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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Using Synchrophasor Status Word as Data Quality Indicator: What to Expect in the Field?

Data quality plays a crucial role in successful applications of synchrophasor data in power system operation and control. This paper presents the results of a data quality analysis of a multi-year field-recorded synchrophasor dataset. The analysis has identified several typical data quality issues encountered in the field data. An examination of the PMU status words included with the dataset has revealed several inconsistent implementations and the lack of correlation between the PMU data quality and the status word, which impacts the usefulness of such information. Our investigation has concluded that the status word alone as found in the recorded field dataset could not be used as a reliable indicator of data quality for field-recorded data. Several recommendations are proposed to improve the usefulness of the PMU status word.

Cheng, Zheyuan↗
DOE OSTI2021

Online Detection of Low-Quality Synchrophasor Data Considering Frequency Similarity

Here, this letter proposes a new approach for online detection of low-quality synchrophasor data under both normal and event conditions. The proposed approach utilizes the features of synchrophasor data in time and frequency domains to distinguish multiple regional PMU signals and detect low-quality synchrophasor data. It is more effective to detect low-quality data with apparently indistinguishable profiles. Case studies from recorded synchrophasor measurements verify the effectiveness of the proposed approach for detecting low-quality synchrophasor data in frequency, voltage magnitude and voltage angle.

42 ENGINEERING↗
DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

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. Here, 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.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Cyber-Attack Identification of Synchrophasor Data Via VMD and Multifusion SVM

A large amount of synchrophasor data in the wide area measurement system (WAMS) needs to be collected and transmitted to the phasor data concentrator, thereby increasing the possibility of being attacked by hackers. The attacked data are therefore hidden into the normal synchrophasor data so that the synchrophasor data based application will be affected. To remedy this problem, an identification framework is proposed to detect the data cyber-attack in WAMS utilizing variational mode decomposition (VMD) and multifusion support vector machine (MSVM). First, VMD is used to transform the attacked data into multiple modal components. Thereafter, a novel MSVM is employed to classify the deterministic features using the proposed linear combined multikernel (LCM). Further, this LCM can fuse multiple types of features, including the time, frequency, and statistical domains of the synchrophasor data. Utilizing the actual data from FNET/GridEye, different experiments are conducted under multiple attack strengths and types. The results demonstrate that the identification framework has higher precision and robustness compared with other conventional classifiers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluating cyber-risk in synchrophasor systems

Technology related to evaluating cyber-risk for synchrophasor systems is disclosed. In one example of the disclosed technology, a method includes generating an event tree model of a timing-attack on a synchrophasor system architecture. The event tree model can be based on locations and types of timing-attacks, an attack likelihood, vulnerabilities and detectability along a scenario path, and consequences of the timing-attack. A cyber-risk score of the synchrophasor system architecture can be determined using the event tree model. The synchrophasor system architecture can be adapted in response to the cyber-risk score.

Pal, Seemita↗
DOE OSTI2024

Data Security Defense: Modeling and Detection of Synchrophasor Data Spoofing Attack for Grid Edge

Data security and cyberattack have become critical issues in the distributed power system where adversaries can swap the source information of sensors or even spoof and alter measurements. However, the cyber security of the power system is challenged by the unpredictability and stealth of the spoofing attacks. Here, to protect the data security at the grid edge, this paper developed a synchrophasor data spoofing attack detection framework based on the time-frequency feature extraction techniques including the short-time Fourier transform (STFT) and object detection network for real-time synchrophasor data categorization and spoofing attack localization. The proposed approach outperforms earlier work in terms of spoofing attack detection and offers a vital localization function employing distributed synchrophasor sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Source Authentication of Distribution Synchrophasors for Cybersecurity of Microgrids

This letter proposes a hybrid approach combining Self-Adaptive Mathematical Morphology (SAMM) and Time-Frequency (TF) techniques to authenticate the source information of Distribution Synchrophasors (DS) within near-range locations. The SAMM can adaptively regulate the synchrophasors variations which are representatives of local environmental characteristics. Subsequently, TF mapping is employed to extract informative signatures from the regulated synchrophasors variation. Finally, Random Forest Classification (RFC) is used to correlate the extracted signatures with the source information based on the derived TF mapping. Experiment results using DS collected at multiple small geographical scales validated the proposed methodology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Automated System-wide Event Detection and Classification Using Machine Learning on Synchrophasor Data

As the number of phasor measurement units (PMUs) deployed in a power system increases, and their data volume streamed to the control canter intensifies, operators are facing challenges related to the analysis of such data, which need to be observed and responded to as the measurements are displayed in the Control Room. Humans are generally unable to process such large amount of data efficiently and rapidly. There is an apparent need for automated ways to analyze the data, extract actionable information about occurrence of specific events, and characterize the events quickly and cost effectively. This paper discusses the use of machine learning (ML) to facilitate such tasks by providing automated, highly computationally efficient, and cost-effective ways of extracting actionable information from synchrophasor big data in real-time. We developed Big Data Smart (BDSmart) ML-based prototype tool for the Control Room use that automatically analyses data properties from synchrophasor system measurements taken across the three grid Interconnections in the USA (Western, Eastern and ERCOT). The data collected from several hundreds of PMUs located across the Interconnections over a period of two years have been made available for our extensive study. As a result, we were able to identify a number of big data properties that influence how ML methodology is applied to select, develop, train and test the data models that can eventually be used for the tool implementation. The resulting set of candidate algorithms spans unsupervised, supervised, semi-supervised and transfer-learning approaches. Many ML techniques, such as decision trees, multinomial logistic regression, feed-forward neural networks, K-nearest neighbor, multiclass support vector machine, and single and multi-channel convolutional neural networks, are implemented, and their performance is examined. We offer the results from testing the data models. The novelty of our study is in the approaches for bad data detection and mitigation, selection of a simplified feature for event detection, and data label improvements. As a result, we came up with a list of recommendations for the utilities on how to improve the PMU recording practices to cater to the future ML applications aimed at automating the analysis of synchrophasor data.

Synchrophasors, Machine Learning, System-wide Even↗
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
DOE OSTI2023

Transformer Health Monitoring via Synchrophasors

Traditional transformer protection schemes such as differential protection operate once the transformer is in a critical state. Proactive means of transformer health evaluation such as Sweep Frequency Response Analysis, can only be performed periodically with the transformer out-of-service. This work presents a framework where the health and structural integrity of transformers is monitored via synchrophasors in real-time. Synchrophasors and subspace estimation techniques are used to build reduced-order models which are then analyzed across multiple domains. A diagnostics algorithm is developed and tested via Matlab simulations. Test results are promising, opening up new prospects in transformer protection research.

cyber-physical security↗
DOE OSTI2022

Power System Event Identification with Transfer Learning Using Large-scale Real-world Synchrophasor Data in the United States

The lack of sufficient labeled events and long training time limit the applicability of deep neural network-based power system event identification using synchrophasor data. In this paper, we propose to leverage transfer learning technique to boost the reliability and reduce the required training time of neural classifier for power system event identification. We use the weights of a neural classifier trained on one transmission system as the initial parameters of another neural classifier for a different transmission system. Numerical tests with real-world synchrophasor data from the Eastern and Western Interconnections of the United States show that the proposed transfer learning approach is very effective in not only improving the training reliability but also reducing the training time.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Cyber-Power Co-Simulation for End-to-End Synchrophasor Network Analysis and Applications

The resiliency, reliability and security of the next generation cyber-power smart grid depend upon efficiently leveraging advanced communication and computing technologies. Also, developing real-time data-driven applications is critical to enable wide-area monitoring and control of the cyber-power grid given high-resolution data from Phasor Measurement Units (PMUs). North American Synchrophasor Initiative Network (NASPlnet) provides guidance for PMU data exchanges. With the advancement in networking and grid operation, it is necessary to evaluate the performance of different data flow architectures suggested by NASPInet and analyze the impact on applications. Therefore, we need a cyber-power co-simulation framework that supports very large-scale co-simulation capable of running in parallel, high-performance computing platforms and capturing real-life network behavior. This work presents an end-to-end automated and user-driven cyber-power co-simulation using NS3 to model communication networks, GridPACK to model the power grid, and HELICS as a co-simulation engine. Comparative analysis of latency in synchrophasor networks and a performance evaluation of a power system stabilizer application utilizing PMU data in an IEEE 39 bus test system is presented using this cosimulation testbed.

Mustafa, Hussain M.↗
DOE OSTI2024

Synchrophasor Data Anomaly Detection on Grid Edge by 5G Communication and Adjacent Compute

The fifth-generation mobile communication (5G) technology offers the opportunities to enhance the grid real-time monitoring. The 5G-enabled phasor measurement units (PMUs) features flexible positioning and cost-effective long-term maintenance, without constraints of fixing wire. This paper is the first to demonstrate the applicability of 5G in PMU communication, and the experiment was carried out at Verizon non-standalone testbed at Pacific Northwest National Laboratory (PNNL) Advanced Wireless Communication lab. The performance of 5G-enabled PMU communication setup is reviewed and discussed in this paper, and the paper presents a real-time dynamic linear model (DML) based synchrophasor data anomaly detection application. Last but not least, the practicability of implementing 5G for wide-area protection strategies is explored and discussed by analyzing the experimental results.

5G, Synchrophasor data, machine learning, anomaly ↗
DOE OSTI2022

Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART)

This report contains key findings from a project titled Big Data Synchrophasor Monitoring and Analytics for Resiliency Tracking (BDSMART), which was carried out through a collaborative effort of a team of researchers from Texas A&M Engineering Experiment Station, Temple University, and Quanta Technology, LLC. The in-kind support came from OSIsoft (acquired by AVEVA), which provided their PI Historian software to demonstrate the use case of streaming PMU data. The first section of the report describes the project goals and objectives related to the development of Machine Learning (ML) models capable of detecting and classifying events by processing phasor measurements captured in the field by Phasor Measurement Units (PMUs). The data for this study was contributed by the utilities/ISOs from the Western and Eastern interconnects and ERCOT, further referred to as Interconnect B (IC B), Interconnect A (IC A), and Interconnect C (IC C), respectively. The approach that the BDSMART Research Team proposed and the key research tasks defined by the team are outlined in this section. The next section describes the technical approach. We first discuss the data constraints related to the PMU measurements and data interpretation constraints imposed by the data contributors. They provided neither the topological information of the grid nor PMU placement locations and captured recorded data at very few locations in the system with the reporting rate of either 30 or 60 fps. The recordings are mostly positive sequence voltage, frequency, and ROCOF, and in some limited cases, three-phase voltages and currents. We then reflect on the bad data issues that stem from poor recording practices and vague definitions of the PMU status bits to supposedly be used for bad data identification. Finally, the data discovery points to imprecise time stamps with incomplete event start/end time, as well as inconsistent and incomplete event labeling, which combined make the implementation of the data models using supervising learning quite challenging. Following the data discovery study, we hypothesize that because the IC B data has the most complete labels, we should focus our model development on that data and then test it on data from other interconnects. We also define the common metrics used to evaluate the results from the ML algorithm tests. We concluded this section by summarizing the common ML models we used and explaining how we implemented and tested them. The issues from this section are expanded in the Training Dataset Report from this project. The final section of this report deals with the accomplishments and conclusions. As the accomplishments, we formulate the problem we are solving and what is achieved by solving the problem. We then reflect on each of the analytics tools we developed and point out the performance of each tool when applied to solving the mentioned problems. We reference this work for further details to the papers we published on each tool. In the conclusions, we give recommendations on how to improve future PMU recording practices to facilitate the ML algorithm implementation and guidance for the future standardization work aimed at clarifying the ambiguities associated with the PMU status bits. We finally list future tasks that can bring about further improvements in the proposed algorithms. The issues from this section are expanded in the Training, and Test Dataset Report filed at the project completion date.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Secure Time Synchronization in Power Grids and Network HIL Synchrophasor Testing

Reliable and secure time synchronization underpins the monitoring and control functions of modern power grids. As GPS-based timing infrastructures grow more susceptible to spoofing and jamming, their vulnerabilities pose escalating risks to grid stability. This work investigates a secure, resilient timing framework that can serve as an alternative or redundant source for power grids, with a particular focus on synchrophasorbased applications. A candidate timing system architecture is evaluated to guarantee trustworthy time dissemination, even in degraded conditions. A network hardware-in-the-loop testing of two synchrophasors validates the concepts, demonstrating enhanced timing integrity, improved detection of timing anomalies, and sustained observability during adverse timing events.

Wu, Ori [ORNL] (ORCID:0000000326723410)↗
DOE OSTI2021

Multifractal Characterization of Distribution Synchrophasors for Cybersecurity Defense of Smart Grids

“Source ID Mix” spoofing emerged as a new type of cyber-attack on Distribution Synchrophasors (DS) where adversaries have the capability to swap the source information of DS without changing the measurement values. Accurate detection of such a highly-deceptive attack is a challenging task especially when the spoofing attack happens on short fragments of DS recorded within a relatively small geographical scale. Herein this letter proposes an effective approach to detect this cyber-attack by realizing the multifractal characteristics of DS measurements. First, the multifractal cross-correlation of DS measured at multiple intra-state locations is revealed. Then the derived correlation is integrated with weighted two-dimensional multifractal surface interpolation to reconstruct quasi high-resolution signals. Finally, informative location-specific signatures are extracted from the high-resolution DS and they are integrated with advanced machine learning techniques for source authentication. Experiments using the real-life DS are performed to verify the proposed method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Combinatorial Evaluation of Physical Feature Engineering, Classical Machine Learning, and Deep Learning Models for Synchrophasor Data at Scale

A major objective of the project was to train and evaluate the effectiveness of multiple event and anomaly detection, identification and classification deep temporal learning models for processing of real-time phasor measurement unit (PMU) data streams. A vast dataset, consisting of two years of phasor measurements from all three U.S. Interconnections, was curated and released by the Department of Energy (DOE) through Pacific Northwest National Laboratory (PNNL). The dataset also included an event log that provided event times and types (e.g. generator trips, line trips, planned service events, transformer operations, etc.). Our analysis of this dataset addressed six (6) of the eleven (11) research priorities identified in Funding Opportunity Announcement (FOA) DE-FOA-0001861 “Big Data Analysis of Synchrophasor Data” (FOA 1861). Rather than being limited to pre-determined specific algorithms, this project relied on the uniquely structured, highly performant underlying time series database capabilities of the PredictiveGrid platform to assess the vast dataset utilizing a wide variety of algorithms.

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