High Impedance Fault Detection of Distribution Network by Phasor Measurement Units

This paper proposes a new algorithm for High Impedance Fault (HIF) detection using Phasor Measurement Unit (PMU). This type of faults is difficult to detect by over current protection relays because of low fault current. In this paper, an index based on phasors change is proposed for HIF detection. The phasors are measured by PMU to obtain the square summation of errors. Two types of data are used for error calculation. The first one is sampled data and the second one is estimated data. But this index is not enough to declare presence of a HIF. Therefore another index introduces in order to distinguish the load switching from HIF. Second index utilizes 3rd harmonic current angle because this number of harmonic has a special behaviour during HIF. The verification of the proposed method is done by different simulation cases in EMTP/MATLAB.

Optimal Placement of Phasor Measurement Units Using a Modified Canonical Genetic Algorithm for Observability Analysis

This paper proposes a method for optimal placement of synchronized PMUs （phasor measurement units） in electrical power systems using a MCGA （modified canonical genetic algorithm）, which the goal is to determine the minimum number of PMUs, as well as the optimal location of these units to ensure the complete topological observability of the system. In case of more than one solution, a strategy of analysis of the design matrix rank is applied to determine the solution with the lower number of critical measurements. In the proposed method of placement, modifications are made in the crossover and mutation genetic operators, as well as in the formation of the subpopulation, and are considered restrictive hypotheses in the search space to improve the performance in solving the optimization problem. Simulations are performed using the IEEE 14-bus, IEEE 30-bus and New England 39-bus test systems. The proposed method is applied on the IEEE 118-bus test system considering the presence of observable zones formed by conventional measurements.

Subsynchronous oscillation monitoring and alarm method based on phasor measurements

Owing to the large-scale grid connection of new energy sources, several installed power electronic devices introduce sub-/supersynchronous inter-harmonics into power signals, resulting in the frequent occurrence of subsynchronous oscillations(SSOs). The SSOs may cause significant harm to generator sets and power systems;thus, online monitoring and accurate alarms for power systems are crucial for their safe and stable operation. Phasor measurement units(PMUs) can realize the dynamic real-time monitoring of power systems. Based on PMU phasor measurements, this study proposes a method for SSO online monitoring and alarm implementation for the main station of a PMU. First, fast Fourier transform frequency spectrum analysis is performed on PMU current phasor amplitude data to obtain subsynchronous frequency components. Second, the support vector machine learning algorithm is trained to obtain the amplitude threshold and subsequently filter out safe components and retain harmful ones. Finally, the adaptive duration threshold is determined according to frequency susceptibility, amplitude attenuation, and energy accumulation to decide whether to transmit an alarm signal. Experiments based on field data verify the effectiveness of the proposed method.

A Robust Asynchrophasor in PMU Using Second-Order Kalman Filter

Phasor Measurement Units(PMUs)provide Global Positioning System(GPS)time-stamped synchronized measurements of voltage and current with the phase angle of the system at certain points along with the grid system.Those synchronized data measurements are extracted in the form of amplitude and phase from various locations of the power grid to monitor and control the power system condition.A PMU device is a crucial part of the power equipment in terms of the cost and operative point of view.However,such ongoing development and improvement to PMUs’principal work are essential to the network operators to enhance the grid quality and the operating expenses.This paper introduces a proposed method that led to lowcost and less complex techniques to optimize the performance of PMU using Second-Order Kalman Filter.It is based on the Asyncrhophasor technique resulting in a phase error minimization when receiving the signal from an access point or from the main access point.The MATLAB model has been created to implement the proposed method in the presence of Gaussian and non-Gaussian.The results have shown the proposed method which is Second-Order Kalman Filter outperforms the existing model.The results were tested usingMean Square Error(MSE).The proposed Second-Order Kalman Filter method has been replaced with a synchronization unit into thePMUstructure to clarify the significance of the proposed new PMU.

Estimation Method of Center of Inertia Frequency Based on Phasor Measurement Data

In the world, recent increased disturbances, congestion management problems, and increases of complexity in operating power systems have brought the need for integrations and improvements of power systems. Advanced applications in WAMPAC （wide area monitoring, protection, and control） systems provide a cost effective solution to improve system planning, operation, maintenance, and energy trading. Synchronized measurement technology and the application are an important element of WAMPAC. In addition, PMUs （phasor measurement units） are the most accurate and advanced time-synchronized technology available for WAMPAC application. Therefore, the original measurement system of PMUs has been constructed in Japan. This paper describes the estimation method of a center of inertia frequency by applying actual measurement data. The application of this method enables us to extract power system oscillations from measurement data appropriately. Moreover, this proposed method will help to the clarification of power system dynamics and this application will make it possible to realize the monitoring of power system oscillations associated with the power system stability.

基于二阶罚函数自适应变阶拟合的全局电网惯量时空感知

随着传统同步发电机逐步被新能源发电替代,电力系统惯量不断降低,惯量时空评估对保障系统安全稳定运行愈加重要。首先,提出了基于二阶罚函数自适应变阶拟合的全局电网惯量时空感知方法;其次,基于同步相量测量单元(PMU)准确估测系统各空间节点的频率变化率及功率变化,采用二阶罚函数对PMU监测数据进行预处理,并对每段拟合数据的处理过程加以自适应变阶拟合;最后,通过两种方法的结合,可实现对电力系统广义节点惯量的时空感知。通过仿真案例分析,验证了所提方法在大、小扰动工况下对惯量时空评估的准确性及适用性。

基于PMU梯度动态偏差的新型电力系统快速稳定性

在能源转型和科技进步双重推动下,高比例可再生能源和高比例电力电子设备正成为电力系统发展重要趋势和关键特征.新型电力系统动态行为随之发生重大变化,传统小干扰稳定性分析方法满足使用需要,在应对运行工况快速变化场合时尚存在亟需解决问题.提出基于同步相量测量装置(PMU)数据梯度动态偏差李雅普诺夫直接分析法对新型电力系统小干扰稳定性进行分析,利用PMU数据矩阵降维得到低维矩阵,代入含双馈异步风力发电机组的电力系统矩阵模型,求解李雅普诺夫方程式得到对角矩阵,通过判定矩阵正定性对系统稳定性作出判断.利用求解后得到的对角矩阵计算相应状态变量动态偏差值,对相应状态变量曲线使用梯度下降法迭代计算该曲线极值点数值,时间加权计算整个振荡过程时间加权动态偏差量,为阻尼稳定控制器配置位置提供指导.利用含风力发电新英格兰10机39节点系统仿真验证方法可以达到改善系统小干扰稳定性以及对新型电力系统快速稳定性分析的有效性.

基于相量测量单元优化配置的配电网谐波状态估计研究

随着大量电力电子设备的接入,配电网谐波问题愈发严重。谐波状态估计的准确性直接影响到后续的谐波治理效果。相量测量单元(phasor measurement unit,PMU)可以实时测量节点电压与支路电流,可借助其实现谐波状态估计。然而目前PMU价格较高,如何进行合理的优化配置保证全网谐波状态可观,同时提高谐波状态估计的准确性,是亟待解决的问题。首先构建以PMU经济配置和谐波状态估计精度最高为目标的PMU优化配置模型,并提出一种改进二进制粒子群-遗传混合算法用于求解。随后在实时仿真器中搭建IEEE14节点模型,选用均值插补法以及Vondrak滤波法进行数据处理并分析了优化所得多种PMU配置场景对谐波状态估计的影响。结果表明:所提算法从减少投资成本及降低谐波状态估计误差角度考虑,能够给出合理的PMU配置方案,有助于支撑工程决策。

基于混合量测的新能源电力系统动态频率预测方法

高渗透率新能源波动下系统动态频率预测是实现受端网络频率安全态势感知的基础。该文提出一种基于混合量测和物理状态方程联合驱动的新能源电力系统双向树状长短期记忆网络(combined equation-of-state-driven and data-driven bi-directional tree-struct long short term memory,CEOSD-BITREE-LSTM)动态频率预测方法。首先,引入双层多头注意力图神经网络,提出考虑同步相量测量单元(synchronous phasor measurement unit,PMU)和数据采集与监视控制系统装置(supervisory control and data acquisition,SCADA)量测差异性和时序同步性的混合量测融合策略;其次,依据PMU密集采样特性,建立计及源网荷物理联系的线性时变状态方程,刻画物理-数据空间的频率特征交互关系;然后,考虑新能源出力、负荷波动等不确定因素,结合以PMU并行搜索调频资源形成的拓扑结构,构建CEOSD-BITREE-LSTM动态频率预测模型,实现系统频率态势的高精度预测。最后,以改进新英格兰10机39节点、三区互联系统为算例,验证该文所提方法的可行性和有效性。

基于改进鲸鱼优化算法的同步相量测量单元多目标优化配置

针对因配电网节点数目多但投资成本少造成的同步相量测量单元(phasor measurement unit,PMU)供需不平衡问题,建立了考虑PMU配置个数、状态估计误差的PMU多目标优化配置模型,优化问题的目标是最小化所需的PMU个数和最小化状态估计误差。并提出一种改进鲸鱼优化算法来求解模型。首先引入非支配排序和拥挤度计算来选择并排序Pareto非支配解,保证算法求解全局最优值的能力,其次引入Levy飞行策略对鲸鱼优化算法的螺旋更新位置进行变异扰动,使算法不易陷入局部最优。最后,采用优化配置模型对IEEE 33标准节点系统进行仿真计算。结果表明,与遗传算法和粒子群算法相比,采用改进鲸鱼优化算法求解PMU多目标优化配置模型具有更高的可行性和有效性。

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Curvature Quantified Douglas-Peucker-based Phasor Measurement Unit Data Compression Method for Power System Situational Awareness

Facing constraints imposed by storage and bandwidth limitations,the vast volume of phasor meas-urement unit(PMU)data collected by the wide-area measurement system(WAMS)for power systems cannot be fully utilized.This limitation significantly hinders the effective deployment of situational awareness technologies for systematic applications.In this work,an effective curvature quantified Douglas-Peucker(CQDP)-based PMU data compression method is proposed for situational awareness of power systems.First,a curvature integrated distance(CID)for measuring the local flection and fluc-tuation of PMU signals is developed.The Doug-las-Peucker(DP)algorithm integrated with a quan-tile-based parameter adaptation scheme is then proposed to extract feature points for profiling the trends within the PMU signals.This allows adaptive adjustment of the al-gorithm parameters,so as to maintain the desired com-pression ratio and reconstruction accuracy as much as possible,irrespective of the power system dynamics.Fi-nally,case studies on the Western Electricity Coordinat-ing Council(WECC)179-bus system and the actual Guangdong power system are performed to verify the effectiveness of the proposed method.The simulation results show that the proposed method achieves stably higher compression ratio and reconstruction accuracy in both steady state and in transients of the power system,and alleviates the compression performance degradation problem faced by existing compression methods.Index Terms—Curvature quantified Douglas-Peucker,data compression,phasor measurement unit,power sys-tem situational awareness.

基于多源量测的主配网一体化状态估计方法

针对主配网结构复杂且测量设备无法全节点覆盖的问题,本文基于多源量测数据提出了主配网一体化的状态估计算法。针对不同的量测条件,设计相应的状态估计模型,构建非线性动力学系统以保证状态估计算法的全局收敛性。同时,考虑主配网状态估计的协调问题,融合更新频率高的数据采集与监视控制系统,同步相量测量装置量测和更新频率低的高级计量架构量测,开展主配一体化的状态估计方案设计。在IEEE标准算例基础上改进的主配网络上对该方法进行相关测试,仿真结果验证了所提算法的有效性。

阻抗模裕度指标在新能源多馈入系统静态电压稳定评估的适应性分析

集中送出的新能源场站大多位于电网末端,随着其有功出力的增加,易出现静态电压失稳。该文将传统的阻抗模指标的应用对象由负荷推广至新能源场站,丰富了该指标在静态电压稳定评估方面的适用性场景,包括含无功补偿的新能源系统、新能源集群馈入系统、以及新能源多机多馈入系统。具体地,首先,分析新能源单馈入系统的临界静态电压稳定条件,并据此给出用于评估新能源场站静态电压稳定性的阻抗模裕度指标及稳定判据;其次,通过将无功补偿设备并入系统阻抗,分析无功补偿对于指标的影响;再次,证明在新能源的集群馈入系统中,公共耦合点(point of common coupling,PCC)的电压失稳将发生在单个新能源场站之前,并据此确定PCC点作为指标的计算节点;之后,为考虑多机多馈入系统中不同新能源场站间的影响,在指标的计算过程中,保留待评估的关键新能源场站,将其他新能源场站等值为阻抗,并入节点阻抗矩阵中,实现方法在多机多馈入系统中的扩展应用。最后,基于PSD-BPA中建立的单机单馈入系统、多机多馈入系统、以及某省实际大电网算例验证指标的有效性。

次/超同步间谐波影响下PMU基波频率测量误差机理分析及抑制

高比例新能源和高比例电力电子设备在电网中产生了大量的次/超同步间谐波,导致同步相量测量装置(PMU)的基波频率测量出现较大偏差,影响各类频率相关应用的效果。因此,文中提出了一种次/超同步间谐波影响下基波频率测量误差机理分析及其抑制方法。首先,建立了频率测量算法的频域特性模型,基于此推导了单个间谐波和对称次/超同步间谐波影响下的基波频率振荡数学模型,揭示了间谐波干扰下的基波频率测量误差机理。其次,根据频率的振荡行为,建立了多分量叠加的拟合模型,提出了基于傅里叶变换和最小二乘法的短时窗频率振荡分量抑制方法,大幅降低了基波频率偏差。最后,仿真和现场录波数据测试表明,所提方法极大降低了频率测量误差。

基于混合量测状态估计的配电网故障定位方法

微型相量测量单元(micro-phasor measurement unit,μPMU)为配电自动化的进一步升级提供了良好的量测基础,但现阶段电网中μPMU数量有限,难以满足传统配电网故障定位的需求。针对该问题,结合电网中μPMU与智能电表等量测设备,并基于虚拟节点的多重状态估计方法,提出了一种基于混合量测状态估计的故障定位方法。首先,通过等效变换将μPMU和智能电表的测量信息输入到故障状态估计器当中。然后,利用μPMU将网络划分为不同的区域。根据状态估计结果计算故障电流,缩小故障搜索区域以减少计算复杂度。为了识别区域内的故障位置,通过设置附加虚拟故障节点形成多种特定的故障拓扑结构并执行多重状态估计,计算出用于识别故障位置的加权测量残差指标,以确定故障位置。最后,在实时仿真系统(real-timedigitalsimulation, RTDS)中进行仿真测试,结果表明所提方法在不同故障场景下均能准确有效地定位故障,且对量测误差具有较好的鲁棒性。

基于1DCNN-DACLSTM模型的风电超短期功率预测方法

风电超短期功率预测是电网运行态势感知的重要基础。针对风电随机波动性将给电网带来高动态扰动的问题,利用同步相量测量装置(PMU)的高频采样能力,提出一种融合一维卷积(1DCNN)和双重注意力卷积长短时记忆网络(DACLSTM)模型的风电超短期功率预测方法。首先,基于具有高精度高密集采样的PMU装置对风电超短期功率进行实时量测。然后,利用1DCNN在特征提取和时间卷积减少计算复杂度方面的优势,充分挖掘由PMU采样得到的风电功率及相关因素量测数据关键特征,进而结合DACLSTM模型自主分析风电功率数据与输入特征间的关联关系,实现基于1DCNN-DACLSTM组合模型的风电超短期功率高动态变化趋势预测。最后以已配置PMU的某实际风电场为例,验证所提方法的可行性和有效性。

基于MRMR-DRSN的电力系统暂态稳定评估

随着电力系统的广泛互联互通和相量测量单元(phasor measurement unit,PMU)的广泛应用,电力系统的安全运行面临着巨大挑战。为实现对电力系统运行状态快速、准确、有效的评估,提出了一种基于最大相关-最小冗余(max-relevance and min-redundancy,MRMR)准则和深度残差收缩网络(deep residual shrinkage network,DRSN)的暂态稳定评估方法。首先,利用MRMR准则进行特征选择,并将筛选后的关键特征和相应的类标签作为DRSN模型的输入和输出进行离线训练。然后,制定模型更新机制以应对电力系统运行工况变化。最后,基于PMU实时数据和训练好的DRSN,可立即提供暂态稳定评估结果。在IEEE 10机39节点系统上进行测试,结果表明,所提方法相较于其他数据驱动方法的综合评估性能更优异,同时还具有较好的抗噪性能和鲁棒性。

基于ADASYN和GRU的电力系统暂态稳定评估方法

随着电力系统的广域互联,结构愈发复杂,电力系统暂态稳定性的准确评估受到了更多的关注。提出了基于自适应综合过采样和门控循环单元的电力系统暂态稳定评估方法。首先,利用自适应综合过采样算法处理电力系统样本类别不平衡问题,合成少数类样本。然后,使用得到的平衡样本集对门控循环单元评估模型进行训练。最后,在线应用时,输入相量测量单元采集的实时数据,通过训练好的门控循环单元快速评估电力系统暂态稳定性。在电力系统仿真软件PSS/E提供的23节点系统上进行仿真测试,仿真结果验证了所提方法的有效性。

基于零序分量的阻抗法配电网故障定位技术

精准故障定位对提高配电网的安全性、可靠性有着重要意义。提出了一种基于零序分量的阻抗法定位技术。依据线电压对称性及零序电压幅值判断是否发生单相接地故障,再利用安装在母线处及线路末端的同步相量测量单元获取线路发生单相接地故障后的稳态零序信号。在此基础上,根据线路两端零序电压、母线侧零序电流及线路零序参数建立各区段发生单相接地故障时的测距方程,以区段线路长度为限制排除伪根、确定真实根,通过遍历系统各区段可同时实现区段定位及故障测距。在EMTP/ATP仿真软件中搭建10 kV配电网。结果表明,所提方法在混合线路、多分支线路等多种配电网结构下都能保持较高精度,且测距精度不受接地电阻和故障相角的影响。