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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Control systems and data management for high-power laser facilities

The next generation of high-power lasers enables repetition of experiments at orders of magnitude higher frequency than what was possible using the prior generation. Facilities requiring human intervention between laser repetitions need to adapt in order to keep pace with the new laser technology. A distributed networked control system can enable laboratory-wide automation and feedback control loops. These higher-repetition-rate experiments will create enormous quantities of data. A consistent approach to managing data can increase data accessibility, reduce repetitive data-software development and mitigate poorly organized metadata. An opportunity arises to share knowledge of improvements to control and data infrastructure currently being undertaken. We compare platforms and approaches to state-of-the-art control systems and data management at high-power laser facilities, and we illustrate these topics with case studies from our community

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Machine Learning Based Network Parameter Estimation Using AMI Data

The expansion of distribution power system and the growing penetration of distributed energy resources present new challenges for situational awareness. Calibrating the extended system model with sensor measurements and maintaining the usability is critical for utilities. This paper presents a distribution network parameter estimation (DNPE) approach using machine learning (ML) and metering data that improve the quality of extended distribution power system modeling. The reliability model can improve the ability of endpoint data to be translated into network-level situational awareness in real time and help distribution system operators (DSOs) solve branch flow and voltage problems. In addition, a data analytic and automate processing scheme is proposed to improve the sensor data quality and prevent misleading information. The effectiveness of the proposed method is verified with actual advanced metering infrastructure (AMI) data on a real utility feeder model, while considering the higher penetration of photovoltaic power generation. The test of DNPE and study results are demonstrated in this paper.

Parameter estimation, machine learning, power dist↗
NASA NTRS2000

Airborne Measurements of Nitric Oxide, Nitrogen Dioxide, Ozone, and Total Reactive Nitrogen During the NASA Global Tropospheric Experiment

Fabrication of the University of Michigan Multichannel Chemiluminescence Instrument (UMMCI) was completed in early 1996 and the instrument participated in test flights on the NASA P3B at Wallops Island prior to integration and deployment for the PEM- Tropics A Mission. The UMMCI consists of 4 channels for simultaneous measurements of ozone and NO with the option for measurements of NO2 and NOy (total reactive nitrogen) when converters are placed upstream of the NO channels. Each NO channel consists of a zeroing volume and reaction vessel, while the ozone channel consists of an ozone catalyst (or scrubber) trap that is not in line with the reaction vessel. The detectors in all for channels are Hamamatsu photomultiplier tubes, which are followed by pulse amplifier discriminators on the NO channels and an electrometer on the ozone channel. Schematics of the Detector Module and NOx/03 Probe Insert and Diagrams of the Control and Data System, the Power and Ground System, the Gas Flow System, and the Calibration System Flow are attached. Intercomparisons were conducted with G. Gregory, NASA/Langley, during the test flights (following prior calibration of the ozone generator/calibrators at the Wallops Long-Path Absorption facility). Initial test results appeared to be reasonable, and instrument characterization studies proceeded for the ozone channel and the 3 NO channels until deployment for integration for the PEM-Tropics Mission. Ozone data was obtained for Flights #4, and 6-2 1, and finalized data was submitted to the PEM-Tropics Data Archive and to the Science Team during the April 1997 Data Workshop. Although it initially appeared that the instrument sensitivity varied, subsequent tests showed that this was the fault of a leak in the ozone calibrator. In fact; the instrument sensitivity has not been observed to vary in a large number of tests over the years since the PEM-Tropics mission. We have, therefore, a very high degree of confidence in the O3 data that we submitted. NO data was obtained for all flights except the mission out of Christmas Island and the subsequent return to Hawaii, during which time the NO channels were contaminated with back-flushing pure NO from the O3 channel. The NO channels were found to suffer from a varying artifact (7 - 22 pptv) which made the instrument's results unreliable for the marine boundary layer portions of the missions (where conditions were extremely clean with NO typically < 2 pptv and O3 only 8 ppbv). Nonetheless, the NO values were an upper limit and were quite useful in identifying pollution layers and outflow from the Latin American Continent. NO data was submitted to the archive for all flights during which data was obtained. Ozone data from the P3B was among the results presented in several peer-reviewed publications. Notably, the ozone data supported the first published finding of evidence for the occurrence of aerosol nucleation in the marine boundary layer. As well, the ozone data was utilized in a treatment of the wavenumber spectra of winds, temperature and trace gases and to demonstrate the occurrence of layering as observed by the P3B during PEM-Tropics A . O3 and NO data were used in an assessment of the impact of southern hemispheric biomass burning, and in the role of NOx and O3 in HOx photochemistry during the PEM Tropics A mission. These data, along with other species measured during PEM-Tropics A, have also been employed in the generation of climatologies for tropospheric ozone and its precursors by the PI's research group.

Carroll, Mary Anne↗
NASA NTRS2020

Electromagnetic Compatibility Considerations for International Space Station Payload Developers

The International Space Station (ISS) is a laboratory for scientific research, innovative technology development, and global education. The ISS provides a number of facilities and platforms for payload developers and investigators to conduct biological, microgravity, and Earth and space observation science, as well as for performing technology development. Due to the unique nature of the ISS vehicle and its electrical power and data systems, achieving electromagnetic compatibility (EMC) with the vehicle requires special considerations by the payload developer. The ISS electromagnetic interference (EMI) requirements and test methods are based on MIL-STD-461, “Electromagnetic Emissions and Susceptibility Requirements for the Control of Electromagnetic Interference”, Revision C, and MIL-STD-462, “Electromagnetic Interference Characteristics, Measurement of,” respectively. The low source impedance of the test setup requires special considerations when designing or selecting EMI power filters and switched mode power supplies. Many filters, suited for later revisions of MIL-STD-461, will result in non-compliant designs. ISS electrical power system power quality requirements, imposed to protect the stability of the system, can also affect EMI filter design. The selection and use of commercial-off-the-shelf (COTS) equipment for ISS applications requires special considerations to meet both EMC and crew safety requirements. Furthermore, the ISS environment can provide unique immunity challenges; if the payload developer ignores these challenges, the result is a possible loss of science or impact to technology demonstration. The ISS provides a unique opportunity for the science and technology development community. However, in order to be successful, the payload developer must incorporate special EMC considerations, many of which will be presented.

McCollum, Matthew↗
DOE OSTI2023

Data-driven quantum approximate optimization algorithm for power systems

Abstract Quantum technology provides a ground-breaking methodology to tackle challenging computational issues in power systems. It is especially promising for Distributed Energy Resources (DERs) dominant systems that have been widely developed to promote energy sustainability. In those systems, knowing the maximum sections of power and data delivery is essential for monitoring, operation, and control. However, high computational effort is required. By leveraging quantum resources, Quantum Approximate Optimization Algorithm (QAOA) provides a means to search for these sections efficiently. However, QAOA performance relies heavily on critical parameters, especially for weighted graphs. Here we present a data-driven QAOA, which transfers quasi-optimal parameters between weighted graphs based on the normalized graph density. We verify the strategy with 39,774 expectation value calculations. Without parameter optimization, our data-driven QAOA is comparable with the Goemans-Williamson algorithm. This work advances QAOA and pilots its practical application to power systems in noisy intermediate-scale quantum devices.

Jing, Hang (ORCID:0000000256343489)↗
NASA NTRS1992

Long Duration Exposure Facility (LDEF) attitude measurements of the interplanetary dust experiment

The Long Duration Exposure Facility (LDEF) Interplanetary Dust Experiment (IDE) was unique in providing a time history of impacts of micron-sized particles on six orthogonal faces of LDEF during the first year in orbit. The value of this time resolved data depended on and was enhanced by the proper operation of some basic LDEF systems. Thus, the value of the data is greatly enhanced when the location and orientation of LDEF is known for each time of impact. The location and velocity of LDEF as a function of time can be calculated from the 'two-line elements' published by GSFC during the first year of the LDEF mission. The attitude of LDEF was passively stabilized in a gravity-gradient mode and a magnetically anchored viscous damper was used to dissipate roll, pitch, and yaw motions. Finally, the IDE used a standard LDEF Experiment Power and Data System (EPDS) to collect and store data and also to provide a crystal derived clock pulse (1 count every 13.1072 seconds) for all IDE time measurements. All that remained for the IDE was to provide a system to calibrate the clock, eliminating accumulative errors, and also verify the attitude of LDEF. The IDE used solar cells on six orthogonal faces to observe the LDEF sunrise and provide data about the LDEF attitude. The data was recorded by the EPDS about 10 times per day for the first 345 days of the LDEF mission. This data consist of the number of IDE counts since the last LDEF sunrise and the status of the six solar cells (light or dark) at the time of the last IDE count. The EPDS determined the time that data was recorded and includes, with each record, the master EPDS clock counter (1 count every 1.6384 seconds) that provided the range and resolution for time measurements. The IDE solar cells provided data for an excellent clock calibration, meeting their primary purpose, and the time resolved LDEF attitude measurements that can be gleaned from this data are presented.

Kassel, Philip C., Jr.↗
DOE OSTI2021

Nominal and adversarial synthetic PMU data for standard IEEE test systems

GridSTAGE (Spatio-Temporal Adversarial scenario GEneration) is a framework for the simulation of adversarial scenarios and the generation of multivariate spatio-temporal data in cyber-physical systems. GridSTAGE is developed based on Matlab and leverages Power System Toolbox (PST) where the evolution of the power network is governed by nonlinear differential equations. Using GridSTAGE, one can create several event scenarios that correspond to several operating states of the power network by enabling or disabling any of the following: faults, AGC control, PSS control, exciter control, load changes, generation changes, and different types of cyber-attacks. Standard IEEE bus system data is used to define the power system environment. GridSTAGE emulates the data from PMU and SCADA sensors. The rate of frequency and location of the sensors can be adjusted as well. Detailed instructions on generating data scenarios with different system topologies, attack characteristics, load characteristics, sensor configuration, control parameters are available in the Github repository - https://github.com/pnnl/GridSTAGE. There is no existing adversarial data-generation framework that can incorporate several attack characteristics and yield adversarial PMU data. The GridSTAGE framework currently supports simulation of False Data Injection attacks (such as a ramp, step, random, trapezoidal, multiplicative, replay, freezing) and Denial of Service attacks (such as time-delay, packet-loss) on PMU data. Furthermore, it supports generating spatio-temporal time-series data corresponding to several random load changes across the network or corresponding to several generation changes. A Koopman mode decomposition (KMD) based algorithm to detect and identify the false data attacks in real-time is proposed in https://ieeexplore.ieee.org/document/9303022. Machine learning-based predictive models are developed to capture the dynamics of the underlying power system with a high level of accuracy under various operating conditions for IEEE 68 bus system. The corresponding machine learning models are available at https://github.com/pnnl/grid_prediction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

An Open-Access Repository of Synchrophasor Data Quality Examples: Curation and Example Applications

Synchrophasor measurements are critical in providing wide-area situational awareness to power system operators. However, data artifacts may be introduced due to various issues such as loss of communication, loss of GPS signal, internal clock error, and vendor-specific implementation of phasor estimation algorithms. Tools designed to provide actionable insights from synchrophasor data, hence, must be designed to be robust to these data quality issues. In this work, two years of synchrophasor data sourced from multiple electric utilities in the United States were analyzed to identify examples of data quality problems. These examples were then labeled and published in the Grid Event Signature Library, a publicly available repository of power system measurements hosted by the Oak Ridge National Laboratory. This paper describes the data curation process, and illustrates two application use cases where the dataset can be valuable to the research community. In the first use case, a random forest classifier is trained to distinguish power system disturbance signatures from data anomalies introduced in synchrophasor measurements due to clock errors. The second use case studies the impact of data quality issues on an example synchrophasor application (specifically, event start time determination). The choice of data quality problems investigated is informed by the examples in the repository curated in this work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Convolutional Variational Autoencoder-based Unsupervised Learning for Power Systems Faults

Classification of power system event data is a growing need, particularly where non-protective relaying-based sensors are used to monitor grid performance. Given the high burden of obtaining event data with appropriate labeling, an unsupervised approach is highly valuable. This approach enables using event data without labeling, which is far easier to obtain. This paper presents an unsupervised learning method to classify and label transients observed in the distribution grid. A Convolutional Variational Autoencoder (CVAE) was developed for this purpose. We demonstrate the efficacy of our approach using the transient data generated from the simulations. The simulation data is used to train the CVAE that identifies different faults as different clusters in the latent space. The clusters are then used as the foundation model to categorize the real-world data.

Alam, Maksudul↗
DOE OSTI2020

Estimation Matrix Calibration of PMU Data-driven State Estimation Using Neural Network

Linear state estimation (LSE) is a phasor measurement unit (PMU) data-based power system state estimation that incorporates a linear measurement model in rectangular coordinates. Due to the high computational efficiency and high observational time-resolution, LSE can act as a supplementary state estimation in a wide-area monitoring system (WAMS). The performance of LSE is relatively sensitive to noises in measurements. Therefore, the estimation accuracy relies heavily on the accuracy of the estimation matrix, which is directly influenced by the measurement weight matrix. This paper proposes two novel calibration method of the estimation matrix using neural networks. One is based on the minimum absolute network loss (ANL), and the other is based on the minimum average squared network loss (ASNL). Both methods are tested and compared with LSE algorithms on the IEEE 14-bus system

neural network↗
DOE OSTI2024

Market Implications of Alternative Operating Reserve Modeling in Wholesale Electricity Markets

Pricing and settlement mechanisms are crucial for efficient resource allocation, investment incentives, market competition, and regulatory oversight. In the United States, Regional Transmission Operators (RTOs) adopts a uniform pricing scheme that hinges on the marginal costs of supplying additional electricity. This study investigates the pricing and settlement impacts of alternative reserve constraint modeling, highlighting how even slight variations in the modeling of constraints can drastically alter market clearing prices, reserve quantities, and revenue outcomes. Focusing on the diverse market designs and assumptions in ancillary services by U.S. RTOs, particularly in relation to capacity sharing and reserve substitutions, the research examines four distinct models that combine these elements based on a large-scale synthetic power system test data. Our study provides a critical insight into the economic implications and the underlying factors of these alternative reserve constraints through market simulations and data analysis.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

NOODLES Grid [SWR-25-93]

NOODLES Grid is a real-time visualization server for power system simulation data. It loads precomputed datasets, builds optimized instance renderings, and streams live interactive scenes to connected clients. It is built for high scalability, flexible visualization, and fast interaction. NOODLES is a cross-platform/device/tool protocol for collaborative visualization. NOODLES was Developed at the National Renewable Energy Laboratory (NREL) as a capability of the Insight Center https://www.nrel.gov/computational-science/insight-center.html

Brunhart-Lupo, Nicholas [National Renewable Energy↗
NASA NTRS1973

Assessment of lightweight mobile nuclear power systems

A review was made of lightweight mobile nuclear power systems (LMNPS). Data cover technical feasibility studies of LMNPS and airborne vehicles, mission studies, and non-technical conditions that are required to develop and use LMNPS.

Anderson, J. L.↗
NASA NTRS1977

Long Duration Exposure Facility - A unique mode of Shuttle utilization

The Long Duration Exposure Facility (LDEF) is a Shuttle-transported, reusable, essentially passive facility with accommodations for a wide variety of experiments which require a free-flying carrier for exposure in space. Specifically, the LDEF is tailored to provide low-cost accommodations for experiments which have modest requirements for electrical power and data systems, and for experiments which benefit from postflight laboratory investigations with the retrieved experiment hardware. Each experiment for LDEF will be totally self-contained in a tray. As the interface with the facility has been minimized, the LDEF experimenters will be freed from many of the requirements which have complicated development of space experiments in the past.

Kinard, W. H.↗