Synchrophasor Data Research Articles

Retracted: Synchrophasor data transfer tool IEEE C37.118.2—Implementation and study of measurement standard for a futuristic substation automation

Sasikala R, Meenakumari R. Synchrophasor data transfer tool IEEE C37.118.2–Implementation and study of measurement standard for a futuristic substation automation, International Journal of Communication Systems 2021, (https://doi.org/10.1002/dac.4759). The above article published online on 05 May 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the Editor and John Wiley and Sons Ltd. Following publication, it has come to our attention that the people named as the Guest Editors of the Special Issue were being impersonated by a fraudulent entity and the articles were not reviewed in line with the journal's peer review standards. The Editor has determined that the articles do not meet the required scholarly standards of the journal, and therefore has taken the decision to retract the article. We did not find any evidence of misconduct by the authors. The authors have been informed of the decision to retract.

Synchronized Waveforms – A Frontier of Data-Based Power System and Apparatus Monitoring, Protection, and Control

Voltage and current waveforms contain the most authentic and granular information on the behaviors of power systems. In recent years, it has become possible to synchronize waveform data measured from different locations. Thus large-scale coordinated analyses of multiple waveforms over a wide area are within our reach. This development could unleash a set of new concepts, strategies, and tools for monitoring, protecting, and controlling power systems and apparatuses. This paper presents an in-depth review and analysis of the advancements in synchronized waveform data, including measurement devices, data characteristics, use cases, and comparisons with synchrophasor data. Based on the findings, five strategies are proposed to discover and develop synchronized waveform based applications over multiple application areas. The paper also presents three complementary measurement platforms and two data screening algorithms for application implementation. It further discusses committee activities and standard developments useful to explore the full potential of the data.

Efficient Identification of Bus Split Events Using Synchrophasor Data

Accurate grid topology information is of paramount importance for routine power system operations, while the growing availability of synchrophasor data offers the opportunity to identify topology changes in real time. Identification of bus split events, where the substation becomes electrically disconnected, is becoming increasingly important for maintaining the security of power systems. This paper aims to provide an efficient modeling and monitoring framework for bus split events by using a concise bus-branch representation. The linear sensitivity analysis is first performed to quickly evaluate the grid-wide impact of such events. Furthermore, the synchrophasor data enabled identification problem is formulated by matching the changes in bus phase angles (and possibly line flows). To address the resultant bilinear multiplication involving the binary connectivity variables, the McCormick relaxation technique is leveraged to attain an equivalent mixed-integer linear program reformulation that is efficiently solvable. Numerical studies on the IEEE 14-bus and 300-bus systems demonstrate the validity and efficiency of the proposed identification algorithm towards real-time implementation.

Communication infrastructure for situational awareness enhancement in WAMS with optimal PMU placement

Real time monitoring and control of a modern power system has achieved significant development since the incorporation of the phasor measurement unit (PMU). Due to the time-synchronized capabilities, PMU has increased the situational awareness (SA) in a wide area measurement system (WAMS). Operator SA depends on the data pertaining to the real-time health of the grid. This is measured by PMUs and is accessible for data analytics at the data monitoring station referred to as the phasor data concentrator (PDC). Availability of the communication system and communication delay are two of the decisive factors governing the operator SA. This paper presents a pragmatic metric to assess the operator SA and ensure optimal locations for the placement of PMUs, PDC, and the underlying communication infrastructure to increase the efficacy of operator SA. The uses of digital elevation model (DEM) data of the surface topography to determine the optimal locations for the placement of the PMU, and the microwave technology for communicating synchrophasor data is another important contribution carried out in this paper. The practical power grid system of Bihar in India is considered as a case study, and extensive simulation results and analysis are presented for validating the proposed methodology.

Synchrophasor Data Driven Islanding Detection, Localization and Prediction for Microgrid Using Energy Operator

A new method is suggested for islanding detection in microgrids with huge penetration of non-dispatchable renewable energy sources. Time synchronised data is collected in the form of variables like voltage phasors, frequency and rate of change of frequency using phasor measurement units from various locations. The method then uses weighted difference principal component analysis for considering nonlinearities among variables and for data condensation. Multi-resolution Teager energy operator (MTEO) is employed to Q statistics obtained to detect islanding events. Further, to eliminate false alarms a difference of the obtained signal with its mean (MTEOD) is computed. When it is compared with a threshold which is derived based on upper control limit of Q, can successfully identify islanding events with no spurious detections. The method implemented in DIgSILENT/Power Factory and MATLAB achieved 100% accuracy, precision and reliability with zero non detection zone. Moreover, islanding location has been precisely identified to attain situational awareness of the microgrid. Immunity to noisy conditions in the microgrid has been demonstrated using a four-variable mathematical model. For a faster online prediction decision tree classifier is utilized. Real time performance and prediction capability was evaluated with decision tree technique which shows promising results.

Anomaly Detection, Localization and Classification Using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units (PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real-time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This article proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm. Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

Synchrophasor Data Under GPS Spoofing: Attack Detection and Mitigation Using Residuals

The installation of Phasor Measurement Units (PMUs) is a step towards achieving wide-area situational awareness for the power grid. PMUs utilize GPS signals for time synchronization and are vulnerable to GPS Spoofing Attacks (GSAs) as civilian GPS signals are unencrypted and have low signal power. We propose a novel Residual-based Spoofing Detection and Measurement Correction (RSDMC) algorithm for the power grid that detects GSAs and corrects spoofed PMU measurements. During one or multiple GSAs, RSDMC iteratively minimizes the residual norm to correct spoofed PMU measurements. We derive a necessary condition for our proposed algorithm to show that the residual norm increases during GSAs. We perform Monte Carlo simulations to verify our derived necessary condition. We further simulate the IEEE 14, IEEE 118, and Illinois 200 bus test cases to validate RSDMC against multiple GSAs.

A Synchrophasor Data Compression Technique With Iteration-Enhanced Phasor Principal Component Analysis

The phasor data were compressed as separated amplitudes and phases in previous synchrophasor data compression techniques. To utilize the spatial correlation and temporal continuity of synchrophasors for data compression, a phasor principal component analysis (PPCA) in the field of complex numbers is proposed to compress synchrophasors as a whole in this article. Then, an iterative phasor principal components selection method is proposed to achieve PPCA and ensure the accuracy of reconstructed data since the existing eigenvalue-based criteria are not suitable for data compressions. Moreover, the proposed PPCA is enhanced by an iteration-based process to reduce the computation of PPCA. Actual PMU data measured under both a low-frequency oscillation incident and a two-phase short circuit incident conditions are used to verify the performance of PPCA compared with a recent PCA-based compression method. The results demonstrate that PPCA achieves higher compression ratios with better accuracy of reconstructed data, significantly reduced computation, and better real-time performance under both conditions.

Real-Time Synchrophasor Data Anomaly Detection and Classification Using Isolation Forest, KMeans, and LoOP

Power grid operators assess situational awareness using time-tagged measurements from phasor measurement units (PMUs) placed at multiple locations in a network. However, synchrophasor measurements are prone to anomalies which may impact the performance of phasor based applications. Anomalies include any deviation from expected measurements resulting from power system events or bad data. Bad data include data errors or loss of information due to failures in supporting synchrophasor cyber infrastructure. It is necessary to flag bad data before utilizing for an application. This work proposes a tool for the detection and classification of anomalous data using an unsupervised stacked ensemble learning algorithm. The proposed synchrophasor anomaly detection and classification (SyADC) tool analyzes a selected window of data points using a combination of three unsupervised methods, namely: isolation forest, KMeans and LoOP. The method classifies the data as anomalies or normal data with more than 99% recall. The method also provides a probability of the data to be an event or bad data with more than 99% recall. Results for the IEEE 14 and 68 bus systems with synchrophasor data obtained using Real-Time Digital Simulator and data of industrial PMUs highlight the superiority of the algorithm to detect and classify anomalies.

Voltage State Estimation using a Power Network Model driven AutoEncoder Neural Network

Abstract This paper proposes a combined network model and synchrophasor data-based approach to formulate a non-linear state estimator for the reconstruction of the three-phase voltages of the nodes (buses) in an electric power network. The network model uses network topology with impartial admittance information to create a first estimate of any missing three-phase complex-valued synchrophasor voltage measurements. These first estimates along with synchrophasor measurements from other nodes in the network are used in a trained autoencoder neural network to further improve the three-phase voltage state estimation. Instead of standard voltage magnitude and voltage angle synchrophasor data, the proposed method uses real and imaginary parts of the three-phase voltages for state reconstruction to address discontinuities in voltage phase angle wrapping. The approach is illustrated on an example network and actual synchrophasor data measurements to validate performance. The illustrations show that the nonlinear state estimator that combines the network model and synchrophasor data-based approach can outperform a non-linear estimator that uses synchrophasor data only.

Review of Low-Rank Data-Driven Methods Applied to Synchrophasor Measurement

There is a growing acceptance of using synchrophasor data collected over large power systems in control centers to enhance the reliability of power system operations. The spatial and temporal nature of power system ambient and disturbance response allows the analysis of large amount of synchrophasor data by low-rank methods. This paper provides an overview of several applications of synchrophasor data utilizing the low-rank property. The tools to capitalize on the low-rank property include matrix completion methods, tensor analysis, adaptive filtering, and machine learning. The applications include missing data recovery, bad data correction, and disturbance recognition.

Real-Time Area Angle Monitoring Using Synchrophasors: A Practical Framework and Utility Deployment

This article develops a practical framework of Area Angle Monitoring (AAM) to monitor in real time the stress of bulk power transfer across an area of a power transmission system. Area angle is calculated from synchrophasor measurements in real time to provide alert to system operators if the area angle exceeds pre-defined thresholds. This article proposes a general method to identify the warning threshold of area angle and a simplified method to quickly update area angle thresholds under significant topology change. A mitigation strategy to relieve the area stress is also proposed. In order to handle the limited coverage of synchrophasor measurements, this article proposes a method to estimate phase angles for boundary buses without synchrophasor measurements, which extends the application scenario of AAM. AAM is verified for a power transmission area in the Western Electricity Coordinating Council system with both simulated data and synchrophasor measurements recorded from real events. A utility deployment for real-time application of AAM with livestream and recorded synchrophasor data is described.

Machine Committee Framework for Power Grid Disturbances Analysis Using Synchrophasors Data

Events detection is a key challenge in power grid frequency disturbances analysis. Accurate detection of events is crucial for situational awareness of the power system. In this paper, we study the problem of events detection in power grid frequency disturbance analysis using synchrophasors data streams. Current events detection approaches for power grid rely on individual detection algorithm. This study integrates some of the existing detection algorithms using the concept of machine committee to develop improved detection approaches for grid disturbance analysis. Specifically, we propose two algorithms—an Event Detection Machine Committee (EDMC) algorithm and a Change-Point Detection Machine Committee (CPDMC) algorithm. Both algorithms use parallel architecture to fuse detection knowledge of its individual methods to arrive at an overall output. The EDMC algorithm combines five individual event detection methods, while the CPDMC algorithm combines two change-point detection methods. Each method performs the detection task separately. The overall output of each algorithm is then computed using a voting strategy. The proposed algorithms are evaluated using three case studies of actual power grid disturbances. Compared with the individual results of the various detection methods, we found that the EDMC algorithm is a better fit for analyzing synchrophasors data; it improves the detection accuracy; and it is suitable for practical scenarios.

A novel method for locating the source of sustained oscillation in power system using synchrophasors data

Large interconnected power systems are usually subjected to natural oscillation (NO) and forced oscillation (FO). NO occurs due to system transient response and is characterized by several oscillation modes, while FO occurs due to external perturbations driving generation sources. Compared to NO, FO is considered a more severe threat to the safe and reliable operation of power systems. Therefore, it is important to locate the source of FO so corrective actions can be taken to ensure stable power system operation. In this paper, a novel approach based on two-step signal processing is proposed to characterize FO in terms of its frequency components, duration, nature, and the location of the source. Data recorded by the Phasor Measurement Units (PMUs) in a Wide Area Monitoring System (WAMS) is utilized for analysis. As PMU data usually contains white noise and appears as multi-frequency oscillatory signal, the first step is to de-noise the raw PMU data by decomposing it into a series of intrinsic mode functions (IMF) using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) technique. The most appropriate IMF containing the vital information is selected using the correlation technique. The second step involves various signal processing and statistical analysis tools such as segmented Power Spectrum Density (PSD), excess kurtosis, cross PSD etc. to achieve the desired objectives. The analysis performed on the simulated two-area four-machine system, reduced WECC-179 bus 29 machine system, and the real-time power system PMU data set from ISO New England, demonstrates the accuracy of the proposed method. The proposed approach is independent of complex network topologies and their characteristics, and is also robust against measurement noise usually contained in PMU data.

Novel phasor sequence‐based fault detection scheme for wide‐area backup protection of series‐compensated line

For series-compensated line, the distance relay finds its limitations during voltage inversion, current inversion, load encroachment, power swing and balanced fault during the power swing. These limitations can be overcome by wide-area backup protection schemes. This study proposes a novel fault detection technique utilising the synchrophasor data of voltage and current signals at system protection centre. The algorithm compares the phase angle difference between positive-sequence voltage phasor under system intact condition and real-time positive- or negative-sequence current phasor, which is computed from the synchronised data obtained from phasor data concentrator after identifying the possible critical buses to which the faulted line is connected. Different cases of fault type, distance and resistance have been considered to validate the proposed scheme with 3-generator, 9-bus western system coordinating council using MATLAB/Simulink platform. The results justify the suitability of the proposed scheme to be deployed for achieving more reliable performance.

저 차원 Hankel 구조를 이용한 PMU 데이터 복구에 관한 연구

Phasor Measurement Units (PMUs) provide synchronized phasor measurements at much higher sampling rate than that in the traditional Supervisory Control And Data Acquisition (SCADA) system. Several synchrophasor-based algorithms and techniques have been and continue to be developed for real-time operation applications such as state estimation, stability analysis, disturbance detection, dynamic security assessment etc. However, synchrophasor data quality limits the incorporation of synchrophasor-based applications into control room operations environment and processes. The goal of this project is to develop methods that can improve synchrophasor data quality by recovering missing data reliably and efficiently. Data recovery refers to methods that estimate the values of missing data in the synchrophasor streams. Recently, modeless missing data recovery methods have been developed, that exploit the low-rank property of the spatial-temporal synchrophasor data blocks. A spatial-temporal synchrophasor data block can be considered as a matrix that is constructed by the measurements sampled at consecutive time instants with each row denoting the measurement of one certain channel across time. By exploiting the low-rank property of synchrophasor data matrices, the missing data recovery can be formulated as a low-rank matrix completion problem. The low-rank matrix completion problem has been extensively studied in the past few years and several algorithms have been developed to recover a low-rank matrix from partial observations. In this study, synchrophasor data analysis has been combined with low-rank matrix completion theory to develop a missing synchrophasor data recovery technique and tool.

Cyber Spoofing Detection for Grid Distributed Synchrophasor Using Dynamic Dual-Kernel SVM

Cyber spoofing with distributed synchrophasor adversely affects the decision-making and situational awareness of the power grid. To detect the spoofing trail, this letter proposes a composite signature-based cyber spoofing detection methodology. The intrinsic principal modes are first extracted from the distributed synchrophasor data. Then, multiple signatures of different intrinsic model components are derived to quantify the spoofing. Thereafter, the dynamic dual-kernel support vector machine is proposed to identify cyber spoofing using multiple signatures. Multiple experimental results using six spoofing methods have verified the validity of the methodology.

A Real Time Event Detection, Classification and Localization Using Synchrophasor Data

With an increasing number of extreme events, grid components and complexity, more alarms are being observed in the power grid control centers. Operators in the control center need to monitor and analyze these alarms to take suitable control actions, if needed, to ensure the system's reliability, stability, security, and resiliency. Although existing alarm and event processing tools help in monitoring and decision making, synchrophasor data along with the topology and component location information can be used in detecting, classifying and locating the event, which is the focus of this work. Phasor Measurement Unit's (PMU's) data quality issue is also addressed before using data for event analysis. The developed algorithms include statistic, clustering, and Maximum Likelihood Criterion (MLE) based anomaly detection, Density-based spatial clustering of applications with noise (DBSCAN) for event detection and physics-based rule/ decision tree for event classification. Further, topology information, statistical techniques, and graph search algorithms are used for event localization. Developed algorithms have been validated with satisfactory results for IEEE 14 bus and 39 Bus as well as with real PMU data from the western US interconnection (WECC).

A Method Based on Linear Matrix Inequalities to Design a Wide-Area Damping Controller Resilient to Permanent Communication Failures

One of the main advantages of the wide-area measurement systems in the small-signal stability of the electric power systems is to use synchrophasor data from the phasor measurement units (PMUs) for the operation of a central controller, or wide-area damping controller (WADC), to improve the damping ratio of the low-frequency oscillation modes. However, cyber-attacks in the PMU measurements can lead to permanent communication failure of the WADC channels and, therefore, the dynamic performance of the power system can be damaged. Besides, the signals for this controller are from different locations and thus they present time delays that can also damage the power system dynamic performance. This article introduces a method based on linear matrix inequalities to design a WADC robust to power system operation uncertainties, time-delay uncertainties, and permanent communication failure of the WADC channels. The proposed method was applied and evaluated in the IEEE 68-bus system by modal analysis and time-domain nonlinear simulations.

Synchrophasor based fault location algorithm for three terminal homogeneous transmission lines

This paper presents a new fault location algorithm for three-terminal transmission lines, with the help of synchrophasor data. The algorithm utilizes the information of voltages and currents which are available from all the three ends. The proposed algorithm utilizes positive and negative sequence data for locating asymmetrical faults and only positive sequence data for locating symmetrical faults. The accuracy of the algorithm has been evaluated for various factors, such as fault inception angles, fault resistance, fault location, synchronization errors, measurement errors. The simulation of faults on the system have been carried out using PSCAD® and the algorithm is implemented in MATLAB software. Based on the simulation results it has been observed that the % error is less than 1.5% for 178 test cases out of 180, which shows 98.8% accuracy.