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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Power System Event Identification Based on Deep Neural Network With Information Loading

Online power system event identification and classification are crucial to enhancing the reliability of transmission systems. In this study, we develop a deep neural network (DNN) based approach to identify and classify power system events by leveraging real-world measurements from hundreds of phasor measurement units (PMUs) and labels from thousands of events. Two innovative designs are embedded into the baseline model built on convolutional neural networks (CNNs) to improve the event classification accuracy. First, we propose a graph signal processing based PMU sorting algorithm to improve the learning efficiency of CNNs. Second, we deploy information loading based regularization to strike the right balance between memorization and generalization for the DNN. Numerical results based on real-world dataset from the Eastern Interconnection of the U.S power transmission grid show that the combination of PMU based sorting and the information loading based regularization techniques help the proposed DNN approach achieve highly accurate event identification and classification results.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Online PMU Missing Value Replacement Via Event-Participation Decomposition

We introduce a new method for online Phasor Measurement Unit (PMU) missing value replacement. Our approach allows us to decompose PMU event responses into a non-dynamic component (denoted the participation factor) that can be inferred directly from the past and a dynamic component that can be inferred directly from all other PMUs (denoted the event strength). When missing values occur, we can use these two components, which do not rely on the missing index, to estimate the correct value. The method is extremely fast and can easily be used for online applications. Furthermore, extensive testing on real power system event data reveals that our approach achieves state-of-the-art performance in terms of Mean Absolute Percent Errors (MAPEs) for PMU data dropped during event periods. Here, the method also yields an interpretable and simplified view of events for further analysis and applications. The method relies only on PMU data and does not take outside information such as network topology.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Frequency Disturbance Event Detection Based on Synchrophasors and Deep Learning

Power system frequency disturbances are caused by various generation and transmission events including generator trips, load disconnections, line trips, etc. Accurate detections of the events are crucial to bulk power system situation awareness and event investigation. This paper utilizes the recent advances of deep learning to build a convolutional neural network model to detect events in an accurate yet straightforward manner. Herein, the rate of change of frequency and the relative angle shift are converted to images as the inputs of the proposed model. Finally, this paper uses two convolutional neural networks and classifier fusion to achieve the detection result. Compared with the conventional event detection algorithm and the frequency only deep learning model, the proposed model improves the detection accuracy by over 48%. As a promising tool for bulk power system situation awareness, the proposed model requires a short decision time, which is suitable for practical scenarios.

42 ENGINEERING↗
DOE OSTI2025

Synchro-Waveform-Based Event Identification Using Multi-Task Time-Frequency Transform Networks

Influenced by the transient dynamics and reduced inertia characteristics of high-penetration renewable energy systems, power system events frequently exhibit distinct characteristics such as high-frequency components including wide-band oscillations and hyper-harmonics. This makes standard systems face challenges including significant latency and reduced accuracy due to limited data resolution. However, current methods face significant limitations, including insufficient pattern capture ability, low noise immunity, limited feature learning, and restricted localization capabilities, thereby hindering real-time performance. To tackle this issue, this paper proposed a novel synchro-waveform-based event identification approach via a Multi-task Time-frequency Transform Network (MTTNet). Initially, a Time-frequency Transform Block (TTB) is developed to extract both local and global information. The TTB leverages both Fourier and S-transforms to derive comprehensive time-frequency information from synchro-waveforms. Subsequently, a multi-task learning strategy is employed to identify the type and distinguish localization of events. Integrating the TTB and multi-task learning, the MTTNet is designed for synchro-waveform-based event identification, incorporating an adaptive weighting strategy and simplified computation for the S-transform. Two different datasets, comprising simulated and actual synchro-waveforms, are collected from the IEEE 123 bus system and a real-world high-penetration renewable energy system using a universal grid analyzer. Extensive experiments on various conditions are carried out. In conclusion, results demonstrated that the MTTNet consistently surpasses both basic and advanced baselines, with maximum improvements of 13.24% and 9.86%, respectively, while reducing the calculation burden by 15-19 times to achieve real-time event identification.

Event identification↗
DOE OSTI2023

Virtual Time III, Part 1: Unified Virtual Time Synchronization for Parallel Discrete Event Simulation

Algorithms for synchronization of parallel discrete event simulation have historically been divided between conservative methods that require lookahead but not rollback, and optimistic methods that require rollback but not lookahead. In this paper we present a new approach in the form of a framework called Unified Virtual Time (UVT) that unifies the two approaches, combining the advantages of both within a single synchronization theory. Whenever timely lookahead information is available, a logical process (LP) executes conservatively using an irreversible event handler. When lookahead information is not available the LP does not block, as it would in a classical conservative execution, but instead executes optimistically using a reversible event handler. The switch from conservative to optimistic synchronization and back is decided on an event-by-event basis by the simulator, transparently to the model code. UVT treats conservative synchronization algorithms as optional accelerators for an underlying optimistic synchronization algorithm, enabling the speed of conservative execution whenever it is applicable, but otherwise falling back on the generality of optimistic execution. We describe UVT in a novel way, based on fundamental invariants, monotonicity requirements, and synchronization rules. UVT permits zero-delay messages and pays careful attention to tie-handling using superposition. We prove that under fairly general conditions a UVT simulation always makes progress in virtual time. This is Part 1 of a trio of papers describing the UVT framework for PDES, mixing conservative and optimistic synchronization and integrating throttling control.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

HANDBOOK BEST PRACTICES FOR ORGANIZING PUBLIC E WASTE COLLECTION EVENTS Indoor and Outdoor Settings

This handbook was created to share best practices of organizing public e-waste collection events in the United States. The steps and checklists outlined in this handbook have been drawn from Sunnking’s decade’s long experience of organizing large scale outdoor collection events in New York. These practices have been verified by INL and ECyclers of Idaho to work well for both indoor and outdoor events in Idaho at a smaller scale. Understanding that the local conditions vary significantly, we have compiled key deciding factors to share in this handbook. We hope that by following these steps, you will succeed in organizing your own events. Although e-waste events typically require careful logistics planning due to the nature of the materials, the experience is very rewarding to bring the community together.

Collection Event↗
DOE OSTI2022

Automated detection of photovoltaic cleaning events: A performance comparison of techniques as applied to a broad set of labeled photovoltaic data sets

Extracting accurate soiling loss information from photovoltaic (PV) production data first requires segmenting the time series data per natural or manually occurring cleaning events. Maintenance logs are often incomplete, rain data are often unavailable, and the debate on rain thresholds for cleaning and dew or wind cleanings is still ongoing. The present work aims to overtake these issues by improving automated methods to detect these cleaning events and therefore improve extraction of soiling loss information. Time series power production data from 22 PV inverters were labeled for natural or manually occurring cleaning events. The data sets were carefully selected to include varying degrees of soiling, cleaning events, and noise. Several algorithms, including filtering logic and change point detection, were examined for efficacy at detecting the labeled cleanings. All the methods introduced except for changepoint detection showed significant improvement at detecting the labeled cleaning events per the mean F 1 score. Furthermore, the highest performing cleaning detection algorithm achieved an absolute increase in the mean F 1 score of 43% over the default version of the RdTools stochastic rate and recovery (SRR) algorithm. The highest performing algorithm included irradiance filtering and a cleaning detection threshold, adjusted based on the 40-day centered rolling median of the absolute day-to-day deviations in the daily performance index (PI). Furthermore, these improvements are promising as cleaning detection is an essential step in the automated analysis of PV soiling.

14 SOLAR ENERGY↗
DOE OSTI2024

Rainfall events stimulate episodic associative nitrogen fixation in switchgrass

Abstract Associative N 2 fixation (ANF) is widespread but poorly characterized, limiting our ability to estimate global inputs from N 2 fixation. In some places, ANF rates are at or below detection most of the time but occasionally and unpredictably spiking to very high rates. Here we test the hypothesis that plant phenology and rainfall events stimulate ANF episodes. We measured ANF in intact soil cores in switchgrass ( Panicum virgatum L.) in Michigan, USA. We used rain exclusion shelters to impose three rainfall treatments with each receiving 60 mm of water over a 20-day period but at different frequencies. We concurrently established a treatment that received ambient rainfall, and all four treatments were replicated four times. To assess the effects of plant phenology, we measured ANF at key phenological stages in the ambient treatment. To assess the effects of rainfall, we measured ANF immediately before and immediately after each wetting event in each treatment involving rainfall manipulation. We found that the previous day’s rainfall could explain 29% of the variation in ANF rates within the ambient treatment alone, and that bulk soil C:N ratio was also positively correlated with ANF, explaining 18% of the variation alone. Wetting events increased ANF and the magnitude of response to wetting increased with the amount of water added and decreased with the amount of inorganic N added in water. ANF episodes thus appear to be driven primarily by wetting events. Wetting events likely increase C availability, promote microbial growth, and make rhizosphere conditions conducive to ANF.

Vizza, Carmella (ORCID:0000000292690357)↗
DOE OSTI2020

Single electron–hole pair sensitive silicon detector with surface event discrimination

Here, we demonstrate single electron-hole pair resolution in a single-sided, contact-free 1 cm$^2$ by 1 mm thick Si crystal operated at 48 mK, with a baseline energy resolution of 3 eV. This crystal can be operated at voltages in excess of $\pm50$ V, resulting in a measured charge resolution of 0.06 electron-hole pairs. The high aluminum coverage ($\sim$70%) of this device allows for the discrimination of surface events and separation of events occurring near the center of the detector from those near the edge. We use this discrimination ability to show that dark events seen in previous detectors of a similar design are likely dominated by charge leakage along the side wall of the device. We saw only a small reduction in dark event rate with this biasing scheme, further suggesting that sidewall and edge-dominated processes are responsible for the majority of these events.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Data-driven upper bounds and event attribution for unprecedented heatwaves

The last decade has seen numerous record-shattering heatwaves in all corners of the globe. In the aftermath of these devastating events, there is interest in identifying worst-case thresholds or upper bounds that quantify just how hot temperatures can become. Generalized Extreme Value theory provides a data-driven estimate of extreme thresholds; however, upper bounds may be exceeded by future events, which undermines attribution and planning for heatwave impacts. Here, we show how the occurrence and relative probability of observed yet unprecedented events that exceed a priori upper bound estimates, so-called “impossible” temperatures, has changed over time. We find that many unprecedented events are actually within data-driven upper bounds, but only when using modern spatial statistical methods. Furthermore, there are clear connections between anthropogenic forcing and the “impossibility” of the most extreme temperatures. Robust understanding of heatwave thresholds provides critical information about future record-breaking events and how their extremity relates to historical measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Reflections on the anomalous ANITA events: the Antarctic subsurface as a possible explanation

The Antarctic Impulsive Transient Antenna (ANITA) balloon experiment was designed to detect radio signals initiated by high-energy neutrinos and cosmic ray (CR) air showers. These signals are typically discriminated by the polarization and phase inversions of the radio signal. The reflected signal from CRs suffer phase inversion compared to a direct ‘tau neutrino’ event. In this paper, we study subsurface reflection, which can occur without phase inversion, in the context of the two anomalous up-going events reported by ANITA. It is found that subsurface layers and firn density inversions may plausibly account for the events, while ice fabric layers and wind ablation crusts could also play a role. This hypothesis can be tested with radar surveying of the Antarctic region in the vicinity of the anomalous ANITA events. Future experiments should not use phase inversion as a sole criterion to discriminate between down-going and up-going events, unless the subsurface reflection properties are well understood.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Semiquantitative Estimates of Rainfall Variability During the 8.2 kyr Event in California Using Speleothem Calcium Isotope Ratios

A multiproxy record from a fast-growing stalagmite reveals variable hydroclimate on the California coast across the 8.2 kyr event and a precursor event likely caused by initial drainage of proglacial Lake Agassiz. Using speleothem δ 44 Ca, we develop the first semiquantitative estimates of paleorainfall variability for California through calibration with measurements of the modern climate and cave environment. We find that the magnitude of rainfall variability during the 8.2 kyr event approached the multiyear variability observable in the recent past (1950–2019) and the magnitude of variability during the precursor event likely exceeded this range. Additionally, we observe other instances of multidecadal variability comparable in magnitude to the precursor event during the record. Finally, our work suggests that speleothem calcium isotope ratios are a powerful semiquantitative means to reconstruct paleorainfall, although numerous factors must be assessed in each cave system before applying this approach.

58 GEOSCIENCES↗
DOE OSTI2021

Estimation of Possible Primary Biological Particle Emissions and Rupture Events at the Southern Great Plains ARM Site

We use 10 years of data from the Department of Energy (DoE) Atmospheric Radiation Measurements (ARM) United States Southern Great Plains (SGP) site with nearby regional pollen and fungal spore measurements to indirectly estimate the seasonal influence of these two primary biological aerosol particles (PBAP). We estimate possible primary emissions of larger PBAP and PBAP rupture events, which form submicron organic aerosol during precipitation or high relative humidity. High pollen counts at two urban stations near SGP occur during late winter/early spring (day of year (DOY) 50–120) and late summer (DOY 240–310). Around 4–19 days per year show possible pollen events (PPE) when near-surface lidar observations of daily linear particle depolarization ratio >0.1 are coincident with high organic aerosol fraction. For PPE days with rainfall, aerosol size distribution observations show enhanced submicron particle concentrations consistent with pollen rupture events. For fungal spores, high fungal spore counts occur during late spring/early summer (DOY 110–195) and late summer/autumn (DOY 220–340). Based on size distribution observations, up to 7% of days have possible fungal spore rupture events (PFE) with higher aerosol number count specifically over the range expected for fungal spore fragment mobility diameter (20–50 nm). These short-lived PFE correlate with rainfall or occur after prolonged exposure to rainfall (e.g., >10 h). While the SGP site lacks direct measurements of bioaerosol and large particle sizes, this analysis suggests that PBAP primary emissions and rupture events could occur about 32 days per year, representing an important component of the aerosol budget during seasonal emissions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

How Do Climate Model Resolution and Atmospheric Moisture Affect the Simulation of Unprecedented Extreme Events Like the 2021 Western North American Heat Wave?

Abstract Although the 2021 Western North America (WNA) heat wave was predicted by weather forecast models, questions remain about whether such strong events can be simulated by global climate models (GCMs) at different model resolutions. Here, we analyze sets of GCM simulations including historical and future periods to check for the occurrence of similar events. High‐ and low‐resolution simulations both encounter challenges in reproducing events as extreme as the observed one, particularly under the present climate. Relatively stronger amplitudes are observed during the future periods. Furthermore, high‐ and low‐resolution short initialized GCM simulations are both able to reasonably predict such strong events and their associated high‐pressure ridge over the WNA with a 1 week forecast lead time. Moisture sensitivity experiments further indicate a drier atmospheric moisture condition results in substantially higher near‐surface temperatures in the simulated heat events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Quantifying the impacts of climate change and extreme climate events on energy systems

Climate induced extreme weather events and weather variations will affect both the demand of energy and the resilience of energy supply systems. The specific potential impact of extreme events on energy systems has been difficult to quantify due to the unpredictability of future weather events. In this work, we develop a stochastic-robust optimization method to consider both low impact variations and extreme events. Applications of the method to 30 cities in Sweden, by considering 13 climate change scenarios, reveal that uncertainties in renewable energy potential and demand can lead to a significant performance gap (up to 34% for grid integration) brought by future climate variations and a drop in power supply reliability (up to 16%) due to extreme weather events. Finally, appropriate quantification of the climate change impacts will ensure robust operation of the energy systems and enable renewable energy penetration above 30% for a majority of the cities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Scintillation event imaging with a single photon avalanche diode camera

Position and time measurements of scintillation events encode information about the radiation source. Single photon avalanche diode (SPAD) arrays offer multiple-megapixel spatial resolution and tens of picoseconds temporal resolution for detecting single photons. Current lensless designs for measuring scintillation events use sensors that are lower in spatial resolution. Camera-based designs use sensors that are lower in temporal resolution or readout rate and cannot image individual interactions. Here we propose to image scintillation events in a thick, monolithic scintillator using a high-resolution SPAD camera. We demonstrate that a commercial SPAD camera is able to gather sufficient signal to image individual scintillation events and observe 3D shifts in their spatial distribution. Simulations show that a SPAD camera can localize individual scintillation events in 3D. We report direct imaging of gamma-ray interactions in a scintillator with a SPAD camera. The proposed design may allow to measure complex signatures of individual particles interacting in the scintillator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Enhanced particle transport events approaching the density limit of the J-TEXT tokamak

Enhanced particle transport events are discovered and analyzed as the density limit of the J-TEXT tokamak is approached. Edge shear layer collapse is observed and the ratio of Reynolds power to turbulence production decreases. Simultaneously, the divergence of turbulence internal energy flux (i.e. turbulence spreading) increases, indicating that shear layer collapse triggers an outward spreading event. Studies of correlations show that the enhanced particle transport events are quasi-coherent, and manifested primarily in density fluctuations which exhibit positive skewness. Furthermore, electron adiabaticity emerges as the critical parameter which signals transport event onset. For α < 0.35 as density approaches the Greenwald density, both turbulence spreading and density fluctuations rise rapidly. Taken together, these results elucidate the connections between edge shear layer, density fluctuations, particle transport events, turbulence spreading and plasma edge cooling as the density limit is approached.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Studies of event burst phenomenon with SiPMs in liquid nitrogen

Silicon photomultiplier (SiPM) are used to collect scintillation photons in many cryogenic noble liquid detectors deployed around the world, such as DarkSide, nEXO, MEGII, ProtoDUNE and DUNE. An event burst phenomenon was observed during routine characterization on many models of SiPMs operated in liquid nitrogen. These bursts of consecutive pulses are initiated by an intense dark photoelectron pulse with an event rate much lower than the time-uncorrelated thermal dark pulse. Although the rate of these burst events is very low, it can potentially compromise some dedicated rare physics event searches which are also anticipated to be of extremely low rate. Here, we systematically studied the behavior of the event burst phenomenon and identified the probable cause of the phenomenon. Here, this investigation is important for the selection of SiPMs for use in noble liquid detectors, high energy physics experiments, and industrial applications where SiPMs are used in cryogenic environment.

47 OTHER INSTRUMENTATION↗