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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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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Evaluating a Commercial Dynamic Line Rating Software with the National PMU Dataset

To accelerate the development of data-driven applications for power systems, the Department of Energy (DOE) supported the collection and curation of a synchrophasor dataset spanning two years of observations from transmission utilities across the US. This National PMU Dataset (NPDS) was anonymized and distributed to awardees of a DOE research grant under nondisclosure agreements (NDAs) but has also been retained at PNNL to enable further research. Agreements with data contributors prevent the data from being shared outside the organization. However, establishing a blind research validation methodology is envisioned to maximize the value proposition of the NPDS. In this validation strategy, researchers may share algorithms/software (potentially as executables to protect intellectual property) with PNNL, and PNNL will share feedback about the software’s performance on subsets of the NPDS. Such a blind methodology ensures that sensitive information about critical infrastructure remains protected, but the value of the NPDS can be extended to research beyond PNNL. Through iterative feedback, the algorithms may be tweaked to address real-world artifacts. As the NPDS data is temporally and geographically diverse, it may capture features absent in smaller datasets used during the development of the algorithm under test. This report presents lessons learned from applying the blind validation methodology to LineID™, a synchrophasor-based dynamic line rating software developed by Topolonet Corporation. Improvements made to the software through iterative feedback, limitations of the validation methodology, as well as how the limitations of the NPDS affected the evaluation process are discussed. Observations indicate that the proposed validation methodology can be valuable for evaluating other tools in the future.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Timestamp Error Detection and Estimation for PMU Data based on Linear Correlation between Relative Phase Angle and Frequency

Time synchronization is essential to synchro-phasor-based applications. However, Timestamp Error (TE) in synchrophasor data can result in application failures. This paper proposes a method for TE detection based on the linear correlation between frequency and relative phase angle. The TE converts the short-term relative phase angle from noise-like signal to one that linear with the frequency. Pearson Correlation Coefficient (PCC) is applied to measure the linear correlation and then detect the timestamp error. The time error is estimated based on the variation of frequency and relative phase angle. Case studies with actual synchrophasor data demonstrate the effectiveness of TE detection and excellent accuracy of TE estimation.

Yu, Wenpeng↗
DOE OSTI2021

Estimation of Electromechanical Modes of Oscillation in the Eastern Interconnection from Ambient PMU Data

A power system's inter-area electromechanical modes of oscillation determine if the system is small-signal stable. Mode meters are tools that estimate the modes based on ambient synchrophasor measurements, allowing continuous monitoring of the system's stability margin. This paper reports on the application of mode meter algorithms to 21 months of synchrophasor measurements from the United States Eastern Interconnection (EI). Estimates for three modes were updated every minute, providing a much more comprehensive set of results than previous studies based on disturbance recordings. The detailed signal designs and mode meter configurations provided in this paper can be used to accelerate mode meter deployments in the EI. Analysis of the mode estimates reveal for the first time how seasonal load changes and system reconfiguration impact the EI system's dynamics.

Follum, James D.↗
DOE OSTI2020

Attack-resilient algorithms and testbed federation for wide-area protection and control in smart grid

Today’s energy infrastructure is undergoing a massive transformation across all generation, transmission, and distribution systems to provide reliability, efficiency, and sustainability to the power system network. During the past 10 years, there has been a remarkable enhancement in developing close-loop wide-area protection and control (WAPAC) applications, such as automatic generation control (AGC), wide-area protection scheme (WAPS), synchrophasor-based wide-area voltage control system (WAVCS), etc., in the energy management system (EMS) to provide an adequate situational awareness to control center operators during physical disturbances and natural events. The rapid digitalization, unencrypted communication, and numerous data-sharing devices have increased digital access points that have exposed the grid network to countless cyberattacks pertinent to the WAPAC cybersecurity. Given the amount of complex grid network coupled with legacy infrastructures, it is untrustworthy to completely rely on information technology (IT)-based conventional security measures to defend against evolving cyberattacks. Hence, there is a compelling need to go beyond the traditional paradigm of ‘security by obscurity’ and ‘bolt-on’ security measures, and develop a suite of innovative security solutions at multiple layers to transform the current “fault-resilient” grid into “fault and attack-resilient” grid of the future. This dissertation specifically focuses on developing application-specific attack-resilient solutions for critical WAPAC applications in the smart grid. The first part of the study, Attack-Resilient Wide-Area Protection Scheme, presents a cyber-physical attack-resilient system (CPARS) for WAPS, where two anomaly detection methods are proposed to detect multi-level cyberattacks followed by the rules-based intrusion mitigation system (RIMS) to initiate effective mitigation strategies, tailored to these attacks, to restore the normal grid operation after disturbances. The first anomaly detection component, Machine learning-based Anomaly Detection for Centralized WAPS, discusses a novel architecture and methodology for developing a machine-learning-based anomaly detection system (ADS) for the centralized wide-area protection system (CWAPS) by applying the variational mode decomposition (VMD) technique and decision tree (DT) algorithms using cyber logs and synchrophasor measurements. The second component, Multi-Agent-based Anomaly Detection for Decentralized WAPS, presents a two-level hierarchical multi-agent-based decentralized WAPS against the system-aware stealthy cyber-attacks. Finally, for the RIMS, the state transition diagram-based mitigation measures are defined based on the grid operation states and experimental testing and evaluation are presented in a cyber-physical testbed environment. The second part of the study, Attack-Resilient Wide-Area Voltage Control System, addresses the existing vulnerabilities in the wide-area voltage control system (WAVCS), presents the methodology for performing an impact analysis during data integrity attacks, and proposes a data-driven attackresilient system (DARS) by incorporating machine learning-based ADS and rules-based anomaly mitigation system (RAMS) to detect these attacks and provide necessary mitigation actions to maintain the transient voltage stability after disturbances. Finally, the third part, Networked Federation Testbed (NEFTSec) for Smart Grid Cybersecurity, focuses on developing an interconnected federated testbed through a common network like the internet, describes its conceptual architectures, and also presents its design components for experimental testing, validation, and evaluation. Further, it presents the co-simulation interface algorithm (CIA) to facilitate the geographically-dispersed real-time distributed simulation (GD-RTDS) during the cyber-physical federation. In general, this dissertation presents innovative attack-resilient solutions for WAPAC applications that are experimentally tested and evaluated using the PowerCyber testbed and NEFTSec platform to accelerate the transition of state-of-the-art research works to the real-field deployments.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Rapid Monitoring and Defense Approach for Resilience Improvement of Grid Cyber Security

Cyber-physical systems and electric utilities significantly depend on the reliability and efficiency of information and operational technology. However, false data injection attacks based on synchrophasor measurement data pose a serious threat to the safe and reliable operation of modern power systems. Here, to mitigate this problem, a rapid monitoring and defense approach is proposed to defend against cyber attacks. Initially, the Time and Frequency based Convolutional neural Network (TFCN) is proposed to detect different types of attacks. Within the TFCN, the advances are that both time and frequency domain information can be fused without extra spectrum analysis methods, and can save detection time to speed the calculation efficiency using the developed time-frequency block. Next, a comprehensive defense strategy is developed for multiple cyber attacks to ensure the stability and resilience of the power system according to the feedback detection results. The advances of this strategy are that different control strategies can be automatically selected to recover the stability to the greatest extent according to the detected attacks. To verify the effectiveness of the proposed approach, the high-speed frequency measurements collected from the wide-area monitoring system are used. The results demonstrate that the cyber attack detection performance can reach 95.57% accuracy, outperforming both traditional and some advanced neural networks. Importantly, the defense strategy is conducted and verified in a modified IEEE 39 bus system as well, which illustrates profound performance in faster stability restoration.

Comprehensive defense strategy↗
DOE OSTI2022

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. In conclusion, case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

frequency disturbance recorder (FDR)↗
DOE OSTI2021

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. Here, we have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Furthermore, our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2021

Hardware-In-the-Loop Benchmarking Setup for Phasor Based Control Validation

Phasor Based Control is a novel approach to controlling Distributed Energy Resources that aims at relieving various constraints that arise in the distribution grid. It is a two-layer control system with a supervisory control that coordinates distributed controllers to reach voltage phasor targets. The distributed controllers use local synchrophasor measurements and operate as feedback controllers. This control method is currently under development with several algorithms being under consideration for both the central and distributed components. In this report, we present the experimental setup that was prepared to prototype a hardware implementation and validate the control method in Hardware-In-the-Loop.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

A Statistics-Based Threshold for the RMS-Energy Oscillation Detector

System operators and reliability coordinators currently rely on extensive baselining studies to set thresholds for their oscillation detectors. The resulting thresholds are based largely on engineering judgment and may vary significantly between organizations. In this paper, statistical distribution theory is used to derive a detection threshold for the widely deployed root-mean-square (RMS)-energy detector. This expression provides a theoretical basis for the detector's configuration, simplifies the process of selecting the threshold, and enables improved consistency among organizations that need to coordinate during system-wide events. Three methods for calculating the threshold using synchrophasor measurements are also proposed. These methods ensure that the threshold can be calculated reliably for various applications. Tests with simulated and field-measured data demonstrate that the statistics-based threshold provides consistent detection of grid disturbances while maintaining a low probability of false alarm.

Follum, James D.↗
DOE OSTI2021

Time-frequency based cyber security defense of wide-area control system for fast frequency reserve

Global power systems are transiting from conventional fossil fuel energy to renewable energies due to their environmental benefits. The increasing penetration of renewable energies presents challenges for power system operation. The efficiency and sufficiency of responsive reserves have become increasingly important for power systems with a high proportion of renewable energies. The Fast Frequency Reserve (FFR), especially the Wide-area Monitoring System (WAMS)-based FFR, is a promising and effective solution to secure and enhance the stability of power systems. However, cyber security has become a new challenge for the WAMS-based FFR system. Cyber attacks on the FFR control system may threaten the safety of power system operation due to the rapid power controllability requirement of FFR. Therefore, to address this problem, a time-frequency based cyber security defense framework is proposed to detect the cyber spoofing of synchrophasor data in WAMS-based FFR control systems. This paper first introduces the Continuous Wavelet Transforms (CWTs) to decompose spoofing signals. Then, the Dual-frequency Scale Convolutional Neural Networks (DSCNN) is proposed to identify the time-frequency domains matrix from two frequency scales. Integrating CWTs and DSCNN, an identification framework called CWTs-DSCNN is further proposed to detect the spoofing attacks in the WAMS-based FFR system. Multiple experiments using the actual data from FNET/GridEye are performed to verify the effectiveness of the framework in securing WAMS-based FFR systems.

25 ENERGY STORAGE↗
DOE OSTI2020

Cyber‐physical system testbed for power system monitoring and wide‐area control verification

The electric power system is intrinsically a cyber‐physical system (CPS) with power flowing inthe physical system and information flowing in the cyber‐network. Testbeds arecrucial for understanding the cyber‐physical interactions and provideenvironments for prototyping novel applications. This study proposes afour‐layer architecture for CPS testbeds with emphases on communication networkemulation and networked physical components. A configurable software‐definednetwork is employed to bridge physical components with wide‐area applicationsfor closed‐loop control. In order to distribute physically coupled devices intomultiple software simulations, this study proposes a data broker setup based ona distributed messaging environment to achieve low‐latency data streaming. Thedecoupled design with data streaming allows for building testbed components asmodules and running them in a distributed manner. Case studies verify the databroker setup for low‐latency sensing and actuation, as well as the communicationemulation setup for the desired network latency. Also illustrated is a replayattack scenario using synchrophasors in the Western Electricity CoordinatingCouncil (WECC) 181‐bus system for demonstrating the closed‐loop cyber‐physicalsimulation capability of the testbed.

Cui, Hantao↗
DOE OSTI2025

Characterizing the Oscillatory Properties of Bulk Electric Systems

This paper presents a process for characterizing the oscillatory dynamics of a large bulk power system. As a demonstration, the process is applied to the Western Interconnection of North America. Several complementary analysis approaches, both new and existing, are employed to provide a comprehensive understanding of the oscillatory properties of the system. Established modal analysis techniques based on ringdown and mode-meter algorithms are utilized. In addition, we derive and apply methods based on spectral correlation analysis to identify modal frequencies, distinguish between modes that are closely spaced in frequency, and determine locations at which the modes are observable. Critical interarea modes are identified and characterized using actual-system synchrophasor measurements taken over several years of operation in concert with industry-standard simulation models. This includes 145 hours of PMU data and two planning base cases.

42 ENGINEERING↗
DOE OSTI2020

Application of Chebyshev’s Inequality in Online Anomaly Detection Driven by Streaming PMU Data

The day-to-day operation of modern power systems is highly reliant on prompt and adequate situational-awareness. This can be achieved via various system monitoring functions such as anomaly detection, in which static thresholds are commonly utilized to distinguish the normal and the abnormal system states. However, a predetermined static threshold usually lacks the flexibility to adapt to unobserved scenarios. In this paper, we propose two self-adaptive synchrophasor data driven anomaly detection approaches based on Chebyshev’s Inequality. The proposed approaches have been evaluated with Kundur’s 2area system and Mini-WECC system. Experimental results verify that the proposed approaches can dynamically adapt to unprecedented scenarios, and detect anomalous events with lower false alarm rate compared to static threshold based detection.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Remote Hardware-in-the-Loop Approach for Microgrid Controller Evaluation

Utilities have been installing microgrids because of the increased resilience and reliability advantages they may provide to the distribution system. A microgrid controller is a critical component in microgrids. It is of great benefit to derisk the installation of microgrid controllers before field deployment. Hardware-in-the-loop (HIL) testing is used by controller developers and utilities to evaluate the controllers under stressful conditions. In this work, a microgrid control function developed by the Synchrophasor Grid Monitoring and Automation (SyGMA) laboratory at the University of California, San Diego is tested in a remote HIL (RHIL) setup. The digital real-time simulation of the detailed microgrid system was operated at the National Renewable Energy Laboratory's Energy Systems Integration Facility. Under such RHIL setup, successful controller operation is contingent on understanding and characterizing the communications channel and in particular network latencies. The novelty of this paper is the proposed use of a RHIL setup that leverages existing power system communications protocols to evaluate the controller in conjunction with the simulation capabilities of a remote facility. The work presented here will provide the complete setup of the HIL evaluation platform, the details of the communications protocols used by the setup for data transfer between the two organizations, test cases developed to evaluate the controller, and the results from the experiments.

controller hardware-in-the-loop↗
DOE OSTI2022

Dynamic Performance Comparison and Prediction based on Distribution-level Phasor Measurement Units

This paper introduces a new distribution level Phasor Measurement Unit (PMU) which adopts advanced hardware components and structure. The hardware parameters from the new PMU and the existing PMU are used to build a simulation model to predict the PMU performance. Therefore, a real-world testbench is built and four distribution level PMUs are tested under the steady-state and dynamic tests. The quantitative experiment result confirms the prediction model which could guide future PMU design, and also verifies the accuracy of the new PMU on the synchrophasor and frequency measurements in multiple scenarios.

Wu, Yuru↗
DOE OSTI2024

Open Source Synergy: Developing and Validating PMU Data Analysis Techniques Using Open Source Tools and Datasets

This paper presents an exploration into the development and validation of data analysis approaches for Phasor Measurement Units (PMUs) using open-source datasets and tools. Various methods for event detection, event classification, frequency response, and oscillation analysis were tested. We leverage the capabilities of Archive Walker (AW), the Frequency Response Analysis Tool (FRAT), and the Oscillation Baselining and Analysis Tool (OBAT), all open-source tools, for efficient processing and analysis of synchrophasor data. The open-source Transmission Signature Library (TSL) dataset was employed as a dataset for a comprehensive evaluation to assess the performance and reliability of the proposed methods.

PMU, event analysis, oscillation, Frequency Respon↗
DOE OSTI2026

Development and Evaluation of a Cost-Effective Behind-the-Meter Synchronized Measurement Unit for Enhanced Grid Integration

This paper presents the development of the Inverter Based Resource Monitor (IBRM), an innovative behind-the-meter synchronized measurement unit (SMU) tailored for integration with inverter-based resources (IBRs). The IBRM distinguishes itself as a highly accurate and cost-effective SMU, offering facile deployment and connectivity to IBRs. It is equipped to conduct real-time voltage and current waveform analyses, serving as a phasor measurement unit (PMU) with exceptionally rapid synchrophasor transmission capabilities. The device incorporates a cutting-edge dual-core architecture designed to minimize sampling delays inherent to its microprocessor, thereby enhancing the precision of synchronized waveform measurements. Moreover, the IBRM is adept at recording high-fidelity waveform data, capturing nuances such as waveform distortions, high-order harmonics, and wide-band oscillations prevalent in power grids with substantial IBR presence. A prototype of the IBRM has been constructed and subjected to rigorous testing to assess its functional capabilities and measurement precision, utilizing both idealized signal generators and a real-world off-grid inverter setup as benchmarks.

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