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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Systematic Matter and Binding-Energy Distributions from a Dispersive Optical Model Analysis

We present the first systematic dispersive-optical-model analysis of 16,18 O, 40,48 Ca, 58,64 Ni, 112,124 Sn, and 208 Pb. In all systems, roughly half the total nuclear binding energy is associated with the most-bound 10% of the total nucleon density. The extracted neutron skins reveal interplay of asymmetry, Coulomb, and shell effects on the skin thickness. Here, our results indicate that optical-model fits on isotopic pairs are effective for constraining nuclear structural quantities otherwise difficult to observe experimentally.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Fault Response of Distributed Energy Resources Considering the Requirements of IEEE 1547-2018

Inverter-based DER responses to faults on the electric power system are different than those of conventional generators and are often poorly understood. Inverter fault responses are largely software-defined, within physics-based constraints. DERs are regulated by standards and grid codes that also constrain their responses. Though fault response of DERs will vary widely, for any DER following any particular standard, a pattern of fault response can be synthesized by observing that particular standard. In this paper, the recently published IEEE 1547-2018 is examined to explore the general fault response of any DER that follows IEEE 1547-2018. Additionally, an IEEE 1547-2018 compliant inverter model is developed and tested in simulation to find the fault response, and that response is compared with the fault response of a commercial off-the-shelf inverter.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2026

Energy Distribution of the Galactic Center Excess’s Sources

The Galactic Center Excess (GCE) may yet herald the discovery of annihilating dark matter. Weighing against that conclusion are analyses showing evidence for dim point sources within the spatial structure of the emission. Because of technical limitations these analyses are purely spatial with all spectral information that could disentangle the excess from astrophysical backgrounds discarded. Here, we demonstrate that a neural network simulation-based inference approach can jointly analyze the spatial and spectra data. The addition is profound: energy information drives the putative point sources to be significantly dimmer, indicating either the GCE is truly diffuse in nature or made of an exceptionally large number of sources. Quantitatively, for our best fit background model, the excess is essentially consistent with Poisson emission as predicted by dark matter. If due to point sources, our median prediction is O(10^{5}) sources, or more than 35 000 at 90% confidence-both orders of magnitude larger than the hundreds preferred by earlier point-source analyses of the GCE, although variations allowed by background systematics could reduce the required number of sources by roughly an order of magnitude.

List, Florian↗
DOE OSTI2021

Risk Management for Distributed Energy Resources

The National Institute of Standards and Technology will be hosting on Tuesday, February 2 and Wednesday, February 3, 2021, the second workshop in a new series focusing on the Open Security Controls Assessment Language. NREL extended the scope of the DERCF to include the NIST Risk Management Framework (RMF), addressing the challenges faced by federal energy managers when complying with the NIST RMF for DER systems. The NIST RMF is a cyclical process designed to incorporate principles of security and risk management into an organization’s system policies and procedures. The DER-RM will be downloadable application that runs locally and documents all the major requirements for achieving Authority to Operate the DER.

cybersecurity↗
DOE OSTI2022

Cybersecurity Certification Standard for Distributed Energy Resources

Cybersecurity Certification Standard for Inverter Based Resources led by UL, and contributed to by NREL, is a certification standard for devices and their cyber security practices. The standard, which is being developed and led by UL, contains certification and testing processes for the devices to be certified before they are deployed in the field. The importance of the standard is to ensure that all DER devices have all the five pillars of cybersecurity, in which the baseline cybersecurity posture of the DER industry will be elevated.

certification standard↗
DOE OSTI2023

1-D Convolutional Graph Convolutional Networks for Fault Detection in Distributed Energy Systems

This paper presents a 1-D convolutional and graph convolutional networks for fault detection in microgrids. The combination of 1-D convolutional neural networks (1D-CNN) and graph convolutional networks (GCN) helps extract both spatial-temporal correlations from the voltage measurements in microgrids. The fault detection scheme includes fault event detection, fault type and phase classification, and fault location. There are five neural network model training to handle these tasks. Transfer learning and fine-tuning are applied to reduce training efforts. The combined 1-D convolutional and graph convolutional networks (1D-CGCN) is compared with the traditional ANN structure on the Potsdam 13-bus microgrid dataset. The accuracy of 99.5%, 98.4%, 99.2%, and 95.5% are achieved in fault event detection, fault type classification, fault phase identification, and fault location respectively. The detailed confusion matrices of fault type and fault phase classification are provided for validation.

deep neural network↗
DOE OSTI2021

Evolving Architectures and Considerations to address Distributed Energy Resources and Non-Wired Alternatives

The electric grid is in the beginning stage of a transformation, driven by a combination of shutdowns of coal-fired plants, commissioning of new natural-gas plants, and tremendous growth in energy supply from renewables such as wind, and solar. As utilities navigate this transformation, their progress is supported by advances in Operational Technologies (OT), and Informational Technologies (IT), such as automation, smart inverters, cloud computing, mobile computing, machine learning, big data analytics, which have the potential to enable advanced capabilities more efficiently and at a lower cost. This white paper focuses on the architectural considerations that will allow the industry to transition in a planned manner. It introduces and formalizes two architectural constructs –the data bus and the control bus. The data bus is responsible for carrying all non-operational models and information necessary to drive utility decisions. In contrast, the control bus is responsible for carrying all operational data and control actions taken at the local level, centralized level, or other levels in-between. The paper reviews those architectural considerations, their requirements and how they will evolve. This paper intends to provide a context for vendors, utilities, and their service providers to review and understand the changes that are coming and get ready for them.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Automated Classification of Plasma Regions Using 3D Particle Energy Distributions

We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural network classifier to predict four regions crossed by the Magnetospheric Multiscale Mission (MMS) on the dayside magnetosphere: solar wind, ion foreshock, magnetosheath, and magnetopause using solely DIS spectrograms. The accuracy of the classifier is urn:x-wiley:21699380:media:jgra56742:jgra56742-math-0001%. We use the classifier to detect mixed plasma regions, in particular to find the bow shock regions. A similar approach can be used to identify the magnetopause crossings and reveal regions prone to magnetic reconnection. Data processing through the trained classifier is fast and efficient and thus can be used for classification for the whole MMS database.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Basic factors for acquiring, correcting, and interpreting probe current-voltage characteristic in moderate-collisional plasma for determining electron energy distribution

Methods for inferring the electron distribution function (EDF) from a probe current–voltage trace are reviewed for moderate-collisional plasma. The explicit accounting of particle collisions, modeling sheath depletion, and electron refilling processes are rarely performed when analyzing probe measurements, yet they are critical for regime validity, EDF-profile accuracy, and space potential quantification precision. Furthermore, the advantages, limitations, and utilization of large-area wall probes in moderate-collisional plasma, capable of highly resolved indications of energetic electrons in plasma, are outlined.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗