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At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring

Residential buildings with the ability to monitor and control their net-load (sum of load and generation) can provide valuable flexibility to power grid operators. We present a novel multiclass nonintrusive load monitoring (NILM) approach that enables effective net-load monitoring capabilities at high-frequency with minimal additional equipment and cost. The proposed machine learning based solution provides accurate multiclass state predictions while operating at a faster timescale (able to provide a prediction for each 60- Hz ac cycle used in US power grid) without relying on event-detection techniques. We also introduce an innovative hybrid classification-regression method that allows for the prediction of not only load on/off states but also individual load operating power levels. A test bed with eight residential appliances is used for validating the NILM approach. Results show that the overall method has high accuracy, good scaling and generalization properties.

feature extraction↗
DOE OSTI2020

Second Generation Readout For Large Format Photon Counting Microwave Kinetic Inductance Detectors

We present the development of a second generation digital readout system for photon counting microwave kinetic inductance detector (MKID) arrays operating in the optical and near-infrared wavelength bands. Our system retains much of the core signal processing architecture from the first generation system but with a significantly higher bandwidth, enabling the readout of kilopixel MKID arrays. Each set of readout boards is capable of reading out 1024 MKID pixels multiplexed over 2 GHz of bandwidth; two such units can be placed in parallel to read out a full 2048 pixel microwave feedline over a 4 GHz–8 GHz band. As in the first generation readout, our system is capable of identifying, analyzing, and recording photon detection events in real time with a time resolution of order a few microseconds. Here, we describe the hardware and firmware, and present an analysis of the noise properties of the system. We also present a novel algorithm for efficiently suppressing IQ mixer sidebands to below −30 dBc.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Robust Event Classification Using Imperfect Real-world PMU Data

Here, this paper studies robust event classification using imperfect real-world phasor measurement unit (PMU) data. By analyzing the real-world PMU data, we find it is challenging to directly use this dataset for event classifiers due to the low data quality observed in PMU measurements and event logs. To address these challenges, we develop a novel machine learning framework for training robust event classifiers, which consists of three main steps: data preprocessing, fine-grained event data extraction, and feature engineering. Specifically, the data preprocessing step addresses the data quality issues of PMU measurements (e.g., bad data and missing data); in the fine-grained event data extraction step, a model-free event detection method is developed to accurately localize the events from the inaccurate event timestamps in the event logs; and the feature engineering step constructs the event features based on the patterns of different event types, in order to improve the performance and the interpretability of the event classifiers. Based on the proposed framework, we develop a workflow for event classification using the real-world PMU data streaming into the system in real time. Using the proposed framework, robust event classifiers can be efficiently trained based on many off-the-shelf lightweight machine learning models. Numerical experiments using the real-world dataset from the Western Interconnection of the U.S power transmission grid show that the event classifiers trained under the proposed framework can achieve high classification accuracy while being robust against low-quality data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2025

Differential Seismic Phase Detection Probability as a Potential Discriminant of Explosions and Earthquakes

Deep learning models trained to estimate the probability of seismic P and S phases are rapidly expanding the scale of local event detections. Here, we evaluate the potential for deep learning model output phase detection probabilities to contribute to event‐type classification, particularly discrimination of single‐fired borehole explosions and earthquakes at local distances (<300 km). Motivated by the empirical success of P/S amplitude ratios, we consider the difference between P and S pick probability output from previously developed phase detection models, P prob −S prob ⁠, as a discriminant. Test data include M L ∼1–4 earthquakes and explosions observed by common seismographs in ten geologically diverse localities. Depending on the picking model and training data, binary classification using P prob −S prob with at least three stations can achieve approximately equivalent classification accuracy as P/S amplitude ratios without requiring any customization. Joint classification with P/S and P prob −S prob improves accuracy for most quality control scenarios. Pick probabilities are an efficient attribute to consider in explosion discrimination because they can be automated byproducts of event detection. They avoid the binary choice of picking or not picking weakly visible S waves common to explosions.

Duan, Chenglong [Rice Univ., Houston, TX (United S↗
DOE OSTI2020

Seismic monitoring and high-frequency noise using arrays in the Arabian Peninsula

Seismic arrays provide useful tools for regional seismic monitoring. Two small-aperture, regionally-oriented arrays, QWAR and HQAR, were deployed in Saudi Arabia and Oman in 2012 and 2016. We utilize a time-domain beampacking method, similar to frequency-wavenumber analysis, to examine the performance of the arrays in terms of slowness and azimuthal bias and event detection capabilities. Additionally, we investigate persistent ambient noise sources recorded by the arrays. We find that the arrays provide slowness vectors with biases comparable with similar-sized arrays of the International Monitoring System in other locations around the world. At QWAR, regional events of magnitude 3.0 and above are detected a majority of the time, and as magnitudes increase to 4.0 and above, the detection rate is greater than 82%. Finally, strong noise generation, primarily with slownesses characteristic of Lg waves, is found in the directions of the northern Arabian/Persian Gulf and southern Red Sea and may be a factor in event detection capabilities.

58 GEOSCIENCES↗
DOE OSTI2026

Evaluation of geophysical and anthropogenic sources of hydroacoustic noise in the Alaskan arctic

Quantifying the ocean soundscape is crucial for ocean-based seismoacoustic monitoring; it sets a baseline for the kinds and sizes of signals that can be detected above the background noise. As sea ice recedes, human activity in and around the Arctic Ocean is increasing, elevating sound levels and heightening the urgency of monitoring. Here, seven years of passive acoustic recordings from a National Oceanographic and Atmospheric Administration hydrophone in the Beaufort Sea are analyzed, focusing on frequencies between 1 and 125 Hz, a band of interest for detecting regional earthquakes and similar events. Sound is related to geophysical and anthropogenic sources, and seasonal and intraseasonal variations in the soundscape are examined. Sea ice emerges as a keystone feature of the Arctic Ocean acoustic environment, controlling or contributing to ambient sound levels at all frequencies studied. During low-ice months, sound levels are dominated by wind and sea surface waves, and in ice-covered months, wind-driven ice noise dominates. Seismic air gun surveys are prominent during low-ice periods, with sound levels decreasing with increasing distance and bathymetric complexity along the propagation path. The implications of this baseline soundscape for event detection in the Alaskan Arctic are discussed.

Niklasson, Siobhan [Los Alamos National Laboratory↗
DOE OSTI2025

Remote Sensing Improves Multi‐Hazard Flooding and Extreme Heat Detection by Fivefold Over Current Estimates

The co‐occurrence of multiple hazards is of growing concern globally as the frequency and magnitude of extreme climate events increases. Despite studies examining the spatial distribution of such events, there has been little work in examining if all relevant life threatening and damaging hazards are captured in existing hazard databases and by common hazard metrics. For example, local/regional flash flooding events are seldom captured by optical satellite instruments and are subsequently excluded from global hazard databases. Similarly, the heat hazard definitions most frequently used in multi‐hazard studies inherently fail to capture events that are life‐threatening but climatologically within an expected range. Our goal is to determine the potential for increasing multi‐hazard event detection capabilities by inferring additional hazard footprints from widely accessible satellite data. We use daily precipitation and temperature satellite data to develop an open‐source framework that infers additional hazard footprints that are not included in traditional methods. With the state of Texas as our study area, we detected 2.5 times as many flood hazards, equivalent to $320 million in property and crop damages. Furthermore, our expanded heat hazard definition increases the impacted area by 56.6%, equivalent to 91.5 million km 2 over an 18 year period. Increasing hazard detection capabilities and expanding existing definitions of hazards using daily satellite data increases the temporal and spatial resolutions at which multi‐hazard events are detected. Having more complete data sets of all relevant hazard extents improves our ability to track global trends and more accurately determine the magnitude of hazard exposure inequities.

equity↗
DOE OSTI2020

Detection of Anomalous Events in Electronic Health Records

Over the past decade, Health Information Technology (Health IT) has enabled an explosion in the amount of digital information stored in electronic health records (EHRs). According to recent studies, safety-related issues in healthcare can present themselves as anomalies in EHR data. Motivating examples of anomalous events in EHRs include clinical events related to invalid order cancellations or rejections, which may be initiated by clinical staff or automatic software routines in Health IT systems. Such events may be detected using anomaly detection or change point detection methods. In this paper, we explore the use of a forecasting approach to detect anomalies in EHR data using an online Support Vector Regression technique. Specifically, the proposed approach uses temporal frequency of activities in EHRs, coupled with dynamic robust confidence intervals, to characterize events as normal or anomalous. Once an event is characterized as an anomaly, our approach suppresses its effects in subsequent time intervals. The proposed approach shows encouraging results using real-world EHR data from the Veterans Affairs' corporate data warehouse

Pellett, Jordan J.↗
DOE OSTI2023

Trigger Detection for the sPHENIX Experiment via Bipartite Graph Networks with Set Transformer

Trigger (interesting events) detection is crucial to high-energy and nuclear physics experiments because it improves data acquisition efficiency. It also plays a vital role in facilitating the downstream offline data analysis process. The sPHENIX detector, located at the Relativistic Heavy Ion Collider in Brookhaven National Laboratory, is one of the largest nuclear physics experiments on a world scale and is optimized to detect physics processes involving charm and beauty quarks. Furthermore, these particles are produced in collisions involving two proton beams, two gold nuclei beams, or a combination of the two and give critical insights into the formation of the early universe. This paper presents a model architecture for trigger detection with geometric information from two fast silicon detectors. Transverse momentum is introduced as an intermediate feature from physics heuristics. We also prove its importance through our training experiments. Each event consists of tracks and can be viewed as a graph. A bipartite graph neural network is integrated with the attention mechanism to design a binary classification model. Compared with the state-of-the-art algorithm for trigger detection, our model is parsimonious and increases the accuracy and the AUC score by more than 15%.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Creation of a Mixed-Mode Fracture Network at Mesoscale Through Hydraulic Fracturing and Shear Stimulation

Enhanced Geothermal Systems could provide a substantial contribution to the global energy demand if their implementation could overcome inherent challenges. Examples are insufficient created permeability, early thermal breakthrough, and unacceptable induced seismicity. In this study we report on the seismic response of a mesoscale hydraulic fracturing experiment performed at 1.5-km depth at the Sanford Underground Research Facility. We have measured the seismic activity by utilizing a 100-kHz, continuous seismic monitoring system deployed in six 60-m length monitoring boreholes surrounding the experimental domain in 3-D. The achieved location uncertainty was on the order of 1 m and limited by the signal-to-noise ratio of detected events. These uncertainties were corroborated by detections of fracture intersections at the monitoring boreholes. Three intervals of the dedicated injection borehole were hydraulically stimulated by water injection at pressures up to 33 MPa and flow rates up to 5 L/min. We located 1,933 seismic events during several injection periods. The recorded seismicity delineates a complex fracture network comprised of multistrand hydraulic fractures and shear-reactivated, preexisting planes of weakness that grew unilaterally from the point of initiation. We find that heterogeneity of stress dictates the seismic outcome of hydraulic stimulations, even when relying on theoretically well-behaved hydraulic fractures. Once hydraulic fractures intersected boreholes, the boreholes acted as a pressure relief and fracture propagation ceased. In order to create an efficient subsurface heat exchanger, production boreholes should not be drilled before the end of hydraulic stimulations.

58 GEOSCIENCES↗
DOE OSTI2025

Simulation of Binary-single Interactions in AGN Disk. I. Gas-enhanced Binary Orbital Hardening

Stellar-mass binary black hole (BBH) mergers within the accretion disks of active galactic nuclei may contribute to gravitational wave (GW) events detected by ground-based GW detectors. In particular, the interaction between a BBH and a single stellar-mass black hole (sBH), known as the binary-single interaction (BSI) process, can potentially lead to GW events with detectable nonzero eccentricity. Previous studies of the BSI process, which neglected the effects of gas, showed that BSIs contribute non-negligibly to GW events in a coplanar disk environment. In this work, we conduct a series of two-dimensional hydrodynamical and N-body simulations to explore the BSI in a gas environment by coupling REBOUND with Athena++. We perform 360 simulation runs, spanning parameters in disk surface density Σ 0 and impact parameter b. We find that the gas-induced energy dissipation within the three-body system becomes significant if the encounter velocity between the sBHs is sufficiently large (≫c s ). Our simulation results indicate that approximately half of the end states of the BSI are changed by gas. Furthermore, at higher gas density, the number of close encounters during the BSI process will increase, and the end-state BBHs tend to be more compact. Consequently, the presence of gas may shorten the GW merger timescale for end-state BBHs and increase the three-body merger rate.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Transient Data Library of Solar Grid Integrated Distributed System

This submission contains an open-source library of transient events in distributed system with high solar PV. The library includes the collected data, related documents and scripts for loading the data. The data library is built for transient event detection and machine learning based analysis algorithm development. The data was collected via both field test and software simulation. The units for the data are included in the data file headers for each data series. A text editor or spreadsheet software, such as Excel, and Matlab is required to view the data.

algorithms↗
DOE OSTI2022

Assessment of the Electrical Substation-Grid Testbed with Inside/Outside Devices and Distributed Ledger Technology

The electrical substation-grid testbed was created to integrate the GOOSE and/or DNP (Distributed Network Protocol) messages with time synchronized sources and Distributed Ledger Technology (DLT). The objective was to study the impact of faults and cyber-events at an electrical substation with inside (protective relays) and outside (power meters) substation devices. The electrical substation-grid testbed was based on the design of a 34.5/ 12.47 kV electrical substation (sectionalized bus configuration) with two power transformers, connected to radial power lines and load feeders. The electrical substation-grid testbed was installed at 252 lab space (Advanced Power System Protection), Grid Research Integration and Deployment Center (GRID-C), Oak Ridge National Laboratory. This testbed was created for Task 5, DarkNet project. The electrical substation-grid testbed was created to simulate fault and/or cyber events that could potentially result in damage to the electrical infrastructure. In addition, tests were run that are usually not allowed to be performed in an operational electrical power grid, because these test scenarios could trip breakers and/or generate fault situations that could potentially damage equipment. The number of tests performed in the electrical substation-grid testbed were executed in a better way than in a real electrical substation and/or power grid, because multiple tests could be run in a short period of time, and complex permits, and safety/ schedule restrictions like in a real electrical substation environment were not needed. The electrical substation-grid testbed was created using real measurement, communication, and protection devices that are used by electrical utilities, to have same conditions that we could observe in a real power grid or electrical substation. The electrical substation-grid testbed was based on using a real time simulator and expansion box with amplifiers that were wired to electrical substation-grid devices. This hardware-in-the-loop (HIL) was provided by protective relays, power meters, ethernet switches, remote terminal units, synchronized timing network clock, DLT devices, workstations, and servers. This report includes the design, installation, and assessment of the electrical substation-grid testbed that was similar to an operational electrical substation, integrating the power system protection, communication, and control systems. The results for the electrical substation-grid testbed were based on:• verifying the analog signals for protective relays and power meters, • observing the synchronized time source frame at devices, • authenticating the GOOSE (IEC 61850) and DNP messages from power meters and protective relays, and • verifying the trip conditions of protective relays at fault tests with the power system fault event detection, using DLT devices. For future work, the electrical substation-grid testbed with protective relays and power meters, using DLT and synchronized time source from DarkNet, will be used to study the impact of cyber-events at inside and outside substation devices. Advanced algorithms for detecting cyber-events produced by non-desired protective relay settings will be studied, to improve the detection and reliability of protection, control, and communication systems at power grids.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Utah FORGE Project 3-2417: DAS Microseismic Event Catalog from the 16A/16B Circulation Test, 2023

This preliminary data archive includes the relocated microseismic event catalog, 1D velocity model, and methods report from DAS acquisition conducted during the Well 16A and 16B circulation test (July 19th and 20th, 2023) at Utah FORGE. The methods report describes all processing steps, including real-time event detection, hierarchical clustering, joint velocity/hypocenter inversion, and relocation. The resulting work is accepted and will be presented at IMAGE 2024. This dataset was acquired by the FOGMORE R&D project (Fiber Optic MOnitoring for Reservoir Evolution), Utah FORGE R&D Project 3-2417.

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Intelligent Energy Optimizer for Residential Buildings

Demand-side management in the buildings is essential for meeting grid flexibility needs in a highly renewable energy scenario. Appliance load monitoring helps decision making for demand-side management by providing the information on operation status/power consumption from different appliances in the buildings. Nonintrusive load monitoring (NILM) is an attractive option for appliance load monitoring using because it has lower cost for sensors and helps mitigate privacy concerns. In this study, the team used an event detection technique followed by two different methods for event classification. The results from k-means clustering showed that the events from a single appliance are often distributed in multiple clusters. Thus, the unsupervised method of NILM using k-means clustering used in this study was not very suitable for load disaggregation. The results from NILM showed that the F1 score for event classification was 0.77 for a heat pump water heater and very low for other appliances using the rule-based classification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Event-Based Analysis of Solar Power Distribution Feeder Using Micro-PMU Measurements

Solar distribution feeders are commonly used in solar farms that are integrated into distribution substations. In this paper, we focus on a real-world solar distribution feeder and conduct an event-based analysis by using micro-PMU measurements. The solar distribution feeder of interest is a behind-the-meter solar farm with a generation capacity of over 4 MW that has about 200 low-voltage distributed photovoltaic (PV) inverters. The event-based analysis in this study seeks to address the following practical matters. First, we conduct event detection by using an unsupervised machine learning approach. For each event, we determine the event’s source region by an impedancebased analysis, coupled with a descriptive analytic method. We segregate the events that are caused by the solar farm, i.e., locallyinduced events, versus the events that are initiated in the grid, i.e., grid-induced events, which caused a response by the solar farm. Second, for the locally-induced events, we examine the impact of solar production level and other significant parameters to make statistical conclusions. Third, for the grid-induced events, we characterize the response of the solar farm; and make comparisons with the response of an auxiliary neighboring feeder to the same events. Fourth, we scrutinize multiple specific events; such as by revealing the dynamics to the control system of the solar distribution feeder. The results and discoveries in this study are informative to utilities and solar power industry.

14 SOLAR ENERGY↗
DOE OSTI2021

Methods, systems, and devices for accurate signal timing of power component events

Disclosed herein are methods, systems, and devices for system event detection associated with power grid components. Methods include detecting, at a plurality of sensors, emissions from a system event associated with at least one winding of a power component. Methods also include determining, using a processor, a plurality of event parameters based, at least in part, on measurements made by the plurality of sensors, the event parameters identifying arrival times of the emissions at each of the plurality of sensors. Methods further include generating, using the processor, an output identifying an estimate of a position of the system event within the power component, the estimate being generated based, at least in part, on the arrival times identified by the plurality of event parameters.

Wu, Keshang↗