Fault Characterization Based on Synchrophasor Data Using Heuristic Approach

With the advent of phasor measurement unit (PMU) technology, the grid observability has got a new dimension. This facet of technology helps in getting the real-time and dynamic scenario of the grid operations which was a remote possibility some decades before. Achieving this level of observability puts us at an advantage of responding to the system faults with reduced response time, and helps in restoring the grid stability within fraction of second. This paper demonstrates the detailed fault characterization from the PMU inputs, after illustrations from various real-time examples and different faults occurred in India. This paper tries to shed some light on areas where the accurate fault characterization can help the operator in taking the right decision for reliable grid operations.

Improved Subsynchronous Oscillation Parameter Identification with Synchrophasor Based on Matrix Pencil Method in Power Systems

The subsynchronous oscillations(SSOs)related to renewable generation seriously affect the stability and safety of the power systems.To realize the dynamic monitoring of SSOs by utilizing the high computational efficiency and noise-resilient features of the matrix pencil method(MPM),this paper propos es an improved MPM-based parameter identification with syn chrophasors.The MPM is enhanced by the angular frequency fitting equations based on the characteristic polynomial coeffi cients of the matrix pencil to ensure the accuracy of the identi fied parameters,since the existing eigenvalue solution of the MPM ignores the angular frequency conjugation constraints of the two fundamental modes and two oscillation modes.Then,the identification and recovery of bad data are proposed by uti lizing the difference in temporal continuity of the synchropha sors before and after noise reduction.The proposed parameter identification is verified with synthetic,simulated,and actual measured phase measurement unit(PMU)data.Compared with the existing MPM,the improved MPM achieves better accuracy for parameter identification of each component in SSOs,better real-time performance,and significantly reduces the effect of bad data.

Data quality issues for synchrophasor applications PartⅠ:a review

Synchrophasor systems, providing low-latency,high-precision, and time-synchronized measurements to enhance power grid performances, are deployed globally.However, the synchrophasor system as a physical network,involves communication constraints and data quality issues, which will impact or even disable certain synchrophasor applications. This work investigates the data quality issue for synchrophasor applications. In Part I, the standards of synchrophasor systems and the classifications and data quality requirements of synchrophasor applications are reviewed. Also, the actual events of synchronization signal accuracy, synchrophasor data loss, and latency are counted and analyzed. The review and statistics are expected to provide an overall picture of data accuracy,loss, and latency issues for synchrophasor applications.

基于新型改进矩阵束的便携式电网同步相量测量装置

针对传统同步相量算法受电网间谐波影响较大的问题,本文提出了一种基于新型改进矩阵束的电网同步相量测量算法.将电网信号构建成Hankel矩阵,对其进行奇异值分解并使用自适应定阶方法滤除其中的噪声干扰分量,通过矩阵运算得到准确的电压幅值、频率及相角.经过仿真验证,本算法对含有间谐波分量的电网信号测量结果优于流行的傅里叶变换测量算法.并根据此算法设计了一款便携式电网同步相量测量装置,可解决传统同步相量测量设备体积庞大、成本较高的问题,利用高精度卫星同步信号监测晶振实际频率,动态调整采样控制参数降低采样时间误差,将同步采样的电网波形参数与时间标签等无线传输至移动终端,完成电网信号幅值、频率及相角计算.实际测试表明,本装置具有较高的测量精度,其中电压幅值、频率和相角测量误差分别为0.038 5%、0.000 51 Hz和0.053 7°,满足《电力系统同步相量测量装置检测规范》(GB/T 26862—2011),具有较强的实际应用价值.

Survey on synchrophasor data quality and cybersecurity challenges, and evaluation of their interdependencies

Synchrophasor devices guarantee situation awareness for real-time monitoring and operational visibility of smart grid. With their widespread implementation,significant challenges have emerged, especially in communication, data quality and cybersecurity. The existing literature treats these challenges as separate problems,when in reality, they have a complex interplay. This paper conducts a comprehensive review of quality and cybersecurity challenges for synchrophasors, and identifies the interdependencies between them. It also summarizes different methods used to evaluate the dependency and surveys how quality checking methods can be used to detect potential cyberattacks.

Applications of synchrophasor technologies in power systems

Synchrophasors are time-synchronized electrical measurements that represent both the magnitude and phase angle of the electrical sinusoids. Synchrophasors are measured by fast time-stamped devices called phasor measurement units(PMUs) to constitute the basis of realtime monitoring and control actions in the electric grid.Due to its enhanced situational awareness capabilities,many applications of PMUs are presented in the literature in the past decades. This paper presents a comprehensive summary of synchrophasor technology, its architecture,optimal placement techniques and its applications in electric power transmission and distribution systems. These applications include wide-area situational awareness and monitoring, state estimation, fault location and protective relaying, islanding detection etc. This review also covers some of the existing challenges in its implementation and its potential applications.

Data quality issues for synchrophasor applications Part Ⅱ:problem formulation and potential solutions

This work investigates the data quality issue for synchrophasor applications, and pays particular attention to synchronization signal loss and synchrophasor data loss events. First, the historical synchronization signal loss events are analyzed and the potential reasons and solutions are discussed. Then, the scenario of a small amount of synchrophasor data loss is studied and a Lagrange interpolating polynomial method is used to adaptively estimate the incomplete and missing data. The performance of proposed method is demonstrated with simulation results.Specifically, the proposed method considers the trade-off between the estimation accuracy and the hardware cost,and could be efficiently employed in reality.

Malicious Synchrophasor Detection Based on Highly Imbalanced Historical Operational Data

By maliciously manipulating the synchrophasors produced by phasor measurement units in power systems,cyber attackers can mislead the control center into taking wrong actions.From the viewpoint of machine learning,normal and malicious synchrophasors may exhibit different spatial distribution characteristics when mapped into a latent space.Hence,a malicious synchrophasor detector can be acquired by training a classification model with instances derived from historical operational synchrophasor data.However,malicious synchrophasors occur infrequently in practice.It is likely to incur a great deal of effort and may even introduce inevitable experience errors when extracting and labeling a sufficient number of malicious synchrophasors from historical operational data for training.For most existing detectors,if they are directly trained with highly imbalanced datasets,their performances may severely deteriorate.In this paper,a novel type of malicious synchrophasor detector is developed based on a combinatorial use of data rebalancing,Bagging-based ensemble learning,and the widely recognized eXtreme Gradient Boosting(XGBoost)classifier.Experiments show that although fewer malicious instances are provided,the proposed detector is still capable of detecting malicious synchrophasors.

A review on synchrophasor communication system:communication technologies,standards and applications

The present-day power system is a complex network that caters to the demands of several applications with diverse energy requirements.Such a complex network is susceptible to faults caused due to several reasons such as the failure of the equipment,hostile weather conditions,etc.These faults if not detected in the real-time may lead to cascading failures resulting in a blackout.These blackouts have catastrophic consequences which result in a huge loss of resources.For example,a blackout in 2004 caused an economic loss of 10 billion U.S dollars as per the report of the Electricity Consumers Resource Council.Subsequent investigation of the blackout revealed that the catastrophe could have been prevented if there was an early warning system.Similar other blackouts across the globe forced the power system engineers to devise an effective solution for real-time monitoring and control of the power system.The consequence of these efforts is the wide area measurement system(WAMS).The WAMS consists of several sensors known as the phasor measurement units(PMUs)that collect the real information pertaining to the health of the power grid.This information in the form time synchronized voltage and current phasors is communicated to the central control center or the phasor data concentrator(PDC)where the data is analyzed for detection of power system anomalies.The communication of the synchrophasor data from each PMU to the PDC constitutes the synchrophasor communication system(SPCS).Thus,the SPCS can be considered as the edifice of the WAMS and its reliable operation is essential for the effective monitoring and control of the power system.This paper presents a comprehensive review of the various synchrophasor communication technologies,communication standards and applications.It also identifies the existing knowledge gaps and the scope for future research work.

Synchrophasor-based real-time state estimation and situational awareness system for power system operation

State estimation is a critical functionality of energy management system(EMS) to provide power system states in real-time operations. However, problems such as failure to converge, prone to failure during contingencies,and biased estimates while system is under stressed condition occur so that state estimation results may not be reliable.The unreliable results further impact downstream network and market applications, such as contingency analysis,voltage stability analysis, transient stability analysis, system alarming, and unit commitment. Thus, operators may lose the awareness of system condition in EMS. This paper proposes a fully independent and one-of-a-kind system by integrating linear state estimator into situational awareness applications based on real-time synchrophasor data. With guaranteed and accurate state estimation solution and advanced real-time data analytic and monitoring functionalities, the system is capable of assisting operators to assess and diagnose current system conditions for proactive and necessary corrective actions. The architecture, building components, and implementation of the proposed system are explored in detail. Two case studies with simulated data from the subsystems of Electric Reliability Council of Texas(ERCOT) and Los Angeles Department of Water and Power(LADWP) are presented. The test results show the effectiveness and reliability of the system, and its value for realtime power system operations.

Attack Detection for Spoofed Synchrophasor Measurements Using Segmentation Network

Synchrophasor measurements are essential to realtime situational awareness of the smart grid but vulnerable to cyber-attacks during the process of transmission and invocation.To ensure data security and mitigate the impact of spoofed synchrophasor measurements,this work proposes a novel object detection method using a Weight-based One-dimensional Convolutional Segmentation Network(WOCSN)with the ability of attack behavior identification and time localization.In WOCSN,automatic data feature extraction can be achieved by onedimensional convolution from the input signal,thereby reducing the impact of handcrafted features.A weight loss function is designed to distribute the contribution for normal and attack signals.Then,attack time is located via the proposed binary method based on pixel segmentation.Furthermore,the actual synchrophasor data collected from four locations are used for the performance evaluation of the WOCSN.Finally,combined with designed evaluation metrics,the time localization ability of WOCSN is validated in the scenarios of composite attacks with different spoofed intensities and time-sensitivities.

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。

基于基波同步相量的次/超同步振荡参数辨识探究:频谱特性和关键问题

大量新能源发电和电力电子设备引起的次同步振荡(SSO)严重影响了电力系统稳定性。基于广域测量系统(WAMS)和相量测量单元(PMU)提供的基波同步相量辨识次/超同步振荡参数具有广域同步和高刷新率的核心优势,可实现次/超同步振荡的动态在线监测。该文从基波同步相量在次/超同步振荡下的复数域频谱特性出发,综述并分析了目前已有次/超同步振荡参数辨识方法的参数辨识特性及关键问题。首先,根据同步相量频谱分析了频率辨识范围、各分量叠加耦合特性及相位翻转特性;其次,分析了参数辨识的关键问题在于振荡分量耦合关系、基波分量耦合关系的解耦问题及频谱泄漏问题,并围绕复数域频谱分析方法分析了在解决以上问题时的三个关键难点:针对次/超同步分量的耦合关系有效地辨识超同步分量的问题、不同同步相量采样频率下进行参数辨识的适应性问题,以及如何处理频谱泄漏从而缩短数据窗长的难题;对此,综述了相应解决方法并展望了未来可能的技术突破点;最后,通过仿真数据和实际PMU数据对比分析验证了该文观点。

基于局部检测信息的电力系统时间同步攻击定位方法

卫星时间同步攻击可造成电力系统设备时间紊乱,PMU相角量测错误,危及系统安全运行。鉴于基于线路两端PMU数据的攻击检测方法无法判断线路哪端节点受到攻击,提出了一种基于局部攻击检测信息及同步区域搜索聚合的卫星时间同步攻击定位方法。首先,结合同步攻击特性构建了电力系统等效图及邻接矩阵。其次,结合线路两端攻击检测信息,利用广度优先搜索获取报警线路分割形成的各内部同步的区域子系统。再次,根据子系统是否两两相连分为简单情形和复杂情形;针对复杂情形,结合相角差偏差估计与同步子系统聚合将其转化为简单情形。最后,基于正常节点数量占优假设定位受攻击节点。IEEE 39节点系统测试表明,该方法在不同攻击下可有效定位攻击,可区分受不同攻击的节点,对微小攻击的定位能力强。

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FIR-MSMA的配电网测频算法

针对配电网中含有谐波、带外干扰及其他严重干扰的同步相量测量问题,提出一种基于有限冲击响应滤波器和M类同步相量测量算法(finite impulse response-M synchrophasor measurement algorithm, FIR-MSMA)相结合的配电网测频算法。该方法利用最小二乘法设计出FIR-1滤波器提取低频信号,在此基础上利用设计的FIR-2滤波器实现频率测量的目的,并分析2种滤波器参数设定的原则。利用Matlab软件编制算法仿真程序,验证所提算法的动态测量性能。结果表明,相比于基于离散傅里叶变换的算法和基于滑动平均滤波器的算法,基于FIR-MSMA的算法具有更强的抗干扰能力和更高的测量精度。

基于同步相量数据的次同步振荡检测与模态参数辨识方法

新能源集中并网已引发了多起次同步振荡(sub-synchronousoscillation,SSO)事故,严重威胁电力系统安全稳定。为对SSO进行实时预警与分析,提出一种基于同步相量数据的SSO检测与模态参数辨识方法。首先,推导SSO与同步相量中SSO分量实部、虚部的对应关系,在此基础上,选取同步相量实部作为特征量;其后,分析了SSO振荡特性变化时,同步相量中SSO分量的表现形式,并引入变分模态分解(variationalmodedecomposition,VMD)与希尔伯特变换(Hilbert transform,HT)对同步相量中SSO分量进行模态参数辨识,进而根据对应关系实现SSO模态参数的准确辨识。最后,利用同步相量中SSO分量瞬时幅值的差分来检测SSO振荡特性是否变化,并通过合成信号、电磁暂态仿真数据与实测数据对所提方法的有效性进行验证,该方法的理论成果有望在未来为SSO的实时预警,辅助决策提供技术支撑。

Application of Hankel Dynamic Mode Decomposition for Wide Area Monitoring of Subsynchronous Resonance

In recent years,subsynchronous resonance(SSR)has frequently occurred in DFIG-connected series-compensated systems.For the analysis and prevention,it is of great importance to achieve wide area monitoring of the incident.This paper presents a Hankel dynamic mode decomposition(DMD)method to identify SSR parameters using synchrophasor data.The basic idea is to employ the DMD technique to explore the subspace of Hankel matrices constructed by synchrophasors.It is analytically demonstrated that the subspace of these Hankel matrices is a combination of fundamental and SSR modes.Therefore,the SSR parameters can be calculated once the modal parameter is extracted.Compared with the existing method,the presented work has better dynamic performances as it requires much less data.Thus,it is more suitable for practical cases in which the SSR characteristics are timevarying.The effectiveness and superiority of the proposed method have been verified by both simulations and field data.

基于RA-CNN和同步相量的风电场次/超同步振荡参数智能辨识方法

近年来风电并网比例大幅提高,由此引发的次/超同步振荡的发生概率也大大提高,严重威胁系统的安全稳定性。准确辨识次/超同步振荡参数是抑制振荡的基础,提出基于注意力机制的残差卷积神经网络的辨识方法。卷积神经网络的局部相关性和权值共享决定了其具有更强的特征学习和表达能力,通过结合注意力机制可以更准确地辨识振荡参数。同时,引入残差连接,用以解决深层卷积神经网络存在的梯度消失和网络退化问题。仿真结果表明:相较于传统方法,该方法不仅能在较短时间窗数据上完整地辨识次/超同步振荡的参数,且能规避传统方法因主观因素带来的辨识误差,降低振荡参数辨识的复杂度。

基于同步相量轨迹拟合的电力系统次同步/超同步振荡的实时参数辨识

基于同步相量数据的次同步振荡参数辨识可有效监测次同步振荡的动态过程。该文提出一种基于同步相量轨迹拟合的电力系统次同步/超同步振荡的实时参数辨识方法。通过求解超定非线性的同步相量轨迹拟合方程组,能准确得到频移基波、次同步和超同步分量的频率、幅值和相位。该方法利用各分量对应的同步相量正负频率部分耦合而成的椭圆轨迹特性,仅依据100ms的同步相量数据序列即可进行高实时性的参数辨识。所提算法相比现有算法的优势在于,一方面可辨识与次同步分量耦合的超同步分量参数;另一方面超短数据窗大幅提升了算法实时性,并克服了频谱分析法的频率分辨率受限问题。模拟同步相量测量终端(phasor measurement unit,PMU)数据和实际仿真数据的对比分析结果表明,所提方法可准确获取基波和次同步/超同步振荡参数,并有效实现次同步振荡的动态实时监测。