线正方向,采用多个种群对解空间协同搜索,避免算法陷入局部最优,以最优个体保持代数作为收敛条件,充分提高收敛效率,适用于复杂的含分布式电源的配电网络。通过算例对配电网的故障定位进行仿真,结果表明算法能准确定位,并具有一定的有效性和容错性。     

基于特征暂态零模电流偏态系数的有源配电网单相故障定位方法

含分布式电源的配电网是新型电力系统的重要组成部分,但分布式电源接入导致配电网的故障形态变得复杂,特别是使得中性点经消弧线圈接地的配电网单相故障特征进一步被弱化,配电网单相故障定位变得愈加困难。现有方法存在灵敏度不足或特征量提取与处理困难的问题,通过解析有源配电网单相故障暂态过程中零模电流的分布特点,发现故障点上游和非故障区段的暂态零模电流始终呈振荡衰减且极性相反的特征。进而引入偏态系数刻画暂态零模电流的振荡衰减特征,构造了基于特征暂态零模电流偏态系数的单相故障定位判据,并提出了有源配电网单相故障区段的灵敏定位方法。理论分析和仿真结果表明,该方法在显著降低故障特征量提取及处理难度的基础上,仍能可靠实现有源配电网单相故障区段定位,具有整定计算简单和抗通信干扰能力强等优点,为新一代配电网单相故障快速定位提供理论依据。     

A novel method for SLG fault location in power distribution system using time lag of travelling wave components

This paper presents an approach to single line‐to‐ground fault location in a power distribution system based on the time lag of different modal components . It is a single‐ended method applicable to unsynchronized measurements. The fault location formulation is derived by analyzing the traveling wave propagation characteristic in a distributed parameter model. To tackle the key problem of ground‐mode velocity estimation, an iterative velocity method based on prior knowledge is first proposed. Since the practical measurements are not ideal because of sensor accuracy and nonequivalent transient response, fuzzy neural network is utilized to process traveling wave data acquired at multiple measurements to get an error‐tolerant effect. Furthermore, electromagnetic transient simulations for a 34‐bus test feeder system under various conditions of fault resistances, inception angles, and distributed generator access reveal the robust estimation of fault location. The performance of the proposed method is also validated by simulations with noisy and noise‐free measurements. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.     

基于多目标加权灰靶决策的有源配电网故障区段定位方法

分布式电源接入配电网后,现有利用单一故障特征信号的集中式定位方法难以可靠定位故障区段。针对这一问题,提出一种基于多目标加权灰靶决策的分布式故障区段定位方法。通过分析分布式电源接入对单相接地故障电流的影响,选取分别归一化的区段内等值电阻、区段暂态零模电流波动量、衰减速度及零序电流差作为区段状态评价指标,计算各区段采样数据在健全区段、故障点上游区段及故障区段三种决策结果下的靶心度,判断该区段是否为故障区段。PSCAD/EMTDC仿真结果表明,该方法能够融合多个故障特征,适用于不同故障条件下含IIDG辐射型配电网的故障区段定位。     

基于微扰法的低压有源配电网故障定位方法

分布式电源（DG）接入低压配电网,使配电网潮流分布变得复杂,基于单向潮流的传统故障定位方案不再适用。提出基于微扰法进行相模变换实现含分布式电源低压配电网的故障定位方法。针对配电网三相线路参数不对称,运用微扰原理,通过构造线路参数阻抗矩阵微扰量,求出使原相互耦合的三相网络解耦成3个相互独立序网图的相模变换矩阵,实现了对不对称线路参数阻抗矩阵的解耦;在此基础上,对低压有源配电网依据区段故障前后相模变换电流故障分量的相角差值的变化,推导出判断配电网故障区段的判据,实现对低压配电网的故障定位。通过Matlab/Simulink进行了仿真分析,结果表明：该方法可以快速准确地定位出故障区段,解决了配电网络参数不对称对定位精度的影响。     

基于电流序分量的多源配电网故障定位方法

为了克服传统配电网故障定位方法对分布式电源(Distribution Generation,DG)接入的适应性较差的问题,且需要在馈线中配置大量测量装置导致成本增加的缺点,提出了一种基于电流序分量相关系数的多源配电网故障区段定位方法。首先利用叠加法分析了多端电源配电网故障点电流特征及定位原理,然后通过电源端口测量装置获取故障前后电源输出电流幅值差,再假设不同位置发生故障并计算此时各电源输出故障电流理论值,最后利用相关分析法计算理论与实测两组数据的相关系数,并提取相关系数最大的两个相邻节点实现区段定位。通过ETAP电力系统仿真平台搭建了改进的IEEE33节点模型模拟测量值,利用MATLAB编程进行相关分析,结果验证了方法的正确性。     

基于可达矩阵和贝叶斯定理的含分布式电源的配电网故障区段定位

随着分布式电源接入配电网,电流变为双向流动,导致传统的故障区段定位方法不再适用。为解决含分布式电源的配电网故障区段定位问题,提出了一种基于可达矩阵和贝叶斯定理的故障区段定位方法。不同于传统矩阵算法中使用邻接矩阵描述网络拓扑的思路,使用蕴含信息量更大、全局可观性更强的可达矩阵进行拓扑描述和矩阵运算。为应对信息畸变,利用贝叶斯定理进行容错处理,从而找出故障区段。算例结果表明：该方法具有良好的通用性,对于电机型和逆变型分布式电源,可以统一处理。对于含分布式电源的配电网中的单个故障和多重故障,该方法能够准确定位故障区段并且具有一定的容错性。     

基于零序阻抗模型故障特征的含分布式电源配电网故障区间定位方法

电力系统配电网的故障区间定位是故障精确测距的基础,其研究对尽快恢复供电及减少停电经济损失具有重要作用。分布式电源(DG)的引入增加了故障区间定位的难度,为此提出一种基于电源端测量数据的含DG配电网故障区间定位新算法。该方法在含DG配电网零序阻抗模型的基础上,根据故障点的故障特征搜索故障支路关联节点,定位故障区间。算例仿真结果表明所提出方法具有区间定位准确度高、鲁棒性强和计算速度快的优点。     

基于TensorFlow框架的有源配电网深度学习故障定位方法

随着大规模分布式电源(DG)接入配电网,配电网的结构由传统的辐射型变为多端电源结构,传统的故障定位方法不再完全满足含DG的配电网系统,对此提出一种基于深度学习的有源配电网故障定位方法。首先通过馈线监控终端采集过电流故障数据与节点电压数据,结合各电源出力数据,形成故障数据向量;然后使用Tensorflow构建基于全连接网络的深度神经网络模型,挖掘故障数据向量与故障支路之间的映射联系,形成故障定位模型;最后利用该模型在线定位故障并验证其有效性。模型测试结果表示,与反向传播神经网络、学习向量量化神经网络模型相比,深度学习模型收敛速度更快,故障定位准确率更高,同时在数据畸变或缺失时,模型具有较高的容错性。     

基于改进鸽群算法的含分布式电源配电网故障定位

分布式电源（DG）接入配电网中,使得配电网由传统的单电源辐射状网络变成多电源复杂网络,增加了配电网故障定位的难度。针对DG接入配电网定位问题,提出了一种基于改进鸽群算法的故障区段定位方法。首先,建立了适用于含多个分布式电源的开关函数并对电流编码方式重新定义。其次,对基本鸽群算法中的指南针因子和地标算子进行改进,并通过结合模拟退火算法防止其陷入局部最优,提高了算法的容错性。仿真结果表明,该算法适用于含分布式电源配电网的单重和多重故障区段定位,且在相同故障情况下,改进鸽群算法分别比传统鸽群算法和遗传算法在迭代时间上降低了17.019%和43.763%,具有一定的快速性。     

Fault location in active distribution networks using non-synchronized measurements

Fault location is a necessity to realize the self-healing concept of modern distribution networks. This paper presents a novel fault location method for distribution networks with distributed generation (DG) using measurements recorded at the main substation and at the DG terminals. The proposed method is based on an iterative load flow algorithm, which considers the synchronization angle as an unknown variable to be estimated. Therefore, it obviates the need of synchronized measurements. A new fault location equation is also proposed which is valid for all different fault types, hence the fault type information is not required. The developed method can be simply implemented by minor modifications in any distribution load flow algorithm and it is applicable to different distribution network configurations. The accuracy of the method is verified by simulation studies on a practical 98-node test feeder with several DG units.     

Fault location for transmission lines using inference model neural network

The authors propose a new fault location method that uses a neural network to analyze the distribution pattern of the ground wire current along the power line. This method is able to locate the fault section even for secondary power lines with complicated configurations. The method is based on the inference process, i.e., human experts will analyze the distribution pattern of the current amplitude and phase angle. In locating fault sections, higher precision than ordinary three-layer neural network or the expert system of previous development can be obtained. The proposed neural network comprises three sets of three-layer neural networks which follow the back-propagation learning procedure. The 1st and 2nd neural networks calculate the candidate-1 and candidate-2 for the fault section using current amplitude and phase angle distribution patterns, respectively. The 3rd neural network then performs final fault location using these candidates and a current amplitude distribution pattern. The results evaluated with all possible fault causes indicate that the new method is precise to as high as 98.4 percent even when the measured values differ by 30 percent from predicted ones with EMTP.     

Hybrid classifier for fault location in active distribution networks

This paper presents a fast hybrid fault location method for active distribution networks with distributed generation (DG) and microgrids. The method uses the voltage and current data from the measurement points at the main substation, and the connection points of DG and microgrids. The data is used in a single feedforward artificial neural network (ANN) to estimate the distances to fault from all the measuring points. A k-nearest neighbors (KNN) classifier then interprets the ANN outputs and estimates a single fault location. Simulation results validate the accuracy of the fault location method under different fault conditions including fault types, fault points, and fault resistances. The performance is also validated for non-synchronized measurements and measurement errors.     

Incipient fault location formulation: A time-domain system model and parameter estimation approach

Power systems faults are unavoidable events which affect distribution networks reliability. The fault process in underground cables is gradual and characterized by a series of sub-cycle incipient faults associated with an arc voltage. These events often are unnoticed and eventually result in permanent faults. This paper presents an incipient fault location formulation for distribution networks with underground cables. Presented formulation is composed by a time-domain system model and parameter estimation strategy. System model derivation considers distribution networks inherent features as unbalanced operation and underground distribution cables capacitive effect. Further, incipient fault characteristics as fault arc voltage are considered. The proposed system model is an overdetermined linear system of equations in which the fault location is estimated through a parameter estimation approach. Parameter estimation is made through a Non-Negative Weighted Least Square Estimator (NNWLSE). Smoothing and curve-fitting procedures are applied to input data aiming to decrease the noise effect. A load current compensation strategy is proposed to reduce its effect in the fault current estimation and a back substitution method is proposed for estimation refinement. Validation is performed using real-life distribution network with underground cable data simulated on ATP/EMTP. Test results are encouraging and demonstrate the method’s potential for real life applications. An average error of 1.95% is obtained when compared with 6.48% derived using the state-of-art.     

含分布式电源的配电网故障分析叠加法

传统的配电网故障分析方法已不能满足未来分布式发电系统的需求。为此,文中针对含分布式电源的配电网故障分析展开研究。将常见分布式电源节点划分为PQ节点、PI节点、PV节点和PQ(V)节点几种类型,并根据各节点类型的特点,提出了在故障分析中的处理方法。根据含分布式电源的三相不平衡弱环配电网的特点,在前推回推类方法的基础上,提出了一种含分布式电源的配电网三相故障计算的叠加方法。该方法既能用于辐射型网络,也能用于弱环网络。采用13节点算例对所述方法进行了测试,算例结果证明了该算法的可行性和有效性。     

基于信度融合和滑模控制的暂态电压扰动源容错性定位

提出了一种基于信度融合和滑模控制的含分布式电源(DG)智能配电网中实现暂态电压扰动源(TVDS)容错性自动定位方法。在基于网络化电能质量监测系统平台的TVDS容错性自动定位系统框架下,对其中关键功能模块的实现原理进行详细分析,包括基于电能质量监测点优化布置的电能质量动态状态估计、扰动方向判定信度影响因素分析及信度融合、DG接入对扰动方向判定影响规律分析与归纳。然后,提出一种基于滑模控制的TVDS容错性定位算法,实现综合考虑了扰动方向融合信度、DG接入方向误判校正的TVDS容错性自动定位。最后,通过IEEE 34节点含DG配电网络算例,分析验证了所提TVDS容错性定位方法的可行性和有效性。     

A new fault location method for distribution networks using sparse measurements

In response to the growing demand to improve reliability and quality of power supply, distributed monitoring devices are gradually being implemented in distribution networks. On the other hand, utilities are demanding more accurate and reliable fault location systems to reduce the economic impact of power outages. This paper presents a novel method that takes full advantage of all available measurements to provide an accurate fault location. The developed method first uses an iterative state estimation based algorithm to find the nearest node to the fault location. It then examines all lines connected to the selected node and locates the fault. The performance of the proposed method is studied by simulation tests on a real 13.8 kV, 134-node distribution system under different fault scenarios. The results verify the accuracy of the algorithm and its robustness even under uncertain measured data. The method robustly handles measurement errors, and is applicable to any distribution network with laterals, load taps and heterogeneous lines.     

基于邻接矩阵的低压配电网故障区段定位方法

低压配电网具有线路区段短、分支众多且拓扑结构复杂多变的特点,分布式电源的并网进一步增加了其故障定位的难度。结合图论分析方法和人工智能算法,提出了基于拓扑邻接矩阵的低压配电网故障区段定位方法。首先生成与配电网故障监测单元配置相对应的邻接矩阵,其次建立故障电流在故障监测单元拓扑中的连通性等效模型,根据故障电流从电源到估计故障区段的最短路径计算故障监测信息。以此为基础建立了考虑多重故障的低压配电网故障定位模型并利用粒子群优化算法进行模型求解。仿真算例验证了所提方法能够有效提高故障定位的效率和准确度。     

基于电压偏差向量2-范数的主动配电网故障定位新方法

随着分布式电源在配电网中渗透率的提高,现有的一些故障定位算法可能失效,对此,提出了一种基于最小电压偏差向量2-范数的故障定位算法。首先,根据配电网各节点注入的等效故障电流计算各电源处电压变化值,并比较各电源处电压变化测量值与电压变化计算值之差,搜索差值向量最小2-范数对应的故障电流注入节点从而确定故障区段的第一个节点,其次,通过该节点两侧电压差值变化确定故障区段的另一节点,实现故障区段定位。最后,利用IEEE 34节点三相不平衡系统进行了故障定位分析,结果表明算法物理意义明确、计算过程简单且具有良好的故障定位准确度。