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.

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.

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.

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。

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.

考虑关键节点和重要线路的电力系统PMU优化配置

相量测量单元(PMU)是一种测量电压和电流的设备,随着电力系统的规模和结构越来越复杂,PMU的数量也大幅增加.利用改进蝠鲼觅食优化算法(IMRFO)对PMU进行了配置,使电力系统在正常运行、考虑关键节点和考虑重要线路时都能完全可观测,同时使PMU配置数最少.在蝠鲼觅食算法的基础上增加了混沌映射以提升算法的收敛速度,增加了跳跃因子以提高算法的勘测能力,从而避免算法陷入局部最优.通过度、特征向量和接近度三种指标,利用TOPSIS综合评价寻找系统的关键节点,利用线的中间中心度判断系统的重要线路.对IEEE-14和IEEE-69节点系统进行了实验仿真,结果表明:IMFRO算法与未改进时相对比,在配置时所需的PMU数和迭代次数均有一定的减少,验证了所提方法对解决PMU优化配置问题的有效性和优越性.

基于μPMU和二分搜索法的辐射状配电网电压暂降源定位

快速准确地定位电压暂降源,对提高供电可靠性和明确供需双方责任具有重要意义。文中提出一种基于微型同步相量测量装置(micro-phasor measurement unit,μPMU)和二分搜索法的辐射状配电网电压暂降源精确定位方法,首先利用装设μPMU的分层电路求解暂降源电流,在各分层电路分别假设暂降源电流注入各母线节点,进而计算末端母线虚拟电压变化量,并与实测的末端母线电压变化量求误差,根据误差大小确定暂降源邻近母线。然后在暂降源邻近母线相邻区段的中点设置虚拟母线,利用二分搜索法快速缩小定位区间,实现暂降源的精确定位。最后在MATLAB中利用IEEE 33节点模型对文中方法进行验证,结果表明该方法对辐射状配电网中不同位置、不同类型的电压暂降源定位精度高,且具有一定的抗干扰能力。

基于MMoE和GRU的PMU数据有损压缩算法

为解决同步向量量测装置广泛部署带来的数据冗余度与数据量快速增长、系统数据储存及传输成本大幅增加的问题,本文提出一种基于多任务学习和门控循环单元自编码器的有损数据压缩算法。首先,根据同步向量量测装置三相量测数据具有相关性的特点,采用多门控混合专家模型和门控循环单元构建了变分自编码器一次数据压缩模型,将各相数据进行特征融合,利用多任务学习挖掘三相量测数据之间的相关性;其次,使用无损压缩算法对一次压缩数据进行二次压缩,得到最终压缩数据;最后,通过算例分析表明,所提算法能够充分利用各相数据之间的相关性,实现对压缩数据的高精度重构,提高数据压缩效率。

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μPMU测量数据的配电网线路阻抗参数估计

为解决长期以来低压配电网线路阻抗参数缺失的问题,基于微型同步相量测量单元(micro phasor measurement unit,μPMU)提供的测量数据,首先,以线路阻抗参数为变量,根据测量数据推算台区首端状态值,以推算值与实测值的差值作为目标函数,将阻抗参数的求解转化为多目标最优化问题;然后,通过实验确定合适的目标函数权重系数取值和数据集大小,以减少误差对求解结果的影响,使模型求解精度达到较高水准;最后,搭建了包含26个负荷节点、34条线路支路的低压配电网台区算例,在考虑实际测量误差的情况下,线路阻抗参数计算平均误差为3.87%。算例结果表明,所提方法实现了阻抗参数的精确计算,验证了所提模型的有效性。

基于流聚类的PMU异常数据辨识算法

为保证同步相量测量装置(phasor measurement unit,PMU)采集数据的准确应用,须排除其量测值中的异常数据。现有PMU异常数据辨识算法存在算法复杂度高、难以在线更新、多源数据难以校准、依赖多源数据应用难度大等不足。为此,文中从PMU事件数据和异常数据模型及PMU异常数据判别信息熵定义出发,提出基于该信息熵的异常数据辨识框架。在此框架基础上,基于利用层次方法的平衡迭代规约和聚类(balanced iterative reducing and clustering using hierarchies,BIRCH)算法提出PMU异常数据辨识算法;然后,对所提出的算法进行原型实现,并针对某变电站的PMU采集数据集进行算法实验验证。实验结果表明,与一类支持向量机(one-class support vector machine,OCSVM)算法与间隙统计算法相比,文中算法的准确度及实时性均具有较强的优势。

基于改进蝙蝠算法的PMU优化配置研究

传统单一的能源系统正过渡为多能流耦合综合能源系统,综合能源系统正面临量测误差问题。受投资约束,同步相量测量装置(phasor measurement unit,PMU)无法大面积配置,而系统内节点数多,供需不平衡,影响系统平稳运行。为此,提出一种适用于综合能源系统内PMU优化布点的方法,监测系统运行状况。首先,考虑热电气之间的耦合特性建立综合能源系统状态估计模型;然后,限制系统内PMU配置个数,以提高状态估计精度为目标,利用改进的蝙蝠算法(improved bat algorithm,IBA)对PMU进行优化选址;最后,通过IEEE 30节点系统算例,将所提算法与遗传算法和模拟退火算法对比,算例结果表明该算法具有更高的可行性和优越性。

基于无功损耗的线路两端PMU相角差时变偏差修正方法

相量测量单元(PMU)的优势之一是提供同步的相角数据。然而,因时间同步异常、装置故障等因素,部分实测PMU相角数据存在异常,影响PMU应用。提出一种基于无功损耗的线路两端PMU相角差时变偏差修正方法。首先,基于线路无功损耗量测值与估计值构造单时刻相角差偏差估计模型;然后,针对单时刻估计模型受噪声影响大的问题,基于相角差和线路参数间关系,将利用单时刻数据的时变偏差估计模型转化为利用多时刻数据的恒定线路参数估计模型;最后,通过黄金分割算法估计获取线路参数后,结合PMU幅值、功率数据反推出PMU相角差偏差,实现PMU时变相角差偏差的估计与修正。仿真和某省实测PMU数据测试结果表明:该方法可准确估计相角差偏差,抗噪能力强;与现有方法相比,该方法仅需单工况数据即可实现估计与修正,适用于不同时变类型偏差的修正。

计及节点故障率的微电网μPMU多目标优化配置策略

微电网动态行为相较传统电网更为复杂,亟须解决其故障检测和定位问题。针对微电网系统中微型同步相量测量单元(μPMU)的优化配置问题进行研究,提出了计及成本、节点故障率和冗余度的μPMU多目标优化配置策略。以μPMU装设成本最低、节点故障率总和最高、可观冗余度最高为多目标函数,在考虑零注入节点的基础上,根据系统正常状态和任意线路故障下的约束条件,建立了多目标优化配置模型。采用差分进化算子对非支配排序遗传算法(NSGA-Ⅱ)进行改进,并用改进后的算法对模型进行求解,选择出最优折中解。基于改进的IEEE 30节点微电网系统进行仿真分析,仿真结果表明,计及节点故障率的多目标优化配置策略是可行的。所提策略能够有效地对微电网系统进行全网可视化监测,并兼顾经济性和准确性。

基于μPMU的智能配电网预想故障集组合筛选方法

智能配电网预想故障集筛选是系统安全态势评估的重要基础。为了全面精准感知智能配电网的安全风险,引入具有实时性、同步性、准确性和量测数据全面性的微型同步相量测量单元(μPMU),提出一种基于其高密集采样数据的融合类内与类间距离的加权K-means聚类方法(KICIC)和云理论的预想故障集组合筛选排序方法。首先遍历智能配电网各节点发生各故障类型的场景构建故障数据集;然后采用KICIC算法进行故障数据集聚类分析,进而基于云模型的云数字特征客观量化评估故障类严重度不确定性的危害并输出预想故障集;最后算例结果表明:融合KICIC聚类和云模型的预想故障集组合筛选排序方法从数据挖掘层面实现高风险预想故障集的可靠筛选。

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.

Rapid Fault Analysis by Deep Learning-Based PMU for Smart Grid System

Smart Grids(SG)is a power system development concept that has received significant attention nationally.SG signifies real-time data for specific communication requirements.The best capabilities for monitoring and controlling the grid are essential to system stability.One of the most critical needs for smart-grid execution is fast,precise,and economically synchronized measurements,which are made feasible by Phasor Measurement Units(PMU).PMUs can pro-vide synchronized measurements and measure voltages as well as current phasors dynamically.PMUs utilize GPS time-stamping at Coordinated Universal Time(UTC)to capture electric phasors with great accuracy and precision.This research tends to Deep Learning(DL)advances to design a Residual Network(ResNet)model that can accurately identify and classify defects in grid-connected systems.As part of fault detection and probe,the proposed strategy uses a ResNet-50 tech-nique to evaluate real-time measurement data from geographically scattered PMUs.As a result of its excellent signal classification efficiency and ability to extract high-quality signal features,its fault diagnosis performance is excellent.Our results demonstrate that the proposed method is effective in detecting and classifying faults at sufficient time.The proposed approaches classify the fault type with a precision of 98.5%and an accuracy of 99.1%.The long-short-term memory(LSTM),Convolutional Neural Network(CNN),and CNN-LSTM algo-rithms are applied to compare the networks.Real-world data tends to evaluate these networks.

基于智能电表和PMU混合量测的低压配电网状态估计方法研究

随着高级量测体系的不断完善和同步相量量测PMU等高精度新型量测装置的发展,为确保低压配电网状态估计性能,提出一种基于智能电表和PMU混合量测的低压配电网状态估计方法。该方法将高级量测体系中的智能电表采集的电压幅值、功率实时量测和PMU同步相量量测相结合,以节点注入电流平衡方程为基础,建立基于指数型权函数的加权最小二乘法模型。模型中利用数据的残差对权函数进行修正,以提高状态估计的抗差性和收敛性。最后基于IEEE 14节点算例系统,对该方法进行仿真分析。仿真结果表明,该方法相较于采用传统加权最小二乘法的状态估计模型,对含有高误差数据的量测数据具有更高的精确性。

IEC微电网环境下PMU的性能监测方法

可靠的保护、通信和控制是公用事业变电站数字保护方案的重要特征,微电网可以作为一种替代解决方案,用于安装长距离输电和配电线路。在微电网中,有许多智能电子设备可用于电力网络的控制和监测,其中一种设备是相位测量单元,它是一种基于微处理器的智能设备,能够收集高分辨率的数据,检查电能质量并记录干扰情况。然而目前还存在一些问题,例如多个设备之间的互操作性以及各个控制系统之间的协调。本文使用IEC 61850905标准,并利用Operational Network Technology软件对PMU在微电网环境中的性能进行设计和测试。研究结果表明,PMU在整个微电网中的端对端通信和延迟方面表现良好。