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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A Proactive Stochastic Framework for Cyber-Physical Power Systems Security

This paper presents a framework for cyberphysical power systems security in which defensive action is proactive, striving to mitigate the harm from strategic cyber attacks before they occur. The prospect is formulated in a previously-studied context of state estimation via the Kalman filter under false data injection attacks. Assuming a cognitive attacker who is both advanced and persistent, the proactive defense rests upon stochastically influencing the sensors, Phasor Measurement Units, such that subsequent falsification attacks are countered. Examples are crafted to illustrate both the efficacy of the proactive approach in ideal situations and the practical challenges implied by non-ideal situations.

El Mezyani, Touria↗
DOE OSTI2023

What Role Do Aggregators Play in Power System Security and Resilience?

Barriers to the participation of distributed energy resources (DERs) in wholesale electricity markets have limited the use of DERs for power system security and resilience. In September 2020, the Federal Energy Regulatory Commission (FERC) approved an order to reduce these barriers. FERC Order No. 2222 enables the participation of DER aggregators in wholesale electricity markets. DERs include renewable generation and technologies that support the integration of renewable generation by increasing grid flexibility and resilience. Requiring wholesale energy markets to allow DER aggregator participation provides a path for DERs to become competitive in these markets. As the contribution from aggregated DERs continues to increase, the aggregator's role in supporting grid security and resilience will become more critical. This paper reviews work that demonstrates how DER aggregators can provide resilience support through technical capabilities, operational strategies, and secure communication architectures. Socioeconomic influences and impacts of aggregators, including implications for social resilience, are presented. In surveying the current state-of-the-art across different but interconnected topics, we illustrate how aggregators can be power system participants that enhance grid security. There is no one-size-fits-all approach to enhancing resilience in a power grid that includes a growing cohort of DER aggregators, but there are many options for aggregators to contribute to a more resilient and secure power grid.

aggregator↗
DOE OSTI2023

What Role Do Aggregators Play in Power System Security and Resilience? Preprint

Barriers to the participation of distributed energy resources (DERs) in wholesale electricity markets have limited the use of DERs for power system security and resilience. In September 2020, the Federal Energy Regulatory Commission (FERC) approved an order to reduce these barriers. FERC Order No. 2222 enables the participation of DER aggregators in wholesale electricity markets. DERs include renewable generation and technologies that support the integration of renewable generation by increasing grid flexibility and resilience. Requiring wholesale energy markets to allow DER aggregator participation provides a path for DERs to become competitive in these markets. As the contribution from aggregated DERs continues to increase, the aggregator's role in supporting grid security and resilience will become more critical. This paper reviews work that demonstrates how DER aggregators can provide resilience support through technical capabilities, operational strategies, and secure communication architectures. Socioeconomic influences and impacts of aggregators, including implications for social resilience, are presented. There is no one-size-fits-all approach to enhancing resilience in a power grid that includes a growing cohort of DER aggregators, but there are many options for aggregators to contribute to a more resilient and secure power grid.

aggregator↗
DOE OSTI2025

Dynamic security assessment of systems powered only by grid-forming power plants with uncertain dispatch using polynomial vectors

A modern challenge in power engineering is to perform the dynamic security assessment (DSA) of grids that are 100% powered by inverter-based resources (IBRs). Addressing this challenge is difficult because: (i) the dispatch of IBRs can be uncertain as a result of the variability of renewable resources and (ii) they have hard current control limits that cannot be neglected, contrasting synchronous machines. To address this problem, this paper sets forth a framework to conduct DSA of bulk power systems that are 100% powered by grid-forming IBRs. Furthermore, the framework considers that IBR operational conditions are unknown but bounded by a zonotope which is also expressed as a polynomial vector for uncertainty propagation via Dormand–Prince integration. The framework is applied to modified versions of the WSCC 9-bus and IEEE 39-bus grids.

14 SOLAR ENERGY↗
NASA NTRS1993

Communications and control for electric power systems: Power flow classification for static security assessment

This report investigates the classification of power system states using an artificial neural network model, Kohonen's self-organizing feature map. The ultimate goal of this classification is to assess power system static security in real-time. Kohonen's self-organizing feature map is an unsupervised neural network which maps N-dimensional input vectors to an array of M neurons. After learning, the synaptic weight vectors exhibit a topological organization which represents the relationship between the vectors of the training set. This learning is unsupervised, which means that the number and size of the classes are not specified beforehand. In the application developed in this report, the input vectors used as the training set are generated by off-line load-flow simulations. The learning algorithm and the results of the organization are discussed.

Niebur, D.↗
NASA NTRS2019

A Multiple Model Based Approach for Deep Space Power System Fault Diagnosis

Improving protection and health management capabilities onboard the electrical power system (EPS) for spacecraft is essential for ensuring safe and reliable conditions for deep space human exploration. Electrical protection and control technologies on the National Aeronautics and Space Administration's (NASA's) current human space platform relies heavily on ground support to monitor and diagnose power systems and failures. As communication bandwidth diminishes for deep space applications, a transformation in system monitoring and control becomes necessary to maintain high reliability of electric power service. This paper presents a novel approach for on-line power system security monitoring for autonomous deep space spacecraft.

Autonomous Power Controller↗
DOE OSTI2022

Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

affinity propagation↗
DOE OSTI2026

Generator Frequency Response Droop Monitoring Tool

Monitoring and analyzing the frequency response performance of power generation units is essential for maintaining reliable and secure power system operation. To address this need, an automation tool has been developed to provide a pipeline for processing historical power plant generation data, including large-scale SCADA archives. The tool performs end-to-end processing, including event detection, frequency response (FR) analysis in accordance with NERC standards, and estimation of speed governor droop characteristics. The tool is designed with a modular architecture, allowing individual components of the workflow to be extended, customized, or deployed independently. In addition, the tool provides an API that enables seamless integration with other production systems and operational analytics platforms.

Etingov, PavelV [Pacific Northwest National Labora↗
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 OSTI2023

Quantification of storage required for preserving frequency security in wind‐integrated systems

Abstract The penetration of wind power generation into the power grid has been accelerated in recent times due to the aggressive emission targets set by governments and other regulatory authorities. Although wind power has the advantage of being environment‐friendly, wind as a resource is intermittent in nature. In addition, wind power contributes little inertia to the system as most wind turbines are connected to the grid via power electronic converters. These negative aspects of wind power pose serious challenges to the frequency security of power systems as penetration increases. In this work, an approach is proposed where an energy storage system (ESS) is used to mitigate frequency security issues of wind‐integrated systems. ESSs are well equipped to supply virtual inertia to the grid due to their fast‐acting nature, thus replenishing some of the energy storage capability of displaced inertial generation. In this work, a probabilistic approach is proposed to estimate the amount of inertia required by a system to ensure frequency security. Reduction in total system inertia due to the displacement of conventional synchronous generation by wind power generation is considered in this approach, while also taking into account the loss of inertia due to forced outages of conventional units. Monte Carlo simulation is employed for implementing the probabilistic estimation of system inertia. An ESS is then sized appropriately, using the system swing equation, to compensate for the lost inertia. The uncertainty associated with wind energy is modeled into the framework using an autoregressive moving average technique. Effects of increasing the system peak load and changing the wind profile on the expected system inertia are studied to illustrate various factors that might affect system frequency security. The proposed method is validated using the IEEE 39‐bus test system.

17 WIND ENERGY↗
DOE OSTI2022

Technologies for providing secure emergency power control of high voltage direct current transmission system

Technologies for providing secure emergency power control of a high voltage direct current transmission (HVDC) system include a controller. The controller includes circuitry configured to receive status data indicative of a present physical status of a power system. The circuitry is also configured to obtain an emergency power control command triggered by a remote source. The emergency power control command is to be executed by an HVDC transmission system of the power system. Further, the circuitry is configured to determine, as a function of the status data, whether the emergency power control command is consistent with the present physical status of the power system and block, in response to a determination that the emergency power control command is not consistent with the present physical status of the power system, execution of the emergency power control command by the HVDC transmission system.

Pan, Jiuping↗
DOE OSTI2022

Cross-Layered Cyber-Physical Power System State Estimation towards a Secure Grid Operation

In the Smart Grid paradigm, this critical infrastructure operation is increasingly exposed to cyber-threats due to the increased dependency on communication networks. An adversary can launch an attack on a power grid operation through False Data Injection into system measurements and/or through attacks on the communication network, such as flooding the communication channels with unnecessary data or intercepting messages. A cross-layered strategy that combines power grid data, communication grid monitoring and Machine Learning based processing is a promising solution for detecting cyberthreats. In this paper, an implementation of an integrated solution of a cross-layer framework is presented. The advantage of such a framework is the augmentation of valuable data that enhances the detection of anomalies in the operation of power grid. IEEE 118-bus system is built in Simulink to provide a power grid testing environment and communication network data is emulated using SimComponents. The performance of the framework is investigated under various FDI and communication attacks.

cyber security, network security, cyber-physical s↗