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At least 973 records · Page 54
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Anomaly Detection in Power System State Estimation: Review and New Directions

Foundational and state-of-the-art anomaly-detection methods through power system state estimation are reviewed. Traditional components for bad data detection, such as chi-square testing, residual-based methods, and hypothesis testing, are discussed to explain the motivations for recent anomaly-detection methods given the increasing complexity of power grids, energy management systems, and cyber-threats. In particular, state estimation anomaly detection based on data-driven quickest-change detection and artificial intelligence are discussed, and directions for research are suggested with particular emphasis on considerations of the future smart grid.

42 ENGINEERING↗
DOE OSTI2023

CPC data from TAMU TRACER campaign in the Houston, TX region from July to September 2022

During TRACER, the Texas A&M Rapid Onsite Atmospheric Measurements Van (ROAM-V) was deployed to capture airmasses behind (maritime) and ahead (continental) of the passage of the sea-breeze front through Houston. On select sampling days, ROAM-V sampled in the morning/mid-day on the coast and then transited to a second inland site for the afternoon/evening. The suite of instruments deployed on ROAM-V included a Condensation Particle Counter (CPC; GRIMM Model 5.403 CPC), Scanning Mobility Particle Sizer (SMPS; TSI 3750 detector, TSI 3082 classifier, TSI 3088 neutralizer, TSI 3081A Differential Mobility Analyzer), Cloud Condensation Nuclei counter (Droplet Measurement Technologies CCN Counter), micro pulse lidar (Droplet Measurement Technologies Micro Pulse LiDAR (miniMPL)), and a Davis Rotating Uniform size-cut Monitor (DRUM; DRUMAir 4-DRUM). Before sampling at each location, the latitude and longitude were recorded using the GPS on the phone application “My Altitude”. Onboard the ROAM-V, aerosol samples are drawn through a shared isokinetic inlet at a flow rate ranging from 3.5 to 7.0 LPM. A portion of this flow is directed through a cyclone impactor (Brechtel, Inc. Model SCC 0.732) and 0.3 LPM is directed to the CPC. To correct the data for particle losses, we used a two-step method. First, the measured SMPS size distributions were used to calculate total particle loss through the inlet for every SMPS scan at a single deployment location. Second, the CPC data was corrected for particle losses using the average of the total losses per scan. This calculation was done separately for each deployment location due to changes in the measured size distributions between locations. Particle loss from diffusion (based on Kesten, 1991 and Gormley, 1949), inertial impaction in 90-degree bends (based on Aerosol Measurement, 2011 and Crane, 1977), and cyclone impactor efficiency (based on Dirgo, 1985) were included in the loss calculation. When the SMPS was not sampling at a location (in the case of an instrument malfunction or operator error), the reported CPC data was corrected with an average of the total losses for the entire campaign at the specified deployment location (e.g., if we needed to correct Galveston data, then the average of all calculated losses at Galveston was taken). These flatline corrections were used for all data on 22/07/13, 22/07/20, 22/07/22, and the data from Galveston on 22/08/09. An additional flatline correction of 14% was applied to all CPC data based on laboratory calibration prior to and after the campaign. In laboratory calibrations, we identified that for the same sample of air the field CPC (GRIMM Model 5.403 CPC) undercounted the total concentration of particles by 14% compared to the ground truth laboratory CPC (TSI Model 3750). This data was collected for ARM Field Campaign AFC07055 and supported by DOE ASR grant DE-SC0021047. For any further questions, please feel free to contact the instrument PI, Sarah D. Brooks, sbrooks@tamu.edu. Kesten et. al. Calibration of a TSI Model 3025 Ultrafine Condensation Particle Counter. Aerosol Science and Technology, 15:2, 107-111, 1991. Gormley et. al. Diffusion from a Stream Flowing through a Cylindrical Tube. Proceedings of the Royal Irish Academy, Vol 52, 163-169, 1948. Aerosol Measurement: Principles, Techniques, and Applications, Third Edition. John Wiley & Sons, Inc, 2011. Crane et. al. Inertial Deposition of Particles in a Bent Pipe. Journal of Aerosol Science, Vol 8, 161-170, 1977. Dirgo et. al. Cyclone Collection Efficiency: Comparison of Experimental Results with Theoretical Predictions. Aerosol Science and Technology, 4:4, 401-415, 1985.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Processing Meteorological Data for the CAP-88 PC Model at Los Alamos National Laboratory

The Environmental Protection and Compliance-Compliance Programs (EPC-CP) group at Los Alamos National Laboratory (LANL) uses the Clean Air Act Assessment Package 1988 (CAP-88, Littleton 2020) PC model (Version 4.1) to estimate radiological doses for a set of areal sectors surrounding a release location, in order to satisfy the Environmental Protection Agency (EPA) National Emission Standards for Hazardous Air Pollutants (NESHAP) dose calculation requirement in 40 CFR 61 Subpart H. Among several types of data that must be prepared for CAP-88 input is a text file of meteorological data (“WIND” file), consisting of the joint frequency of wind direction, wind speed, and atmospheric stability categories. EPC-CP produces customized WIND files by running a CAP-88 utility program on a user generated text file of wind data in a different format, known as a STability ARray (STAR) file (Turner, 1964). At LANL, EPC-CP meteorologists prepare customized STAR files with data over desired time periods at selected meteorological towers. A custom program written in Precision Visuals -Workstation Analysis and Visualization Environment (PV-WAVE), a commercial Fortran-like language, is used to read LANL meteorological data and write a STAR file; the executable filename is “Star.out”. However, the outdated PV-WAVE utility program is being phased out by EPC-CP, due to the inefficient process to run it and an inability to modify the code. To preserve the ability to create customized meteorological data for CAP-88 in a way that will be easy to use and maintain, a new replacement utility program, written in the Python programming language, has been developed. The new, improved program reads a data file from any LANL meteorological tower, and at each desired observation time, determines the wind direction, wind speed, and stability categories defined in the CAP-88 documentation. The frequencies of all combinations of the three sets of categories are calculated and written to a file in the STAR format, which can later be converted to a WIND file for input into CAP-88.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

In-depth analysis on parallel processing patterns for high-performance Dataframes

The Data Science domain has expanded monumentally in both research and industry communities during the past decade, predominantly owing to the Big Data revolution. Artificial Intelligence (AI) and Machine Learning (ML) are bringing more complexities to data engineering applications, which are now integrated into data processing pipelines to process terabytes of data. Typically, a significant amount of time is spent on data preprocessing in these pipelines, and hence improving its efficiency directly impacts the overall pipeline performance. The community has recently embraced the concept of Dataframes as the de-facto data structure for data representation and manipulation. However, the most widely used serial Dataframes today (R, pandas) experience performance limitations while working on even moderately large data sets. We believe that there is plenty of room for improvement by taking a look at this problem from a high-performance computing point of view. In a prior publication, we presented a set of parallel processing patterns for distributed dataframe operators and the reference runtime implementation, Cylon. In this paper, we are expanding on the initial concept by introducing a cost model for evaluating the said patterns. Furthermore, we evaluate the performance of Cylon on the ORNL Summit supercomputer.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Smart Data Mapping for Connecting Power System Model and Geospatial Data

Knowing the geospatial locations of power system model elements is the foundation for analyzing system vulnerability to natural hazards and connecting loads with end users and their communities. However, power system models and geospatial data for power grid assets may have been developed asynchronously without close coordination. Creating a direct mapping between the two may be a challenging task, considering heterogeneous data structures, target uses, historical legacies, and human errors. This work aims to build an automatic data mapping workflow to connect power system model elements and geospatial data for transmission network, and to support energy grid resilience studies for Puerto Rico. The primary steps in this workflow include constructing graphs using geospatial data, and aligning them to the transmission networks defined in the power system data. The results have been evaluated against existing manual mapping practices for part of the Puerto Rico Power Grid model to illustrate the performance of such auto-mapping solutions.

Resilience, geospatial data, grid transmission net↗
DOE OSTI2020

Mesoscale Modeling to Characterize Eagle Soaring Habitat

Uncovering drivers of risk is crucial to understanding interactions between wildlife and wind turbines, and identifying options for impact minimization. These drivers tie to co-variates linked to behavior and movement patterns that allow us to estimate locations and periods of risk. For volant species, atmospheric flow can have significant influence on flight patterns. For obligate soaring birds, like golden eagles, updraft velocities can inform where eagles are likely to travel, at what altitude, and where conditions are not likely sufficient to sustain soaring flight. This has been an active area of study in recent years, using relatively coarse atmospheric data generally at the 20km x 20km scale or larger. Leveraging a 20-year dataset from the Weather Research and Forecasting Model (WRF) (https://www.mmm.ucar.edu/weather-research-and-forecasting-model), we are quantifying vertical velocities across the continental United States at a 2km x 2km resolution. Wind resource data sets originally were static maps showing the mean annual wind speed over an area. However, for these data sets to be optimally used for various applications they must be high-resolution time series, seamlessly span large geographic contexts, and account for uncertainty in wind speed. The National Renewable Energy Laboratory is producing public available datasets that meet these criteria and will be bias corrected to yield the most accurate wind resource data. This effort is an augment to the current WIND Toolkit which houses a high resolution data set. In the new iteration of the WIND Toolkit, a 20-year dataset will be used to improve the accuracy and estimate uncertainty using ensemble and machine-learning techniques. The resulting product will be the most accurate dataset of its size and at a 2km x 2km spatial and 5-minute temporal resolution. Through this work, a mesoscale vertical velocity layer will be produced by calculating the likelihood of orographic updraft and thermal updraft conditions across the continental United States. Specifically, we will use WRF model output combined with digital elevation maps to predict updrafts and then determine if vertical velocities are sufficient to support Golden Eagle soaring and gliding. Ultimately this data layer will be made available as a GIS layer in the Wind Prospector (maps.nrel.gov/wind-prospector/) tool or a similar framework. Data that will be incorporated include wind speed, direction temperature, relative humidity, barometric pressure, air density, precipitation rate, solar radiation, atmospheric stability, skin temperature, and upward heat flux. These products will advance research on interactions between volant species and wind energy by providing open access to highly resolved data with uncertainty quantification not previously available at this scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Weakly Supervised Event Classification Using Imperfect Real-world PMU Data with Scarce Labels

This paper studies event classification using imperfect real-world phasor measurement unit (PMU) data with scarce event types (labels). By investigating the real-world PMU data, it is observed that most real-world PMU data's event type is unknown, which makes it challenging to directly use such dataset to build event classifiers as existing classification techniques require high-quality training data with known event type (i.e., label). To address this challenge, a weakly supervised learning based event classification approach is developed, which can use noisy and low-quality PMU data for the training. First, data quality issues are fixed using data preprocessing techniques and then event features are constructed from the PMU data. Using these features, a series of labeling functions are learnt to generate initial estimates of the labels of large amounts of unlabeled PMU data. As the labeling functions are learnt using the same data with scarce labels, the label estimates from the labeling functions can be correlated, noisy, and bias. To enhance these initial estimates, a generative model is developed to characterize the dependencies among the estimated labels, based on which better labels are obtained for training event classifiers. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S. power transmission grid show that the proposed weakly supervised event classifier trained using the dataset with only 5% labeled data can achieve 78.4% classification accuracy.

Liu, Yunchuan↗
DOE OSTI2021

Object discrimination based on a swarm of agents

A configuration system for identifying a target configuration of agents for acquiring data is provided. In some embodiments, the configuration system generates sample configurations, each of which has a location within the space for each agent. The configuration system selects as the target configuration a sample configuration that is suitable to acquire data. The configuration system selects the target configuration by, for at least one or more sample configurations, directing that the agents assume the sample configuration and then acquire data when in the sample configuration. The configuration system then stores, for each sample configuration, an indication of suitability for acquiring data based on the sample configuration. The configuration system selects a sample configuration that is deemed to be suitable as the target configuration.

Beer, Neil Reginald↗
DOE OSTI2026

Estimating Flexibility Envelopes for Residential Customers From Utility Smart Meter Data: Preprint

Demand response from residential customers has significant potential to support power system operations, but accurate flexibility estimation is challenging due to the limited resolution of advanced metering infrastructure (AMI) data. Most utility AMI measurements are recorded at hourly intervals, with only a small portion at higher resolutions, and even fewer households have appliance-level energy usage data. To address this issue, this paper proposes a two-stage long short-term memory (LSTM) framework for estimating household flexibility envelopes from low-resolution AMI data. In the first stage, the heating, ventilating, and air-conditioning (HVAC) load and non-HVAC loads are estimated by using a model trained on a small set of households with appliance-level profiles. These estimated data are then used to compute the upper- and lower-flexibility bounds, which are subsequently down-sampled to lower-resolution data. In the second stage, these flexibility bounds serve as training inputs for another LSTM model, enabling direct prediction of flexibility envelopes for households with only hourly AMI data. This method is validated using Pecan Street data from two different areas, and the results demonstrate its applicability and effectiveness.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Rad-hard readout system for Timepix3 Hybrid Pixel Detectors

The Beam Gas Ionisation (BGI) profile monitor, located in the Proton Synchrotron (PS) and Super Proton Synchrotron (SPS) at CERN, requires a radiation-tolerant readout system to transfer data from the challenging accelerator surroundings to the back-end for processing. The system needs to control and acquire data from four Timepix3 Hybrid Pixel Detectors (HPDs) located directly inside the beam pipe, a highly radioactive environment. It must ensure reliability given limited hardware access and preserve signal integrity for the high-speed data (32 channels at 320 MHz). However, due to the unavailability of a suitable rad-hard Timepix3 readout, the Beam Instrumentation PiXeL (BIPXL) readout system was designed to meet these requirements. This system employs radiation-hardened components such as the GBTx and the FEASTMP, both developed at CERN. It will be compatible with forthcoming hybrid pixel detector initiatives in similarly harsh radiation conditions.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Observed metallization of hydrogen interpreted as a band structure effect

A recent experimental study of the metallization of hydrogen tracked the direct band gap and vibron frequency via infrared measurements up to ~425 GPa (Loubeyre et al (2020 Nature 577 631). Above this pressure, the direct gap has a discontinuous drop to below the minimum experimentally accessible energy (~0.1 eV). The authors suggested that this observation is caused by a structural phase transition between the C2/c-24 molecular phase to another molecular phase such as Cmca-12. Here, through ab initio calculations of pressure dependent vibron frequency and direct band gap, we find that the experimental data is consistent with the C2/c-24 phase up to 425 GPa, and suggest that this consistency extends beyond that pressure. Specifically, we find that qualitative changes in the band structure of the C2/c-24 phase lead to a discontinuous drop of the direct band gap, which can explain the observed drop without a structural transition. In conclusion, this alternative scenario, which naturally explains the absence of hysteresis in the measurements, will hopefully motivate further experimental studies to ascertain the structure of the phase above the high pressure 'phase transition'.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Search for Neutrino Doublets and Triplets Using 11.4 yr of IceCube Data

We report a search for high-energy astrophysical neutrino multiplets, detections of multiple neutrino clusters in the same direction within 30 days, based on an analysis of 11.4 yr of IceCube data. A new search method optimized for transient neutrino emission with a monthly timescale is employed, providing a higher sensitivity to neutrino fluxes. This result is sensitive to neutrino transient emission, reaching per-flavor flux of approximately 10 -10 erg cm -2 s -1 from the Northern Sky in the energy range E ≳ 50 TeV. The number of doublets and triplets identified in this search is compatible with the atmospheric background hypothesis, which leads us to set limits on the nature of neutrino transient sources with emission timescales of one month.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

DNTTD (Distributed Non-Negative Tensor Train Decomposition)

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exa flops also come the extra-large high-dimensional data generated by high performance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and real world big datasets.

Bhattarai, Manish↗
DOE OSTI2020

Accounting for Training Data Error in Machine Learning Applied to Earth Observations

Remote sensing, or Earth Observation (EO), is increasingly used to understand Earth system dynamics and create continuous and categorical maps of biophysical properties and land cover, especially based on recent advances in machine learning (ML). ML models typically require large, spatially explicit training datasets to make accurate predictions. Training data (TD) are typically generated by digitizing polygons on high spatial-resolution imagery, by collecting in situ data, or by using pre-existing datasets. TD are often assumed to accurately represent the truth, but in practice almost always have error, stemming from (1) sample design, and (2) sample collection errors. The latter is particularly relevant for image-interpreted TD, an increasingly commonly used method due to its practicality and the increasing training sample size requirements of modern ML algorithms. TD errors can cause substantial errors in the maps created using ML algorithms, which may impact map use and interpretation. Despite these potential errors and their real-world consequences for map-based decisions, TD error is often not accounted for or reported in EO research. Here we review the current practices for collecting and handling TD. We identify the sources of TD error, and illustrate their impacts using several case studies representing different EO applications (infrastructure mapping, global surface flux estimates, and agricultural monitoring), and provide guidelines for minimizing and accounting for TD errors. To harmonize terminology, we distinguish TD from three other classes of data that should be used to create and assess ML models: training reference data, used to assess the quality of TD during data generation; validation data, used to iteratively improve models; and map reference data, used only for final accuracy assessment. We focus primarily on TD, but our advice is generally applicable to all four classes, and we ground our review in established best practices for map accuracy assessment literature. EO researchers should start by determining the tolerable levels of map error and appropriate error metrics. Next, TD error should be minimized during sample design by choosing a representative spatio-temporal collection strategy, by using spatially and temporally relevant imagery and ancillary data sources during TD creation, and by selecting a set of legend definitions supported by the data. Furthermore, TD error can be minimized during the collection of individual samples by using consensus-based collection strategies, by directly comparing interpreted training observations against expert-generated training reference data to derive TD error metrics, and by providing image interpreters with thorough application-specific training. We strongly advise that TD error is incorporated in model outputs, either directly in bias and variance estimates or, at a minimum, by documenting the sources and implications of error. TD should be fully documented and made available via an open TD repository, allowing others to replicate and assess its use. To guide researchers in this process, we propose three tiers of TD error accounting standards. Finally, we advise researchers to clearly communicate the magnitude and impacts of TD error on map outputs, with specific consideration given to the likely map audience.

58 GEOSCIENCES↗
DOE OSTI2022

Activation of the plant mevalonate pathway by extracellular ATP

The mevalonate pathway plays a critical role in multiple cellular processes in both animals and plants. In plants, the products of this pathway impact growth and development, as well as the response to environmental stress. A forward genetic screen of Arabidopsis thaliana using Ca 2+ -imaging identified mevalonate kinase (MVK) as a critical component of plant purinergic signaling. MVK interacts directly with the plant extracellular ATP (eATP) receptor P2K1 and is phosphorylated by P2K1 in response to eATP. Mutation of P2K1-mediated phosphorylation sites in MVK eliminates the ATP-induced cytoplasmic calcium response, MVK enzymatic activity, and suppresses pathogen defense. The data demonstrate that the plasma membrane associated P2K1 directly impacts plant cellular metabolism by phosphorylation of MVK, a key enzyme in the mevalonate pathway. The results underline the importance of purinergic signaling in plants and the ability of eATP to influence the activity of a key metabolite pathway with global effects on plant metabolism.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

A practical guide to unbinned unfolding

Unfolding, in the context of high-energy particle physics, refers to the process of removing detector distortions in experimental data. The resulting unfolded measurements are straightforward to use for direct comparisons between experiments and a wide variety of theoretical predictions. For decades, popular unfolding strategies were designed to operate on data formatted as one or more binned histograms. In recent years, new strategies have emerged that use machine learning to unfold datasets in an unbinned manner, allowing for higher-dimensional analyses and more flexibility for current and future users of the unfolded data. This guide comprises recommendations and practical considerations from researchers across a number of major particle physics experiments who have recently put these techniques into practice on real data.

Canelli, Florencia [Univ. of Zurich (Switzerland)]↗
DOE OSTI2025

Tomographic assessment of peening for metallurgical bonding in cold spray

It is widely asserted that peening densifies cold spray coatings and improves particle–particle and particle–substrate bonding, yet systematic studies analyzing the evolution of bonding due to peening particle strikes are scarce. We explore peening at the level of individual particle impacts, by creating two-particle Cu-on-Cu stacks using laser-induced particle impact testing. Exploring impact velocities that range from the onset of bonding at the “critical velocity”, V cr , to its peak near 1.4⋅V cr , we perform full 3D tomography of the particle stacks to characterize the bonding state at the particle-substrate interface. Direct labeling of non-bonded patches and the total particle-substrate interface permits quantification and comparison of bonding in single impacts with bonding after subsequent impacts to reveal the origins of the peening effect. The data reveal a surprising finding: although a second impact directly atop the first particle increases the area of the particle-substrate interface (~40% area increase), there is no improvement in metallurgical bonding. Analysis of these data suggest that not only area expansion (and associated oxide rarefication), but also stress-induced extrusion of metal at that interface needs to occur to achieve metal-on-metal contact to see an enhancement in bonding. Furthermore, our analysis suggests that the subsequent impacting particle needs to have ~70% more kinetic energy to produce additional bonding; this implies that particle and velocity distributions within a spray may be helpful, and that peening may rely on unaligned impacts.

Bonding↗