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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Assessment and Commissioning of Electrical Substation Grid Testbed with a Real-Time Simulator and Protective Relays/Power Meters in the Loop

Electrical utility substations are wired with intelligent electronic devices (IEDs), such as protective relays, power meters, and communication switches. Substation engineers commission these IEDs to assess the appropriate measurements for monitoring, control, power system protection, and communication applications. Like real electrical utility substations, complex electrical substation grid testbeds (ESGTs) need to be assessed for measuring current and voltage signals in monitoring, power system protection, control (synchro check), and communication applications that are limited by small measurement percentage errors. In the process of setting an ESGT with real-time simulators and IEDs in the loop, protective relays, power meters, and communication devices must be commissioned before running experiments. In this study, an ESGT with IEDs and distributed ledger technology was developed. The ESGT with a real-time simulator and IEDs in the loop was satisfactorily assessed and commissioned. The commissioning and problem-solving tasks of the testbed are described to define a method with flowcharts to assess possible trouble-shooting in ESGTs. This method was based on comparing the simulations versus IED measurements for the phase current and voltage magnitudes, three-phase phasor diagrams, breaker states, protective relay times with selectivity coordination at electrical faults, communication data points, and time-stamp sources.

Piesciorovsky, Emilio↗
DOE OSTI2022

Synchrophasors-based Master State Awareness Estimator for Cybersecurity in Power Grid: Testbed Implementation & Field Demonstration

The integration of distributed energy resources(DERs) and expansion of complex network in the distribution grid requires an advanced distributed state estimator to monitor the grid health at micro-level. The distribution state estimator will improve the situational awareness and resiliency of distributed power system. This paper proposes a synchrophasors-based master state awareness (MSA) estimator to enhance the cybersecurity in distribution grid by providing a real-time estimation of system operating states to control center operators. In this paper, the proposed MSA estimator utilizes only phasor measurements, bus magnitudes and angles, from phasor measurement units (PMUs),deployed in local substations, to estimate the system states and also detects data integrity attacks, such as load tripping attack that disconnects the load. To validate the proof of concept, we implement the proposed methodology in cyber-physical testbed environment at the Idaho National Laboratory (INL) Electric Grid Security Testbed. Further, to address the “valley of death” and support technology commercialization, field demonstration is also performed at the Critical Infrastructure Test Range Complex(CITRC) at the INL. Our experimental results reveal a promising performance in detecting load tripping attack and providing an accurate situational awareness through an alert visualization dashboard in real-time

42 ENGINEERING↗
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 OSTI2022

Developing a PSCAD Model of the Reduced 240-Bus WECC Test System: Preprint

Fast-acting controls of inverter-based resources (IBR) introduce unprecedented dynamics to bulk power systems and island power grids, especially the high-frequency dynamics ranging from $>5$ Hz to hundreds of Hz. These oscillatory stability problems cannot be captured by the phasor domain simulations, e.g., in PSS/E, Powerworld and TSAT, due to insufficient modeling. Instead, they should be studied by electromagnetic transient (EMT) simulations by using proper EMT dynamic models. This paper develops and validates the PSCAD model of the reduced 240-bus Western Electricity Coordinating Council (WECC) test system. The PSCAD model will be released to the public, which can be used by both academia and power industry practitioners for studying the fast dynamic problems without any Critical Energy/Electric Infrastructure Information (CEII).

240-bus WECC test system↗
DOE OSTI2020

Model-Agnostic Algorithm for Real-Time Attack Identification in Power Grid using Koopman Modes

Malicious activities on measurements from sensors like Phasor Measurement Units (PMUs) can mislead the control center operator into taking wrong control actions resulting in disruption of operation, financial losses, and equipment damage. In particular, false data attacks initiated during power systems transients caused due to abrupt changes in load and generation can fool the conventional model-based detection methods relying on thresholds comparison to trigger an anomaly. In this paper, we propose a Koopman mode decomposition (KMD) based algorithm to detect and identify false data attacks in real-time. The Koopman modes (KMs) are capable of capturing the nonlinear modes of oscillation in the transient dynamics of the power networks and reveal the spatial embedding of both natural and anomalous modes of oscillations in the sensor measurements. The Koopman-based spatio-temporal nonlinear modal analysis is used to filter out the false data injected by an attacker. The performance of the algorithm is illustrated on the IEEE 68-bus test system using synthetic attack scenarios generated on GridSTAGE, a recently developed multivariate spatio-temporal data generation framework for simulation of adversarial scenarios in cyber-physical power systems.

Nandanoori, Sai Pushpak↗
DOE OSTI2024

Synchrophasor-Based Zonal Current Differential Protection for Secondary Low Voltage Networks

This report describes an approach to utilizing phasor measurement unit (PMU) data from multiple Intelligent Electronics Devices (IEDs) in a low-voltage network to produce a differential scheme for protecting the medium-voltage feeder and low-voltage network transformers. The proposed protection scheme is designed and prototyped on a real-time automation controller. Its performance is evaluated using real-time controller hardware-in-the-loop simulation. Lab testing results indicate that the proposed protection scheme allows significant distributed energy resources (DER) backfeed and enables selective and fast protection of medium voltage feeders.

42 ENGINEERING↗
DOE OSTI2025

Optimization-Based Data-Driven Approach for Detecting Fault Location in Power Systems

In grids with large penetration of converterinterfaced resources (CIRs), measurements of voltage, current, and line parameters can fluctuate significantly during fault conditions. These fluctuations, combined with complex network topologies and extensive system branching, make accurate fault location challenging. Faults, such as short circuits, can cause prolonged outages with serious socio-economic impacts, highlighting the need for rapid fault identification to minimize downtime. However, current fault detection methods—such as relays and digital fault recorders—often relay information too slowly, impeding swift corrective action. Given the limited availability of high-resolution phasor measurement units, this paper introduces an optimization-based observer to estimate fault locations, grid line parameters, and voltages using local CIR measurements. To preserve the confidentiality of CIRs and enhance estimation accuracy, this study uses a black-box model of CIRs. This bottom-up, event-driven approach can enhances protection and control systems through optimized and real-time fault detection. Simulation results show that the optimization-based data-driven observer can accurately detect fault locations and estimate grid states and parameters, providing valuable insights for utilities and operators in grid applications.

Subedi, Sunil [ORNL] (ORCID:000000034069090X)↗
DOE OSTI2021

Extended Frequency Divider for Bus Frequency Estimation Considering Virtual Inertia from DFIGs

Accurate estimation of local bus frequency is important for effectively controlling both synchronous and nonsynchronous generators. As the power grid evolves to accommodate essential reliability services such as virtual inertia from nonsynchronous generators, conventional techniques to estimate frequency face challenges. This paper proposes a new frequency estimation method that can effectively include the inertia contributions from double-fed wind generators (DFIGs). This is achieved through the proposed extended frequency divider formula (FDF) to include the contributions of DFIGs via Thevenin equivalents. The proposed extended FDF does not suffer from numerical issues as compared to existing phasor angle derivative-based approaches. Moreover, the knowledge of the rotor speeds of the synchronous machine and DFIGs as well as of the network admittance matrix allows estimating the frequencies of all buses in the grid, thereby significantly improving system situational awareness with a limited number of measurements. Numerical results on the IEEE 39-bus power system with DFIGs show that the proposed method achieves more accurate bus frequency estimations than the original FDF formula and other approaches based on the numerical derivation of the bus voltage phase angles.

double-fed induction generator↗
DOE OSTI2021

Sensor enabled data-driven predictive analytics for modeling and control with high penetration of DERs in distribution systems

The electric power grid is undergoing a tremendous transformation due to the increasing penetration of renewable energy resources beginning with wind and more recently with the distributed energy resources (DERs) such as solar and battery storage. DERs have dramatically changed the role of the distribution systems in the overall power grid, and they are expected to contribute a significant portion of power generation in the future. If current trends for DERs continue, system operation and control will need to change dramatically for improved grid reliability and resiliency. As renewable resources increase in penetration, new and challenging operational, planning, and design problems are expected to emerge. Some of the key challenges that arise in the planning and operation of the future grid are: 1) Quantifying the impact of high DER penetration in distribution systems on bulk grid behavior over multiple time scales. 2) Identifying whether a particular DER configuration/settings have a large impact on the overall grid behavior. These challenges can be addressed in an offline manner using detailed T&D grid models and they can also be addressed in an online manner using sensor measurements. In particular, the advancement and planned growth in sensor technology in power grid over various voltage levels provide us with a unique opportunity to tackle these challenges from a data analytic perspective without needing detailed T&D grid models. A few questions that naturally arise when addressing the challenges from DERs using sensor data are: 1) How can we use limited sensor measurements to monitor & control voltage stability and small signal stability of the bulk system? 2) How can we ensure that the developed data analytic methods are robust to data availability and quality issues? 3) How can we compute the developed analytics in a scalable manner using streaming measurements? In this project, we addressed the aforementioned challenges arising from DERs and answered the questions raised above on how to effectively use the sensor measurements to enhance the reliability and performance of the electric grid. Thus, the overarching goal of this project is to develop effective reduced/representative system models from data that make the computational complexity sufficiently manageable so as to be useful to simulate, analyze, and even control complex non-linear power systems dynamics with large penetrations of DERs. In order to achieve the objective, the project team established a four-fold technical approach 1) Formulated a combined transmission-distribution co-simulation framework for data generation and validation, 2) Derived reduced/representative models of power systems based on data-driven methods for efficient computation and appropriate representation of system behavior, 3) Developed data driven characterization of power system behavior based on transfer operator theory, machine learning and optimization for model estimation, 4) Incorporated a scalable data management and processing architecture using distributed Kafka streaming applications that coordinate input data streams to the developed data analytics. The key accomplishments of the project are: 1) Development of a scalable multi-timescale T&D co-simulation framework (both for steady state and for dynamic co-simulation) using commercial solvers (PSSE and GridLAB-D). The steady-state T&D co-simulation interface is shared with our industry partner (PJM). 2) A structured reduced order dynamic model of distribution systems that can represent partial motor stalling along with a systematic procedure to derive the model parameters. 3) A PMU based online method to monitor, localize and mitigate fault-induced delayed voltage recovery using DER reactive support and load control in distribution systems. 4) Development of linear operator based robust methodologies for dynamic state estimation, uncertainty quantification, system identification and trajectory prediction for power system dynamics. 5) An adaptive damping control for utilizing wind energy resources to provide oscillation damping and system stability. 6) Implementation of Kafka-based framework for efficient processing of streaming data using Linux-based local virtual environment.

DER integration↗
DOE OSTI2024

Per-phase Phasor Modeling of GFL and GFM Inverters for Distribution System Dynamic Studies

A significant increase in inverter-based distributed energy resources (DERs) is expected in the near future. Existing electromagnetic models and sequential electromechanical models of DERs supporting dynamic analysis cannot be extended to largescale three-phase distribution systems usually with unbalanced construction. As such, there is a need for appropriate industry models that can support distribution system dynamic studies. This paper describes the latest phasor models of grid-following (GFL) and grid-forming (GFM) inverters that can capture distribution system dynamics under grid disturbances. The overall modeling performance is examined and compared with results from other platforms such as controller hardware-in-the-loop (CHIL), MATLAB/Simulink and PSCAD with electromagnetic transient (EMT) and/or phasor simulation capabilities under balanced and unbalanced faults.

Distribution system, Grid modelling, GridLAB-D, Po↗
DOE OSTI2020

Enhanced Tensor Completion Based Approaches for State Estimation in Distribution Systems

Grid state estimation is essential for effective control and management of distribution systems. While weighted least squares has been the conventional method for state estimation, sparsity-aware methods have become popular due to their superior performance with limited data. Matrix completion and compressed sensing-based state estimation approaches exploit the underlying smoothness in the state variables. However, classic matrix completion methods do not take into account the temporal correlation of system states. Compressed sensing methods, on the other hand, require an appropriate choice of sparsifying basis that may not be easy to identify. This paper proposes a blocktensor completion based framework which uses an alternative approach to estimate voltage phasor, power injections and branch currents. This approach utilizes the temporal correlation of the system states in a tensor trace-norm minimization formulation with power flow equations as constraints. Herein, feature scaling is introduced in the problem formulation to benefit from the improved sensitivity of the tensor trace norm to the matrix columns in the scaled unfoldings of the tensor. Weighted tensor norm is utilized to exploit the structures of the different unfoldings of the state measurement tensor to improve the voltage estimation. The estimation accuracy is further improved by alternatively estimating the tensor columns and increasing the available data at each stage in the tensor completion process. The proposed methods are evaluated on the IEEE-33, 37 test systems and a 100- node test system. The proposed methods are shown to provide significant performance gains relative to the classic matrix and tensor completion based approaches.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Data-driven Modeling for Grid Edge IBRs: A Digital Twin Perspective of User-Defined Models

Recent events in Odessa have brought attention to the challenges associated with the interaction between Inverter- Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted sev- eral issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection perfor- mance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long- term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)

Mahapatra, Kaveri [BATTELLE (PACIFIC NW LAB)]↗
DOE OSTI2024

Assessing Inverter-Based Resources Modeling Gaps in Commonly Used Short-Circuit Programs

Future bulk power systems are expected to operate with a high penetration of inverter-based resources (IBR). This is anticipated to fundamentally change the system’s short-circuit behaviors and demand a change in existing transmission line protection settings or schemes. Phasor-domain short circuit programs (e.g., ASPEN, CAPE, and CYME) remain the mainstream tools for developing protection settings. However, the system protection community has shared concerns about the accuracy of IBR models in short-circuit programs. This report compares the performance of existing generic IBR models in short-circuit programs with detailed electro-magnetic transient (EMT) models provided by several different IBR manufacturers in PSCAD, to quantify the short-circuit program’s IBR model accuracy, assess their ability to replicate individual IBR controls such as negative sequence current injections, and identify specific gaps in the existing generic phasor-domain IBR models that are currently available.

42 ENGINEERING↗
DOE OSTI2022

Intensity Modulated Fiber Optic Sensor: A Novel Grid Measurement Unit

This paper presents a novel approach to physical displacement-based power grid measuring via an Intensity Modulated Fiber Optic Sensor (IMFOS). An IMFOS utilizes one fiber to transmit the intensity modulation light from its Electro-Optic controller to a fiber optic probe. The power grid voltage and current can induce physical displacements in transducers via the piezoelectric effect and the Lorentz law, respectively, which then result in a distance change between the optical probe and the reflective surface of the transducers. In parallel, multiple fibers are used to collect the reflective light for electro-optic conversion. Furthermore, a National Instruments-based characterization platform is set up for performance evaluation. Here, the testing result demonstrates that the IMFOS is immune to the inherent DC and low-frequency saturation issues prevalent in conventional potential and current transformers. Finally, the IMFOS is implemented in a Universal Grid Analyzer to illustrate its applicability for phasor estimation in actual power grids.

42 ENGINEERING↗
DOE OSTI2022

Smart sensor for online situational awareness in power grids

Waveforms in power grids typically reveal a certain pattern with specific features and peculiarities driven by the system operating conditions, internal and external uncertainties, etc. This prompts an observation of different types of waveforms at the measurement points (substations). An innovative next-generation smart sensor technology includes a measurement unit embedded with sophisticated analytics for power grid online surveillance and situational awareness. The smart sensor brings additional levels of smartness into the existing phasor measurement units (PMUs) and intelligent electronic devices (IEDs). It unlocks the full potential of advanced signal processing and machine learning for online power grid monitoring in a distributed paradigm. Within the smart sensor are several interconnected units for signal acquisition, feature extraction, machine learning-based event detection, and a suite of multiple measurement algorithms where the best-fit algorithm is selected in real-time based on the detected operating condition. Embedding such analytics within the sensors and closer to where the data is generated, the distributed intelligence mechanism mitigates the potential risks to communication failures and latencies, as well as malicious cyber threats, which would otherwise compromise the trustworthiness of the end-use applications in distant control centers. The smart sensor achieves a promising classification accuracy on multiple classes of prevailing conditions in the power grid and accordingly improves the measurement quality across the power grid.

Dehghanian, Payman↗
DOE OSTI2022

A Cross-Domain Optimization Framework of PMU and Communication Placement for Multidomain Resiliency and Cost Reduction

Phasor measurement units (PMUs) play a crucial role in real-time monitoring and control of power grids. They rely on a communication network to transfer measurement data to the phasor data concentrator (PDC) for further processing and analysis. In this paper, a resilient cross-domain PMU and communication link placement method for minimizing the overall installation cost of the wide-area measurement system (WAMS) is proposed. Here, the main idea is to break down the barrier between the power grid domain and the communication domain, and consider the impact of one when design the other. The PMU placement in the power grid domain takes into account the cost of communication links by generating multiple solutions with equally minimum PMU costs for communication link placement evaluation. On the other hand, the communication link placement problem reduces the cost by customizing the routing policies based on the different roles of PMUs in grid observability. The proposed WAMS design is capable of withstanding any single component failure in the power domain (PMU failure or power branch failure) or in the communication domain (communication link failure or PDC failure). Numerical study on the IEEE 57-bus system reveals that the developed cross-domain optimization framework can significantly reduce the overall installation cost of WAMS while attaining multi-domain resiliency.

24 POWER TRANSMISSION AND DISTRIBUTION↗
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

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↗