Synchrophasor Data Research Articles

Internal clock errors in synchrophasor ambient data: Effects, detection, and a posteriori estimation-based correction

Synchrophasor data from real world power systems are exposed to numerous adverse cyber-and-physical operating conditions that can negatively impact their fitness for use in PMU applications. Although the most significant impairments such as GPS loss, instrument transformer failure, data drop-off, etc., have been studied in the literature, there exist other more complex nuisances that impact PMU data quality. This is the case of differences in how clock synchronization, time disciplining, and phasor estimation is performed by different PMU vendors, which have important implications on the PMUs’ data fitness for use in PMU applications. In particular, ambient data applications, which have increasingly become a focus for tracking grid performance indicators, are extremely sensitive to periodic clock errors. Using synchrophasor and waveform data from Dominion Energy’s power system, this article provides an in-depth analysis of the effect of seemingly minor device clock errors on the measurement signals’ content. Finally, a method to distinguish clock errors from normal system dynamics and to correct them is proposed.

Mitigation of the commutation failure problem in the HVDC multi-infeed scenario in Brazil using synchronized phasor measurement

This paper presents the Commutation Failure (CF) problem in the High Voltage Direct Current - Line-Commutated Converter (HVDC-LCC) scenario in Brazil through the monitoring of Short-Circuit Ratio (SCR), CF indexes, Direct Current (DC) link power, and the use of simulated synchrophasor data. The analysis was performed by training a neural network using data from the year 2020, and its robustness was verified using data from the year 2021 of the Brazilian Interconnected Power System (BIPS). Additionally, an analysis of transient instability in the North-South Transmission Corridor (NSC) will be presented right after the occurrence of CF by monitoring the Lyapunov transient instability margin, with the primary objective of bringing subsidies to the control center operation area according to the severity of the CF occurrence, preventing its propagation and mitigating it through real-time predictive actions, such as power redispatch on DC links and, thus, minimizing the occurrence of CF.

Federated Synchrophasor Data Prediction, Aggregation and Inference Using Deep Learning: A Case of Proactive Control for Short-Term Stability

Federated Synchrophasor Data Prediction, Aggregation and Inference Using Deep Learning: A Case of Proactive Control for Short-Term Stability

Robust Three-Stage Dynamic Mode Decomposition for Analysis of Power System Oscillations

A spatio-temporal method based on Robust Three-stage Dynamic Mode Decomposition (RTDMD) is proposed to improve the modal parameter estimation of synchrophasor data in the presence of noise, missing data and outliers. The proposed method is based on the PMU correlated spatio-temporal data to estimate missing information using an iterative singular value decomposition (SVD) combined with the adjustment of the reduced Koopman operator. Moreover, it is proposed to identify and correct outliers using an inherent statistical extension of the SVD-based Mahalanobis distance. As a result of the proposed enhancements, modal parameters such as damping and power system oscillation frequency are extracted as a byproduct of the RTDMD algorithm process. Data resulting from real-time digital simulators (RTDS) with self-clearing three-phase fault and Phasor Measurement Units (PMUs) of Low-Frequency Oscillation (LFO) incidents at hydroelectric power plants are used to illustrate the effectiveness of the proposed method over other well-established methods.

Transmission line switching spread modelling of synchrophasor smart grid network with load uncertainty inspired by epidemic model: a study of reliability and vulnerability

ABSTRACT Monitoring and controlling of transmission lines become easier in real time with synchrophasor measurements technology. Phasor Measurement Units (PMUs) provide the time-stamped synchrophasor data of the system buses and transmission lines with a high reporting rate. In this paper, a mathematical model is implemented that allows the simulation of the characteristics of the spread of transmission line switching with load variations. In the model, the transmission lines are divided into three parts defined as susceptible, infected and recovered transmission lines inspired from epidemic model. In this paper, Reliability Test System (RTS − 1979) is divided into three zones to simulate the line switching spread behaviour with load variation for reliability and vulnerability analysis of the synchrophasor smart grid network. The anomaly exists with synchrophasor measuring devices due to cyber intrusion in communication network or failure of devices makes the system observable with the characterization of vulnerable behaviour of transmission lines.

Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery

Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. This paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.

Low-frequency oscillations analysis and big data

The increased observability of processes in electric power systems, provided by the widespread introduction of synchrophasor measurement technology, as well as the use of computational processing of measurement data arrays, has led to changes in the methodology for analyzing the power system operating modes. Along with the development of new approaches to modeling processes and received measurement signals, a class of data-based methods has emerged. In a number of recent studies, the analysis of synchrophasor measurement data at the large power system scale is considered as a big data problem. Despite the controversial nature of this point of view, the application of some big data methods in synchrophasor data analysis yields positive results. In the proposed report, an assessment of the volumes of initial and intermediate data in the analysis of forced low-frequency oscillations is performed. The effect of data explosion at intermediate stages of calculations is shown. The storage and transmission of these data can be replaced by their calculation in the presence of a storable and transmittable configuration of a generalized computational scheme. A method for compact visualization of an array of oscillation dynamic parameters is shown by the example of dissipating energy. In addition, an approach is presented for configuring streams of synthetic test data at the power system scale, which provides a desired result in the analysis of dissipative energy.

Experimental End-To-End Delay Analysis of LTE Cat-M With High-Rate Synchrophasor Communications

Micro-Phasor Measurement Units (μ-PMUs) are devices that permit monitoring voltage and current in the distribution grid with high accuracy, thus enabling a wide range of smart grid applications, such as state estimation, protection and control. These devices need to transmit the synchronous measurements of voltage and current, also known as synchrophasors, to the power utility control center at a high rate. The use of wireless networks, such as LTE, to transmit synchrophasor data is becoming increasingly popular. However, synchrophasors are included in small frames and it would be more efficient to use low-power cellular solutions, such as LTE cat-M. In this work, we present experimental research on the deployment of a μ-PMU with the ability to connect over a commercial LTE cat-M network. The deployed μ-PMU is built with off-the-shelf hardware, such as Arduino microcontrollers, and is used to transmit data—compliant with the IEEE C37.118.2 standard—at a variable rate from 1 frame/s to 80 frames/s. A detailed network performance analysis is carried out to show the suitability of LTE cat-M to support μ-PMU communications. Experimental results on performance indicators, such as delay and jitter, are reported. The effect of the LTE cat-M access mechanism on the time distribution of frame arrivals is also thoroughly analyzed.

Development and implementation of a MATLAB-based phasor data concentrator for synchrophasor applications

This work presents the development of MatPDC, an IEEE Std. C37.118.2-2011 compliant Phasor Data Concentrator (PDC) implemented in the Matlab environment. MatPDC enables the integration of wide-area monitoring, protection and control system synchrophasor data into the Matlab platform, offering real-time access to synchronized data streams from multiple Phasor Measurement Units (PMUs). The key features of MatPDC include real-time data access, data integrity verification, data aggregation and alignment, compliant data communication, and latency calculation. These features facilitate efficient processing and analysis of synchronized data, reducing complexity and improving latency between data sources and applications. The development of MatPDC addresses the need for accessing and analyzing real-time synchrophasor data within the Matlab environment, providing researchers and engineers with a powerful tool for power system analysis and experimentation. By developing the PDC and synchrophasor applications on the same platform, MatPDC reduces complexity and latency between data sources and applications. It offers real-time access to synchronized data streams, ensuring data integrity through verification mechanisms and aggregating the data into a unified dataset based on time tags. The implementation of MatPDC opens up opportunities for researchers to work with real-time synchrophasor data. It facilitates the development of advanced algorithms, real-time simulations, and the verification of control strategies. The integration and evaluation of MatPDC demonstrate its effectiveness and potential in power system analysis, providing researchers and engineers with a valuable tool for their research and development activities.

Fault Detection Technique for Distribution Networks and Microgrids Using Synchrophasor Data

Fault Detection Technique for Distribution Networks and Microgrids Using Synchrophasor Data

Разработка метода идентификации синхронного сопротивления турбогенератора и критериев выбора состава данных синхронизированных векторных измерений

To enhance power system dependability and efficiency, it is of great importance to obtain correct parameters of the models of key power system components such as a synchronous generator (SG). Technology of synchronized phasor measurements allows us to estimate the SG model parameters through measurements in power system steady states and transients states, without switching the SG off and conducting complex testing procedures. The theory of electric circuits and electric machines, theory of optimization and statistics, and matrix algebra are applied in this study. The developed parameter estimation method has been implemented in the MATLAB software. The method has been verified through real-world measurements captured by a phasor measurement unit installed at a large power plant in the United Power System. This paper presents an algorithm of the turbogenerator synchronous reactance estimation via a set of synchrophasor data taken under steady-state conditions. Also, the paper considers the criteria for an appropriate data array ensuring successful parameter estimation. An algorithm has been developed and implemented in MATLAB software package. To improve the parameter estimation accuracy, an algorithm has been proposed to modify a nominal value for the SG no-load excitation current depending on the heating of the field winding. The algorithm has been successfully tested out based on a set of steady state synchrophasor data captured for one of the turbogenerators in the Russian United Power System. The authors have developed and analyzed quantitative criteria to determine a set of measured parameters of steady-state modes, providing the most accurate results of the turbogenerator synchronous reactance. The developed algorithm allows accurate update of the turbogenerator synchronous parameters through synchrophasor measurements recorded in a steady state modes of electric power systems. Using the criteria for selecting phasor data, it seems possible to automate the process of generating a data array of measurement of various steady-state modes necessary for identification. The proposed procedure that considers the field winding temperature can be employed in math models to estimate SG transient and subtransient parameters based on phasor measurements in transient modes of electric power systems.

A Blockchain-Enabled Approach for Enhancing Synchrophasor Measurement in Smart Grid 3.0

Smart Grid 3.0 is the latest evolution of the smart grid and incorporates advanced computing and communication technologies. The synchrophasor communication system plays a critical role in wide-area measurement systems (WAMS) for real-time protection and control of power systems, supporting the objectives of Smart Grid 3.0. This system relies on synchrophasor communication technologies, where Phasor Measurement Units (PMUs) transmit synchrophasor data to Phasor Data Concentrators (PDCs) over the synchrophasor communication network. The communication infrastructure of this network is based on the TCP/IP protocol stack, which, unfortunately, is susceptible to cyberattacks, posing security threats such as data tampering and false data injection. These vulnerabilities undermine the intended benefits of synchrophasor applications in terms of situational awareness, observability, grid reliability, resiliency, and synchronized monitoring and control in the smart grid. To address these challenges, it is crucial to enhance the security, integrity, and confidentiality of synchrophasor data within the communication system. This paper proposes a blockchain-based synchrophasor communication system that preserves the security and integrity of synchrophasor data. In this paper, an architecture is proposed for a synchrophasor communication system based on blockchain technology. The proposed architecture aims to enhance the security and integrity of synchrophasor measurements. Furthermore, the architecture is developed as a peer-to-peer distributed blockchain network, leveraging the robustness of a distributed, decentralized, hierarchical PDC architecture. To evaluate the efficacy of the proposed architecture, two case studies, one using the IEEE 9 bus and the other using IEEE 14 bus systems are considered. Moreover, various challenges with potential solutions are also recommended. The proposed work is envisioned to contribute to the advancement of Smart Grid 3.0 by adopting blockchain technology for synchrophasor applications.

Ambient oscillatory mode assessment in power system using an advanced signal processing method

Estimation of electromechanical mode properties is crucial in modern power systems for providing the operators with an adequate indication of the stress in the system. Measurement-based approaches use signal processing algorithms for mode identification and parameter estimation. This paper presents a novel framework for the assessment of low-frequency oscillation modes using real-world synchrophasor data with minimum computational effort. A nonstationary approach known as Time-Varying Filter based Empirical Mode Decomposition (TVF-EMD) technique is used to identify the dominant low-frequency modes present in the ambient PMU data. The combination of TVF-EMD with Teager Kaiser Energy Operator (TKEO) precisely estimates the instantaneous mode parameters, such as frequency, amplitude, and damping ratio. The efficacy of the proposed approach is demonstrated by applying it in a synthetic signal, simulated data of a standard IEEE test system, and in real-world PMU data of the Indian power grid. The proposed method is compared with the existing methodologies and the observations reveal that the proposed method has robust performance in estimating the instantaneous mode features in the power system with less computational complexities.

Synchrophasor network-based detection and classification of power system events: A singular value decomposition approach

Timely detection and classification of power system events are essential for situation awareness and reliable electricity grid operation. It is also a crucial step with regard to synchrophasor network data management and archiving. In this paper, an event detection and classification method based on the singular value decomposition (SVD) of synchrophasor data is proposed. The detection algorithm exploits the low-dimensionality characteristics of synchrophasor data and identifies the changes in the dimensionality of a sliding data matrix. The SVD-based method assigns several detection flags indicating events and outliers in voltage magnitude, phase angle and frequency data. The proposed classification algorithm comprises a decision tree employing detection flags and singular values to classify events into several categories, e.g., fault, voltage magnitude and phase angle events, and generation-load mismatch events. Moreover, the proposed algorithm identifies whether events are spatially correlated. Field synchrophasor data collected from a smart grid are used to evaluate the performance of the proposed method. The numerical results show that the proposed method can successfully detect and classify different types of events even in the presence of measurement uncertainty.

Use of synchronized vector measurements technology for perform relay protection functions

RELEVANCE. Recently, along with the initial scope of synchronized vector measurements (synchrophasor) technology associated with Wide Area Measurement System (WAMS), more and more attention is paid to expanding the scope of this technology. Digital substations are one of the places for generating synchrophasor signals, at the same time, synchrophasor data are proposed to be used for the purposes of relay protection and automation at the digital substations itself, which will facilitate the construction of centralized relay protection systems at the substation. THE PURPOSE. Consider the use of synchrophasor technologies at a digital substation of the fourth type, as a replacement for Sampled Values (SV) signals. Consider additions to the standard set of synchrophasor signals necessary for the operation of various relay protections at a digital substation. Perform an analysis of the accuracy of calculating the synchrophasor when the frequency of the controlled current and voltage changes relative to the nominal value, when higher harmonics appear in the measured signal. Develop a pre-computation technique for relay protection algorithms. METHODS. When solving the problem, the method of calculating the total measurement error of the synchrophasor TVE (Total Vector Error) was used - a value that characterizes the deviation of the amplitude and phase of the measured vector from their specified values. CONCLUSION. The use of synchrophasor signals with the necessary additions for the operation of relay protection reduces the load on the digital network of the digital substation, simplifies the operation of relay protection algorithms. Calculations have shown that the measurement errors of synchrophasor do not go beyond the specified parameters. The proposed methods of precomputation allow to speed up the work of relay protection algorithms.

A Graph-Theoretic Approach for Modelling and Resiliency Analysis of Synchrophasor Communication Networks

In recent years, the Smart Grid (SG) has been conceptualized as a burgeoning technology for improvising power systems. The core of the communication infrastructure in SGs is the Synchrophasor Communication Network (SCN). Using the SCN, synchrophasor data communication is facilitated between the Phasor Measurement Unit (PMU) and Phasor Data Concentrator (PDC). However, the SCN is subjected to many challenges. As a result, the components, such as the links, PMUs, PDCs, nodes, etc., of the SCN are subjected to failure. Such failure affects the operation of the SCN and results in the performance degradation of the SG. The performance degradation of the smart grid is observed either temporarily or permanently due to packet loss. To avoid dire consequences, such as a power blackout, the SCN must be resilient to such failures. This paper presents a novel analytical method for the resiliency analysis of SCNs. A graph-theoretic approach was used to model SCN from the resiliency analysis perspective. Furthermore, we proposed a simulation framework for validating the analytical method using the Network Simulator-3 (ns-3) software. The proposed non-intrusive simulation framework can also be extended to design and analyse the resiliency of generic communication networks.

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 time-varying. The effectiveness and superiority of the proposed method have been verified by both simulations and field data.

Synchrophasor Data Based Q-V Droop Control of Wind Farm Integrated Power Systems

With increasing scalability of grid-integrated wind farms (WFs), reactive power ancillary service through Q-V droop control of doubly-fed inducton generators (DFIGs) is one of the prime interests of power system operators. However, variable and uncertain availability of wind-resource at the wind turbines creates challenge against allocating reactive power reserve at the WFs for maintaining voltage stability of the grid. This issue can be resolved through online coordination among the WFs. This paper proposes a synchrophasor data based Q-V droop (SQVD) control technique for the WFs integrated to the grid through different point of common couplings (PCCs) in the power system transmission network. The data from the phasor measurement units (PMUs) at the WF-connected PCCs are used to obtain synchronized Q-V droop gain for each WF. Further, distributed coordination among DFIGs within each WF is performed to allocate optimum reactive power share at the converters. The total reserve of each WF is employed by their synchronized droop, in improving voltage stability at both the local PCC and neighboring PCCs. Performance of SQVD is verified at 418-bus practical Indian power system, in different dynamic situations of the grid, and found to be more effective as compared to the constant Q-V droop and variable Q-V droop control methods.

Real-time Synchrophasor Data Compression Technique with Phasor Interpolation and Extrapolation

Real-time Synchrophasor Data Compression Technique with Phasor Interpolation and Extrapolation

Data compression for advanced monitoring infrastructure information in power systems based on tensor decomposition

In this paper, an innovative data compression methodology based on Tucker tensor decomposition is presented. The proposed approach exploits the benefits of organizing time series in multidimensional arrays (tensors) in order to achieve higher compression ratios of the original data while preserving most of their properties and, in this form, achieving a low reconstruction error. This reorganization of the data into the tensor allows the creation of correlation for heterogeneous data. Furthermore, it reduces the direct relationship between the number of columns and rows with the final size of the compressed file when the data is compressed using algorithms based on matrix-based dimensional reduction techniques, such as the Singular Value Decomposition (SVD). At the same time, the problem of missing data can be overcome since the problem is formulated as an iterative process. To demonstrate the effectiveness of the innovative algorithm proposed, two study cases from electrical networks are presented. First, the compression of real synchrophasor data acquired from one commercial Phasor Measurement Unit (PMU) measuring 3-phase voltages and frequency measurements from the electrical system of Continental Europe is presented, where a significant CR is achieved for apparently no correlated data. Then, an application to real Smart Meter measurements from the electrical utility ERCOT in the USA is also provided. Here the clustering of the data is obtained as an additional outcome of the proposed algorithm. To underline the benefits of the innovative proposed methodology in contrast to other more common decompositions such as SVD, the compression ratios and reconstruction error achieved using these two different techniques are compared. The results indicate that using Tucker decomposition, not only higher compression can be achieved, but also it is demonstrated that for SM data, additional attributes can be obtained, such as clustering.