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.

Determination of Total Vector Error of the Phasor Measurement Unit (PMU) Using the Phase Angle Error of a Constant Amplitude Voltage Signal

This paper investigates the effect of the Phase Angle Error of a Constant Amplitude Voltage signal in determining the Total Vector Error (TVE) of the Phasor Measurement Unit (PMU) using MATLAB/Simulink. The phase angle error is measured as a function of time in microseconds at four points on the IEEE 14-bus system. When the 1 pps Global Positioning System (GPS) signal to the PMU is lost, sampling of voltage signals on the power grid is done at different rates as it is a function of time. The relationship between the PMU measured signal phase angle and the sampling rate is established by injecting a constant amplitude signal at two different points on the grid. In the simulation, 64 cycles per second is used as the reference while 24 cycles per second is used to represent the fault condition. Results show that a change in the sampling rate from 64 bps to 24 bps in the PMUs resulted in phase angle error in the voltage signals measured by the PMU at four VI Measurement points. The phase angle error measurement that was determined as a time function was used to determine the TVE. Results show that (TVE) was more than 1% in all the cases.

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.

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.

基于HT-STWLS的配电网PMU设计

随着分布式能源和可调负荷大量接入配电网,其随机性和间歇性增加了配电网信号量测的难度,同时配电网存在节点分支类型变化多、通信条件受限等不确定因素,使相量量测单元(PMU)在配电网中应用受到限制。针对配电网节点多分支特点和量测估计高精度要求等问题,提出了基于HT-STWLS的新型配电网同步量测单元,并搭建了实验装置。首先,该装置采用多核分布式结构,通过网络路由模式,能够有效解决节点多分支量测问题,增强节点边缘计算能力;然后,对装置采集信号进行希尔伯特变换(HT)处理后,将数据代入到对称泰勒加权最小二乘(STWLS)过程中进行信号参数估计,获取高精度基波参数值;最后,通过算法验证和实际电网信号的测试,表明所提装置的HT-STWLS算法能够实现配电网信号参数的高精度估计。

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.

基于有限个μPMU和压缩感知理论的主动配电网复故障定位方法

主动配电网(Active distribution network,ADN)动态行为相较传统电网更为复杂,亟需解决其故障检测和定位问题。针对配电网系统中微型同步相量测量装置(Micro phasor measurement unit,μPMU)的运用,提出基于有限个μPMU的复故障定位策略。首先,根据任意线路故障下全网可观的μPMU优化配置方案建立短路、断线故障线路等效模型,通过分析模型将电流故障分量等效至相邻两端节点形成虚拟注入电流向量。其次,利用压缩感知理论求解故障网络节点电压方程,根据重构电流向量中非零元素的位置得到可疑故障节点,并基于可疑故障节点及μPMU配置位置划分动态候选故障域。最后,先分别构建短路、断线区域判据,确定故障区间,再进一步根据区域两端推算电压构造线路判据,精确判断故障线路及类型。基于改进的IEEE 33节点配电网系统进行仿真分析,仿真结果表明,基于有限个μPMU的复故障定位策略是可行的,该策略能够有效判断故障的精确位置。

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.

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。

基于线性最小二乘回归和PMU量测的输电线路参数估计分析方法

电力系统中的架空输电线路参数经常被用于故障定位保护算法、状态估计以及稳定性分析中,而同步相量测量技术能够在线估测电力系统的传输线路参数。提出了一种简单的基于线性最小二乘回归的线路参数的估测方法,其通过线路两端的相量测量单元的电压和电流数据进行正序线路参数估测。所提出的方法计算简单且不需要进行迭代,同时在两个不同电力系统中的四条不同线路中采集的实际相量数据进行了全面测试。测试结果表明:所提出的线路参数估测方法不仅计算简单,同时可以在不同的线路模型中可以快速准确地估测线路电抗和线路电纳,具有较强的通用性和可扩展性。

基于改进的遗传–模拟退火算法和误差度分析原理的PMU多目标优化配置

为了进一步优化同步相量测量单元(phasor measurement unit,PMU)配置的合理性和效率,提出了一种新的误差度分析原理,并使用改进的遗传–模拟退火算法对多个IEEE标准测试系统进行了优化配置。该原理同时考虑了测量冗余度和状态估计的精度,并且避免引入雅可比矩阵,还具备可观测性分析的功能。研究结果表明:该算法不仅可以找到满足全网可观测性的所有PMU数目的配置解,而且进一步提升了全网的测量精度,从而证明了其有效性和优越性。

基于PMU和改进戴维南等值模型的电压稳定在线监视

相量测量单元(PMU)量测和戴维南等值模型的结合,为电压稳定在线监视提供了新思路。文中利用电路原理的相关理论,对传统戴维南等值模型的不足进行了定性分析,并在此基础上提出了一种改进的戴维南等值模型。对改进模型的量测方程、可解性条件、求解方法和误差分析等方面进行了研究,以负荷裕度为电压稳定性指标,将改进模型用于电压稳定在线监视,并对数据窗的选取、改进模型的退化、应用环境等相关问题进行了分析。对IEEE 39节点系统进行了仿真,仿真结果表明改进模型比传统模型具有更好的精度和适用性。

基于SCADA及PMU多时段量测信息的独立线路参数估计方法

基于单一线路两端的监控与数据采集系统(supervisory control and data acquisition system,SCADA)和相量采集装置(phasor measurement unit,PMU)多时段量测信息,建立了5种独立线路的约束最小二乘参数估计模型,其中,量测方程分别由线路两端有功、无功和电压幅值的SCADA量测、电流与电压相量的PMU量测以及线路两端电压相角差的PMU虚拟量测组合形成,约束方程为参数变量的上下限约束。采用Matlab的lsqnonlin优化函数求解参数估计问题,并基于多条典型线路的模拟量测信息仿真分析了所有模型的适用条件。结果表明,在负荷较重、线路较长条件下,利用所建含PMU量测的4种模型,都可以有效估计出线路的阻抗参数。

基于PMU量测数据和SCADA数据融合的电力系统状态估计方法

针对传统静态状态估计方法的缺点,提出了一种改进的电力系统状态估计方法,即将部分节点相量测量单元(phasor measurement unit,PMU)量测数据与监控数据采集(supervisory control and data acquisition,SCADA)量测数据融合进行电力系统的全网状态估计。该方法简化了系统的雅可比矩阵,缩短了计算时间。文章研究了PMU和SCADA系统融合改进后的快速分解法,针对SCADA量测数据的缺点,通过历史数据库对潮流数据进行预测,并依据PMU量测量对系统进行分析,继而进行系统全网状态的动态监测。通过算例证明,与传统的估计方法相比,该方法改善了状态估计的精确性,减少了迭代次数,细致地描绘了电网状态的变化过程,为调度中心下一步的决策提供了依据。

提升PMU动态测量性能的若干方法

为了提高PMU装置在电力系统动态条件下的同步相量测量精度,研究了若干提升PMU装置动态测量性能的方法。采用IEEE C37.118标准中推荐的基于离散傅里叶变换(DFT)的同步相量计算模型,对基于DFT的相量测量算法进行了改进。分析了该改进方法在带外干扰、系统振荡、系统失步、短路或断线故障等动态条件下的特性。设计了等纹波滤波器对相量数据进行滤波处理,保证同步相量的测量精度,最后在PMU装置中实现并进行了测试验证。实验结果表明,通过上述方法的实施,PMU在动态条件下的测量精度得到了提高。

计及PMU的状态估计精度分析及配置研究

基于GPS的同步相量测量单元 (PMU)是一种新型的高精度测量装置 ,能够测量母线电压相量。该文提出了在传统的SCADA量测系统的基础上 ,部分地安装PMU以后混合量测系统的状态估计模型。基于此模型 ,可定量地分析引入PMU以后对状态估计精度的改善程度。

利用PMU数据提高电力系统状态估计精度的方法

同步相量测量单元(phasor measurements units,PMU)因能测得高精度的同步相量数据而被广泛应用于电力系统中,而传统的监控及数据采集系统(supervisory control and data acquisition,SCADA)是电力系统运行和静态安全监视的基础。文中提出了一种PMU与SCADA数据共存的数学模型用于电力系统状态估计。该模型在保留原有SCADA数据的同时,通过虚拟测量方法对PMU观测范围进行大范围拓展,提高数据冗余度及状态估计的精度。仿真结果表明,该方法具有较高的估计精度,且不受网络拓扑结构和PMU数量限制,适于SCADA和PMU数据共存系统。

基于长短期记忆网络的PMU不良数据检测方法

同步相量测量单元(Phasor Measurement Units, PMUs)因其同步性、快速性和准确性,已成为复杂电力系统状态感知的最有效工具之一。但是,现场的复杂环境导致PMU数据存在数据丢失、数据损坏、同步异常、噪声影响等质量问题,严重影响其在系统中的各类应用,甚至威胁电网安全稳定运行。提出了一种基于长短期记忆(Long Short-Term Memory,LSTM)网络的PMU不良数据检测方法。首先分析了LSTM在不良数据检测中的优势。然后基于LSTM网络对时间序列选择记忆的特性,构造了一种双层LSTM网络架构,提出了对原始数据的分解重构方法。在此基础上,定义了两种目标函数,以获得不同的误差特征。提出了一种基于决策树的不良数据阈值确定方法,实现了不良数据的有效检测。通过大量仿真与实测数据验证了该方法的可行性和准确性,可提高PMU数据质量,使其更好地应用于电力系统的各个方面。