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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2021

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify power events. The proposed method fully builds on and is also tested on a large real-world dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. We report the numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

With the increased requirements of real-time grid monitoring, disturbance location, and situation awareness, Phasor Measurement Units (PMUs) have become more critical for the Wide Area Measurement System (WAMS). However, the vulnerability of PMUs has not been well studied, especially under electromagnetic pulse (EMP) scenarios. The stable operation of the power system will be affected directly once EMP damages them. Therefore, studying their immunity to EMP events is urgent and necessary. In this paper, the effective impedance measurement scheme and pulsed current injection (PCI) testing are proposed for the port impedance measurement and immunity levels of PMUs. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then, the circuit of the pulsed current generator is set to generate a damping sinusoid, and the double exponential wave is applied to the port. Finally, using measured impedance as a generator load, the voltage and current responses of different ports are calculated in the PCI testing simulation. In conclusion, results reveal the characteristics of port impedance, waveforms of voltage and current, and distribution of accumulative energy. The relation between port impedance and the waveforms is discussed.

42 ENGINEERING↗
DOE OSTI2020

Detection of Synchrophasor False Data Injection Attack using Feature Interactive Network

The synchrophasor data recorded by Phasor Measurement Units (PMUs) plays an increasingly critical role in the regulation and situational awareness of power systems. However, the widely installed PMUs are vulnerable to multiple malicious attacks from cyber hackers during data transmission and storage. To address this problem, a Modified Ensemble Empirical Mode Decomposition (MEEMD) is proposed first to extract the intrinsic mode functions of each Synchrophasor Data Attacks (SDA). The frequency-based adaptive screening criterion embedded in MEEMD is used to eliminate the false intrinsic mode functions. Next, a Multivariate Convolutional Neural Network (MCNN) is proposed to identify multiple SDA by utilizing the extracted intrinsic mode functions and original SDA as input vectors. A fusion block as the main structure of MCNN is also leveraged to increase the diversity of features and compress the model parameters. Integrating MEEMD and MCNN, a framework with automatic feature extraction and multi-source information fusion capability, referred to as Feature Interactive Network (FIN), is proposed to detect multiple SDA. Based on the proposed FIN framework, six types of SDA are explored for the first time using actual synchrophasor data in FNET/Grideye that was collected from different locations in the U.S. Eastern Interconnection. Finally, a large quantity of experiments with different attack strengths are used to evaluate the adaptability and classification performance of the proposed FIN.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Hybrid Data-Driven Based HVdc Ancillary Control for Multiple Frequency Data Attacks

The high voltage direct current (HVdc) intertie has been applied to provide ancillary-services for ac grids, utilizing the real-time feedback from phasor measurement units (PMUs). However, PMU data communication is vulnerable to false data injection attacks (FDIA) due to protocol defects, thus the HVdc ancillary control and system stability will be threatened. To address this issue, this article proposes a novel HVdc control strategy based on a hybrid data-driven (HDD) methodology. In this work, the HDD methodology is first proposed to detect the types and duration time of multiple frequency attacks. Specifically, the Hilbert Huang transform (HHT) is used to decompose the frequency data, using variational mode decomposition instead of the traditional empirical mode decomposition, to extract data features. Second, a multikernel support vector machine is proposed to classify the attacked data based on the designed distinctive features from HHT. Meanwhile, the attacking duration time is decided using an unsupervised technique. Third, an HDD-based HVdc ancillary control strategy is established to eliminate the effect of FDIAs on the HVdc frequency response. Comprehensive experiments of HDD-based HVdc ancillary controls under different FDIAs suggest that the proposed HDD could fast and accurately classify the FDIAs, and the HDD-based HVdc ancillary control strategy could significantly suppress the impact of the FDIAs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

MindSynchro

This report presents the developments and results of MindSynchro project as part of DOE OE FOA 1861. DOE and Pacific Northwest National Laboratory (PNNL) have made available to FOA awardees datasets containing years of real historical data recorded from various phasor measurement units (PMUs) which are installed in three large US interconnections: Texas (IC A), Western (IC B), and Eastern (IC C). The main goal of the project, which was successfully achieved, was to develop methods for detection and identification of events which are relevant for power grid operation. Tasks performed for achieving the project goals included data exploration and pre-processing, the development and application of physics-based features, data analysis and labeling based on unsupervised learning approaches, training and testing of DSSL models for classification of events which are relevant for power grid operation, and deployment of solutions to cloud environments. The methods developed in the project can potentially provide relevant benefits to power grid asset owners/operators in general in terms of situational awareness. Two main types of outcomes can be provided by these tools: Identification of specific relevant power grid event types: Semi-supervised ML methods developed in the project can adequately employ not only the relatively scarce labeled data but also the large amount of available unlabeled data to train models for detection of specific event types. Such methods enable the application of trained models for the detection of events in a population of PMUs much larger than that associated to the labeled events. Support in data labeling / label validation: Labels are critical for training of models for identification of specific types of events. However, labeling large amounts of data is a manual and tedious process. This means that such process is error prone and is not scalable. Methods developed in the project, based on ensembles of clustering models, have been successfully employed for turning manual labeling into a scalable process. Accurate identification of specific relevant events can provide the operators with immediate situational awareness that could otherwise require hours or days of analysis from domain experts. We envision that such methods could be initially employed in support of post-mortem analysis of events and, as confidence is gained, they could be employed for online/real-time support, providing, among other benefits, insights for avoiding major events which could happen due to a combination of smaller ones. On the longer term, related methods could potentially be employed to improve protection and control.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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 OSTI2020

Impacts of swinging door lossy compression of synchrophasor data

As the number of operational Phasor Measurement Units (PMUs) is increasing, compression is becoming crucial for efficiently transferring and storing enormous amount of synchrophasor data continuously measured from a power system. Some utilities in the U.S. have already adopted a nonlinear compression technique, called Swinging Door Algorithm (SDA), assuming compressed data meets technical and regulatory requirements without resulting in any degradation of the measurements. This paper studies impacts of this compression algorithm on field measured and simulated PMU data, and presents results from spectral analyses of raw and decompressed measurements. The results show blind application of the SDA compression alters frequency content in the measurements and introduces false harmonics and aliasing. These can lead to false conclusions being drawn from the measurements about the power system condition.

42 ENGINEERING↗
DOE OSTI2025

Analytical Identification Method of Generalized Short‐Circuit Ratio Using Phasor Measurement Units

This paper introduces a novel analytical approach for the identification of the admittance matrix and the generalized short-circuit ratio (gSCR) in power systems integrated with renewable energy sources. The proposed method leverages voltage and current measurements from phasor measurement units (PMUs) to construct a least squares objective function, which is then solved using matrix calculus and partial derivatives. Unlike conventional optimization algorithms, this approach provides an analytical solution that substantially reduces data requirements, enabling the efficient and accurate identification of the gSCR with smaller datasets. Additionally, its fixed computational complexity allows for real-time updates as new data are collected, ensuring continuous refinement of the system of equations and enabling rapid, precise gSCR calculations. The method also exhibits strong robustness against measurement noise, making it well-suited for practical applications in dynamic power systems. The combination of reduced data requirements, real-time adaptability, noise robustness and fixed computational load establishes this method as a highly efficient and reliable tool for real-time power system stability analysis. Case studies on an EPRI 36-bus system demonstrate the method's effectiveness, highlighting its accuracy in closely matching true gSCR values, even under diverse disturbances and noisy conditions.

Han, Zelei [Hohai University, Nanjing (China)] (OR↗
DOE OSTI2020

Low cost, flexible, and distribution level universal grid analyser platform: designs and implementations

This study presents the designs and implementations of a distribution level open-universal grid analyser (Open-UGA) platform. The proposed Open-UGA platform consists of distribution-level phasor measurement units (PMUs), a standard signal generator, a router, and a server. Firstly, an overall introduction for the software, hardware, and server architectures of the Open-UGA platform is given. To give a detailed design, the software, hardware, server block diagrams, flowcharts, and printed circuit board photo of the Open-UGA platform are presented in detail. Then, four different types of distribution level PMU algorithms are introduced and implemented in the Open-UGA platform to verify the flexibility and reconfigurability. The flowcharts and functionalities of these four UGAs with different PMU algorithms are given as example implementations. Lastly, a performance comparison is conducted with both quantitative and illustrative results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Voltage and Current Response Characteristics of PMU Device by PCI Simulation

Phasor Measurement Units (PMUs) are critical for the Wide Area Measurement System (WAMS), especially for real-time grid monitoring, disturbance location, and situation awareness. Once they are damaged, the stable operation of power system will be affected directly and greatly. As new electronic devices in power system, their immunity to electromagnetic pulse (EMP) events needs to be studied. In this paper, an effective impedance measurement scheme is proposed for port impedance measurement of PMU. A non-uniform transmission line model is established to eliminate the impact of fixture in the de-embedding process. Then circuit of pulse current generator is established to generate damping sinusoid and double exponential wave applied to the port. Finally, by using measured impedance as load of the generator, the voltage and current responses of different ports are calculated in the pulsed current injection (PCI) simulation. Results shows the characteristics of port impedance, voltage and current waveforms. The relation between port impedance and the waveforms is discussed.

Zhang, Liang↗
DOE OSTI2022

Immunity Study: Port Impedance Measurement of PMU and PCI testing under EMP

Phasor Measurement Units (PMUs) are critical for the Wide Area Measurement System (WAMS), especially for real-time grid monitoring, disturbance location, and situation awareness. Once they are damaged, the stable operation of the power system will be affected directly and greatly. Therefore, it is urgent and necessary to study their immunity to electromagnetic pulse (EMP) events. In this paper, an effective impedance measurement scheme is proposed for port impedance measurement of PMU. The equivalent non-uniform transmission line model is established to eliminate the impact of the fixture in the de-embedding process. Then circuit of pulsed current generator is established to generate damping sinusoid and double exponential wave applied to the port. Finally, by using measured impedance as a load of the generator, the voltage and current responses of different ports are calculated in the pulsed current injection (PCI) testing simulation. Results show that common-mode impedance is large and differential mode impedance is small. The critical condition will appear in the common mode for voltage and differential mode for current.

Zhang, Liang↗
DOE OSTI2022

An Alternative Timing and Synchronization Approach for Situational Awareness and Predictive Analytics

Accurate and synchronized timing information is required by power system operators for controlling the grid infrastructure (relays, Phasor Measurement Units (PMUs), etc.) and determining asset positions. Satellite-based global positioning system (GPS) is the primary source of timing information. However, GPS disruptions today (both intentional and unintentional) can significantly compromise the reliability and security of our electric grids. A robust alternate source for accurate timing is critical to serve both as a deterrent against malicious attacks and as a redundant system in enhancing the resilience against extreme events that could disrupt the GPS network. To achieve this, we rely on the highly accurate, terrestrial atomic clock-based network for alternative timing and synchronization. In this paper, we discuss an experimental setup for an alternative timing approach. The data obtained from this experimental setup is continuously monitored and analyzed using various time deviation metrics. We also use these metrics to compute deviations of our clock with respect to the National Institute of Standards and Technologys (NIST) GPS data. The results obtained from these metric computations are elaborately discussed. Finally, we discuss the integration of the procedures involved, like real-time data ingestion, metric computation, and result visualization, in a novel microservices-based architecture for situational awareness.

Chinthavali, Supriya↗
DOE OSTI2021

PMU-data-driven Event Classification in Power Transmission Grids

This paper presents an event classification in transmission grids. The convolutional neural network (CNN)-based classifier is proposed to capture the temporal similarity of time-synchronized data stream from phasor measurement units (PMUs). The proposed CNN is trained using Bayesian optimization to search for the best hyperparameters. The effectiveness of the proposed event classification is validated through the real-world dataset from the U.S. transmission grids. This dataset includes line outage, transformer outage, frequency, and oscillation events. The validation process also includes different PMU outputs, such as voltage magnitude, phase angle, current magnitude, frequency, and rate of change of frequency (ROCOF). The results show that ROCOF gives the best classification performance compared to other PMU outputs. In addition, it is shown that the classifier trained with a larger dataset has higher accuracy. Moreover, the superiority of the proposed method is validated through comparison with other state-of-the-art classification methods.

Niazazari, Iman↗
DOE OSTI2023

Proactive Frequency Stability Scheme via Bayesian Filters and Synchrophasors

Underfrequency (UF) load shedding schemes are traditionally implemented in two ways: One approach is based on manual load shedding, with system operators requesting loads to be shed ahead of anticipated stressful operating conditions. Manual load shedding is usually done through phone calls. The second method is automatic load shedding via underfrequency relays. Using static static settings, these schemes can be designed to operate in stages and drop previously identified loads. The main limitation of traditional load shedding schemes is that they are reactive and leave little room for optimized corrective actions. This work presents a proactive and automatic underfrequency load shedding solution for power systems. Measurements are captured via phasor measurement units (PMUs) at relatively low sampling rates of 30 Hz. These measurements are then processed by particle filters who predict the future state of the system's frequency. Based on these predictions excess load is determined and shed. Comparative case studies are performed in simulated environments. Easy-to-implement models, without hard-to-derive parameters, highlight potential aspects for real-life implementation.

Paramo, Gian↗
DOE OSTI2020

A Step Towards Machine Learning-based Coherent Generator Grouping for Emergency Control Applications in Modern Power Grid

A dynamic neural network (NN) based multi-class classifier is proposed for improving online prediction of coherent generator groups (CGGs), following the occurrences of various contingencies in the power grid. This is motivated by the increasing availability of the measurements from phasor measurement units (PMUs) and the number of grouping schemes is limited. The proposed method consists of three steps. First, by performing offline simulations, a library of system dynamic responses characterized by post-contingency rotor angles and speeds of individual generators is obtained. To generate sufficient data, up to N-2 contingencies and the uncertain parameters associated with the power grid including type and location of disturbance and fault clearing times are modeled. Secondly, the training data-set is produced by generating labels for individual contingencies using a hierarchical clustering method based on rotor angle and speed data. Lastly, the dynamic NN models are trained for online applications such as emergency controls and controlled islanding. The proposed method is tested on the standard 16-generator 68-bus system to demonstrate its performance. Furthermore, the impact of the sample data lengths on the CGG numbers is evaluated. It is interesting to observe that the time domain stability behaviors can be determined by examining the changes in the CGG numbers.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

State Estimation for Distribution Networks with Asynchronous Sensors Using Stochastic Descent: Preprint

This paper investigates the problem of state estimation for distribution networks with asynchronous sensors comprising of a mix of smart meters and phasor measurement units (PMUs) with multiple sampling and reporting rates. We consider two independent scenarios of state estimation and tracking, with either voltages or currents as states. With these two sets, we investigate estimation under (a) full data, assuming all measurements are available and (b) limited data, where an online algorithmic approach is adopted to estimate the possibly time-varying states by processing measurements as and when available. The proposed algorithm, inspired by the classical Stochastic Gradient Descent (SGD) approach updates the states based on the previous estimate and the newly available measurements. Finally, we demonstrate the estimation and tracking efficacy through numerical simulations on the IEEE-37 test network, while also highlighting how estimation with currents as states leads to faster convergence.

asynchronous sensors↗
DOE OSTI2022

Real-time Event Detection Using Rank Signatures of Real-world PMU Data

Timely detection of power system events is a crucial task, which can facilitate the implementation of remedial actions to improve reliability, resiliency, and security of the system. Meanwhile, the widespread deployment of phasor measurement units (PMUs) makes it possible to develop data-driven event detection techniques. However, relying purely on data without incorporating domain knowledge for the event detection task in power systems poses substantial security and stability risks due to issues associated with data misinterpretation and model accuracy. In this regard, we propose a real-time event detection method using real-world PMU data by incorporating domain knowledge to adequately capture the event signatures. Specifically, we track the change in rank signatures of PMU data to accurately localize the events. To optimize the detection process, we incorporate an offline Bayesian optimization algorithm to tune the parameters by efficiently searching for the best values. The experiments using the real-world PMU dataset from a U.S. interconnection show that the proposed event detection approach can efficiently detect the events from PMU data streams with high accuracy.

Ghasemkhani, Amir↗