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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Deep learning model to detect various synchrophasor data anomalies

High-density synchrophasors provide valuable information for power grid situational awareness, operation and control. Unfortunately, due to factors including communication instability and hardware failure, their data quality can be greatly deteriorated by anomalies. Since the anomalies can impact the performance of the synchrophasor applications, it is of paramount significance to propose a model to detect anomalies in synchrophasor. In this study, a convolutional neural network model is established to detect and classify the anomalies in the synchrophasor measurements. Additionally, four types of anomalies observed in actual synchrophasors including erroneous patterns, random spikes, missing points and high-frequency interferences are considered in this study. The proposed model is extensively evaluated via field-collected measurements from the synchrophasor network in Jiangsu grid, China. The superior performance of the proposed model indicates the great potential of using deep learning for the detection of abnormal synchrophasor measurements.

42 ENGINEERING↗
DOE OSTI2020

High-Frequency Signature-Based Fault Detection for Future MV Distribution Grids: Preprint

Increased deployment of Distributed Energy Resources (DERs) and microgrids are significantly changing the operation of distribution grid. Increasing penetration of power electronics based DERs also raise several operational problems such as protection coordination issues, voltage raise, recloser synchronization, stability, and frequency control. Legacy systems are designed for unidirectional power flow. Bidirectional power flows, and varying short circuit levels are some of the major challenges in implementing these legacy protection schemes for future power grid with high penetration of power electronics based DERs. Inverter based DER’s can contribute to a maximum of 2 p.u. fault current. Interconnection standards might require even lower fault current contribution. If these protection related challenges are not addressed, it will result in limiting the penetration of DERs in distribution feeders. In this paper, the feasibility of high frequency signature generated by transients is evaluated to identify and locate faults in a distribution system. The paper will discuss the need for an accurate line modeling approach to accurately capture the high frequency signatures. These high frequency signatures are naturally occurring electromagnetic transients for events such as switching events in the network, faults events, etc. This is different than that implemented in transmission systems as the proposed approach utilizes advanced signal processing techniques to retrieve valuable information from the high frequency signatures generated during faults and does not just rely on time delay measurements between reflections of the high frequency signals. In the full paper, information of a modified IEEE distribution feeder test system to accurately model the high frequency signatures will be provided. The distribution lines will be modeled using universal line modeling (ULM) technique in an electromagnetic transients program (EMTP) modeling platform. Different test cases will be simulated and these test cases will include different fault types, multiple fault locations, capacitor bank switching, etc. The feeder system modeled for these simulations will also include power electronics based DERs. Additionally, experimental results demonstrating the high frequency signatures generated due to a transient event in a medium voltage (13.8 kV) distribution line will also be presented in the full paper. Furthermore, the analysis combined with simulation and experimental results in the full paper will help demonstrate the feasibility and efficacy of the proposed protection scheme. The developed protection scheme will not just help solve problems generated by bidirectional current flow, varying short-circuit current capacity, but also help in proliferation of DERs in distribution systems.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2024

An Overview of Electric Vehicle Load Modeling Strategies for Grid Integration Studies

The adoption of electric vehicles (EVs) has emerged as a solution to reduce greenhouse gas emissions in the transportation sector, which has motivated the implementation of public policies to promote their use in several countries. However, the high adoption of EVs poses challenges for the electricity sector, as it would imply an increase in energy demand and possible impacts on the power quality (PQ) of the power grid. Therefore, it is important to conduct EV integration studies in the power grid to determine the amount that can be incorporated without causing problems and identify the areas of the power sector that will require reinforcements. Accurate EV load patterns are required for this type of study that, through mathematical modeling, reflect both the dynamic behavior and the factors that influence the decision to recharge EVs. This article aims to present an overview of EVs, examine the different factors considered in the literature for modeling EV load patterns, and review modeling methods. EV load modeling methods are classified into deterministic, statistical, and machine learning. The article shows that each modeling method has its advantages, disadvantages, and data requirements, ranging from simple load modeling to more accurate models requiring large datasets.

Computer Science↗
NASA NTRS2013

Stability, Transient Response, Control, and Safety of a High-Power Electric Grid for Turboelectric Propulsion of Aircraft

This document contains the deliverables for the NASA Research and Technology for Aerospace Propulsion Systems (RTAPS) regarding the stability, transient response, control, and safety study for a high power cryogenic turboelectric distributed propulsion (TeDP) system. The objective of this research effort is to enumerate, characterize, and evaluate the critical issues facing the development of the N3-X concept aircraft. This includes the proposal of electrical grid architecture concepts and an evaluation of any needs for energy storage.

Armstrong, Michael↗
DOE OSTI2024

Grid Optimization Competition Challenge 3 Problem Formulation

This report contains the problem formulation for the Grid Optimization (GO) Competition Challenge 3. The Grid Optimization Competition is run by a team of researchers from a number of organizations, including the sponsor Advanced Research Projects Agency - Energy (ARPA-E), lead organization Pacific Northwest National Laboratory (PNNL), and technical contributors from Los Alamos National Laboratory (LANL), National Renewable Energy Laboratory (NREL), Texas A&M University (TAMU), Georgia Institute of Technology (GT), University of Wisconsin (UW), and others. The GO Competition poses challenge problems in the field of power grid management, invites entrants to develop solvers for these problems, invokes the solvers on a set of problem instances using common hardware, ranks the solvers according to their performance, and awards prizes according to the rankings. The overall goal of the GO Competition is to spur innovative research on high impact and computationally challenging problems in power grid management from initial development through commercial deployment. Complete information about the GO Competition can be found online at [2]. The webpage covers previous Challenges, rules, timeline, registration information, data formats, scoring methods, computational platform information, information on supported solvers and languages, sponsor information, frequently asked questions, administrator contact information, publicly available problem instances, computer code for reading and evaluating problem and solution data, a sandbox for testing solvers, and a solver submission interface, results, and publications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

nnopf

SAND2026-19433O The nnopf tool addresses the optimal power flow (OPF) problem by optimizing electricity delivery from generating plants to consumers. At the same time, it minimizes costs and adheres to power grid constraints. While traditional methods for solving OPF can be computationally intensive, the tool mitigates this challenge by training neural networks on power grids to predict optimal solutions for the OPF problem. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525.

Hart, William [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2024

Real-Time Testbed for Transmission Line Protection

Hardware in the loop (HIL) testing is crucial for designing and managing electric power grids. These grids are becoming larger and more complex. The importance for students to have safe and intuitive ways to interact with the devices associated with the power grids has never been more crucial. Since most of these tests involve high voltages, this can be a deterrent for instructors and students in undergraduate programs. HIL testing is a solution to these common issues. This method has become one of the most popular methods for testing these power systems. With the use of Western Michigan University’s (WMU) Real-Time Digital Simulator (RTDS) and SEL-421-7 protection relay, a HIL testbed has been created. These devices were interconnected using the communication protocol known as Generic Object-Oriented Substation Event (GOOSE). A simulated transmission line system was modeled in an RTDS software RSCAD as the basis for this testbed. This model simulated different types of faults that could occur in a transmission line while in operation. The SEL-421-7 relay is connected to the RSCAD simulation via GOOSE to protect our simulated transmission line. This testbed was set up to give students a clear understanding of how distance protection works as well as how the SEL-421-7 will react to various kinds of faults, in addition to how useful the RTDS can be when testing different power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2003

Extending Grid Computing to Remote Locations

NASA Glenn Research Center, in cooperation with NASA Ames and geologists from the University of Cincinnati and Bowling Green State University has extended the computational capabilities of the information Power Grid to remote research sites. The combination of satellite (EOS) data acquisition and the IPG processing provides geologists with the ability to identify the key mineralogical features at the research site. The underlying connectivity for this research environment is provided by the Numerical Research and Education Network (NREN) using a combination of terrestrial and mobile satellite-based networking solutions. Our approach not only speeds the process of scientific discovery, but also serves as a simple demonstration of NASA's capacity for geological classification and exploration of remote sites such as the Martian surface. The NASA Glenn Demonstrations combines satellite (EOS) data acquisition and the computational capabilities or the Information Power Grid (IPG) to provide geologists with the ability to identify key mineralogical features in near real-time of an area in study. This capability could someday allow geological classification of rocks and minerals of remote sites such as the Martian surface.

Griffin, Robert↗
DOE OSTI2022

Situational Awareness of Grid Anomalies (SAGA)

The modern power industry becomes more vulnerable to cyber events due to the growing interconnectivity, interdependence, and complexity of the electric power grid. High-fidelity modeling and simulation tools that support the preventative risk analysis on potential cyber-relevant events is essential for ensuring the situational awareness of the system operator as it provides an inexpensive and risk-free environment to test the system responses under various cyber-relevant events and hereby can support research on cyber anomaly detection, optimal protective resource allocation, and mitigation measures. In this webinar, we will share NREL's cybersecurity research capabilities by highlighting the development of a scalable cyber-physical event test bed and demonstration with real hardware in the loop. The developed cyber-physical event test bed is backboned by an integrated transmission, distribution, and communication dynamic co-simulation framework and a plug-and-play cyber event generation module. It is designed to be modular and compatible with parallel computing, and thereby supports large-scale system simulations at an affordable computation cost. The test bed can capture millisecond-to-minutes dynamic frequency and voltage responses under cyber events from the bulk transmission system to the active distribution systems and distributed energy resources at the grid edge.

co-simulation↗
DOE OSTI2022

Effects-Based Monitoring of Geomagnetically-Induced Current Using a Convolutional Neural Network

Geomagnetically-induced current (GIC) due to space weather can flow in the power grid causing undesirable effects such as transformer overheating, misoperation of protection devices, and potential blackouts. It is therefore important to monitor GIC in the power grid to improve online situational awareness and decision-making of system operators during a geomagnetic disturbance. To avoid the costly installation of GIC monitors at transformers’ neutrals, it is desirable to find correlations between GIC and already-monitored parameters. Hence, this work proposed the use of a convolutional neural network (CNN) to compute GIC amplitudes from learned patterns in the time-series data of transformer even harmonic currents. Using an electromagnetic transient program, GIC injection simulations were performed for a modeled Dominion Energy Virginia (DEV) substation with two 504 MVA, 500/230 kV transformers. Data collected from these offline simulations were used to train the CNN to provide online GIC monitoring. Testing the CNN performance involved using real GIC measurements from published literature and from a physical GIC monitor in the DEV area. Finally, the results showed that the proposed method was able to provide GIC readings with a root mean squared error of 1.56 A/phase (equivalent to an average accuracy of 94%) for these real GIC waveforms.

42 ENGINEERING↗
DOE OSTI2020

Cybersecurity Methods for Grid-Connected Power Electronics

The present work shows a secure-by-design process, defense-in-depth method, and security techniques for a secure distributed energy resource. The distributed energy resource is a cybersecure, solar inverter and battery energy storage system prototype, collectively called the Cybersecure Power Router. Consideration is given to the use of the Smart Green Power Node for a foundation of the present work. Metrics for controller security are investigated to evaluate firmware security techniques. The prototype's ability to mitigate, respond to, and recover from firmware integrity degradation is examined. The prototype shows many working security techniques within the context of a grid-connected, distributed energy resource. Further work is expected in the Cybersecure Power Router project. Consideration is also provided for the migration of the present research and the Smart Green Power Node to realize a pre-production prototype.

Moquin, Stephen Joe↗
DOE OSTI2020

CyDER: A Cyber Physical Co-simulation Platform for Distributed Energy Resources in Smartgrids

The CyDER project aimed at developing an open-source, modular and scalable co-simulation platform for power grids with large shares of Distributed Energy Resources (DERs). The project partners are the Lawrence Berkeley National Lab (LBNL), Lawrence Livermore National Lab (LLNL), PG&E, SolarCity, and ChargePoint. The prime recipient is LBNL; SolarCity and ChargePoint were partners for the project’s first two years. Increased DER integration introduces a number of challenges in power grid operation including a more dynamic interaction between the transmission grid and distribution grids, and increased modeling complexity. Although specialized software exists to precisely model different components of the power system, it is far from trivial to integrate all various models and perform a holistic simulation. Instead of replicating all models in a common simulation program, a commonly accepted approach to tackle this model diversity is to couple third-party simulators and models through a co-simulation platform that coordinates information exchange among the various components. Following this line of research, this project’s objective was to develop a co-simulation platform based on a widely accepted industrial standard called Functional Mockup Interface (FMI). Within this process, the project developed models compliant with the FMI standard, called Functional Mockup Units (FMUs), and used them to perform various operational and planning power system analyses. Relying and building upon an industrial standard is the main differentiation of this project compared with previous or parallel efforts in the co-simulation area. Particular emphasis was put on delivering software utilities to facilitate setting up and running co-simulations by end-users. Furthermore, a strong aspect of this project is demonstrating that co-simulation techniques can be used to perform Hardware-in-the-Loop (HIL) simulations that couple software components (e.g., simulated models) with hardware components (e.g., real devices such PV systems and batteries). The long-term goal of CyDER project is to help establish FMI as a powerful standard for co-simulation and promote adoption by electric utilities and other interested stakeholders. The main accomplishments of the project include the development of several FMUs including distribution and transmission grid models, PV inverters with Volt/Var/Watt controllers, batteries, and predictive optimal controllers. Additionally, a unique software package was developed, called SimulatorToFMU, which is capable of exporting any Python-driven simulator or Python script as an FMU. This is an important contribution towards establishing FMI as one of the main co-simulation standards, because more and more third-party programs for sub-system modeling and simulation are delivered with Python APIs. The CyDER platform was used to perform PV hosting capacity analyses in real utility feeders with and without smart inverter controls, battery storage, and EV charging. Smart inverter controls include conventional Volt/Var/Watt controls for reactive power support and active power curtailment, but also predictive controls that optimize the charging and discharging profile of the battery connected on the DC side in order to minimize the customer’s economic benefit. Finally, an important result of this project is delivering an experimental setup that consists of residential-scale PV inverters with battery storage, a real-time grid simulator with an ideal voltage source as grid emulator, and micro Phasor Measurement Units (PMUs). All these components and additional software modules are coupled to one another using the FMI standard and can be co-simulated with the CyDER platform.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗
DOE OSTI2022

Power system load flexibility forecasting

The example embodiments are directed to a system and method for forecasting load flexibility of a power grid. In one example, the method includes receiving temperature values associated with temperature set points of a plurality of loads that are included on a power grid, forecasting a flexibility of the plurality of loads using a polynomial-time mixed-integer non-linear programming (MINLP) optimization based on the received temperature values for the plurality of loads, and outputting information about the forecasted flexibility for display to a display device. The MINLP optimization performs the forecasting of the load flexibility on a fine-grained basis in comparison to conventional methods and is still fast enough that it can be computed in real-time.

Genc, Sahika↗
DOE OSTI2020

Autonomous Tools for Attack Surface Reduction

The electric power grid is a complex critical infrastructure that forms the lifeline of modern society, and its secure and reliable operation is of paramount importance to national security and economic well being. However, recent findings documented in authoritative sources indicate the threat of cyber-based attacks growing in numbers and sophistication. However, securing the grid against stealthy cyber attacks is a challenging task due to legacy nature of the infrastructure coupled with dynamic nature of threat landscape and ever growing sophistication of the adversaries. Additionally, the grid’s attack surface continues to grow with the increased dependence on digital communications and control that now extends to each consumer through smart meters and distributed energy resources. Unfortunately, this expansive surface increases the grid’s vulnerability and further exposes critical control systems in both substations and control centers. To respond to this emerging need, we had successfully assembled an interdisciplinary team with academic- industry partnership to successfully conduct research, development, evaluation, demonstration, and commercialization of attack surface reduction tools, whose goal is to significantly reduce the cyber attack surface in the North American power grid. Our proposed project was a synergistic collaborative effort leveraging the synergistic expertise of the team members across power systems, cyber security and CPS security, testbeds, field deployments and demonstration, and successful commercialization. The team consisted of leading experts from two major universities – Iowa State University, Washington State University – complemented by reputed researchers from two DOE national laboratories – Pacific Northwest National Lab, and Argonne National Lab, one major utility vendor GE Global Research, and one utility partner – Cedar Falls Utilities (CFU). The team members have proven track record of successful academic-industry collaboration in interdisciplinary R&D projects, and bring onboard some of the best state-of-the-art testbed resources, industry-grade SCADA/EMS/DMS environment for experimentation and field demonstration.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Consensus-Based Control and Optimization of Power System Inertia

The integration of distributed energy resources (DERs), such as solar, wind, and energy storage systems, into power grids through inverter-based resources reduces power system inertia, leading to faster frequency dynamics and potential grid instability. To address this challenge, this paper proposes a distributed, consensus-based approach for the real-time control and optimization of inertia sources (synchronous generators and/or DERs) during system disturbances, enhancing both system stability and economic performance. The distributed control and optimization approach assumes each inertia source exchanges information solely with its neighbor ones, making it easily scalable to large power grid networks. The impacts of the communication connectivity among the inertia sources as well as their generation capacity limits on the distributed approach are investigated. We also demonstrate the approach’s robustness in scenarios involving communication time delays and packet losses, validating its effectiveness through numerical simulations on a 4-bus test system and a two-area 8-bus test system.

Power system inertia↗
DOE OSTI2024

Riemannian Optimization Applied to AC Optimal Power Flow: Preprint

The nonlinear, nonconvex AC optimal power flow problem is of growing importance as the nature of the power grid evolves. This problem can be difficult to solve for interior point methods. However, the advent of optimization algorithms over smooth Riemannian manifolds presents an alternative approach. The nonlinear, nonconvex constraints in the AC power flow problem form an embedded submanifold of Euclidean space. In this paper, the authors explore the performance of Riemannian optimization algorithms for the ACOPF problem where the optimization is performed directly on the AC power flow manifold. They demonstrate that these are viable computational alternatives to interior point methods. This is done by using Julia and the packages PowerModels.jl and Manopt.jl.

manifold optimization↗
DOE OSTI2025

Physics-informed Deep Reinforcement Learning-based Control in Power systems

Incorporating physics information into the deep reinforcement learning (DRL) process is a promising approach for addressing the challenges faced in learning-based control design problems for physical systems. Power grid dynamics, being a physical system, adheres to specific physical laws, constraints, as well as operational and control rules. Therefore, consideration of such physics-based law improves the learning process drastically. In general, traditional grid control schemes rely on rule-based mechanisms that cannot adapt to changing operating conditions. To improve the adaptability and computation time, recent research has seen a surge of DRL-based applications in power grid control. A generic DRL-based control design imposes the system performance requirements through the design of reward functions. In some cases, some of the important physics information is injected through this reward function. However, due to the complex dynamics and large state-action space, learning an optimal DRL policy often becomes challenging. Inspired by the latest developments in general machine learning (ML) research, power system researchers have been investigating more direct ways of incorporating physics knowledge into DRL training. This chapter specifically focuses on these aspects of physics-informed DRL designs in grid control. It discusses the significance, applications, research gaps, and open problems that need to be addressed in future research.

artificial intelligence, machine learning↗