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At least 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Modeling Subsurface Explosions Recorded on a Distributed Fiber Optic Sensor

Fiber optic distributed acoustic sensors (DAS) are becoming a widely used tool for seismic sensing. Here we examine recordings of two subsurface chemical explosions, DAG-1 and DAG-3, each of which was about one metric ton (TNT equivalent), that were recorded from a helical fiber installed in two boreholes 80 m away from the source location. Several clear phases including the initial P wave, a weak S wave, and a surface reflected P wave are observed on the helical DAS data. We estimate a velocity model using arrival times measured from the fiber. The DAS waveform data were compared with colocated accelerometers at specific depths in both frequency and time domains. Furthermore, the spectra of the DAS data matched spectra estimated from the accelerometer records. Comparisons of observed waveform shape between the accelerometer records and the fiber measurements (strain-rate) show reasonable agreement except for the data near the event depth. The DAS data and the accelerometer agreed in relative amplitudes but we had difficulties in matching absolute amplitudes, possibly due to errors in metadata. Synthetic strain-rate waveforms were calculated using a 2D wavenumber algorithm and matched the waveform shape and relative amplitudes. In general, DAS is effective at recording strong ground motions at high spatial density. Comparison of the synthetic seismograms with observed data indicate that the waveforms are not consistent with a pure isotropic explosion source and that the observed S waves originate from very near the source region.

58 GEOSCIENCES↗
DOE OSTI2024

Investigating New Particle Formation and Growth Over an Urban Location in the Eastern Mediterranean

Abstract This study investigates the new particle formation (NPF) events at an urban location in the Eastern Mediterranean. Particle size distribution, particulate chemical composition, and gaseous pollutants were monitored in Rehovot, Israel (31°53″N 34°48″E) during two campaigns: from April 29 to 3 May 2021 (Campaign 1) and from May 3 to 11 May 2023 (Campaign 2), coinciding with an intensive bonfire burning festival. The organic aerosols (OA) source apportionment identified two major factors—Hydrocarbon‐like OA and Biomass‐burning OA—as well as two secondary factors—MO‐OOA (more oxidized‐oxygenated OA) and LO‐OOA (low oxidized oxygenated OA). NPF events were frequently observed during the day (mostly well‐defined nucleation events) and at night (burst of ultrafine mode particles without any discernible growth). A condensation sink value of (9.4 ± 4.0) × 10 −3 s −1 during Campaign 1 and (14.2 ± 6.0) × 10 −3 s −1 during Campaign 2 was obtained. The daytime events were associated with enhanced sulfuric acid proxy concentrations of (2–12) × 10 6 molecules cm −3 , suggesting the role of gas‐phase photochemistry in promoting NPF. A novel approach of hybrid positive matrix factorization analysis was used to deconvolve the chemical species responsible for the observed events. The results suggest the involvement of multiple components, including ammonium sulfate and MO‐OOA, in the nucleation; Nitrate, HOA and LO‐OOA participate in the subsequent particle growth for the daytime events. Nighttime events involve only semi‐volatile species (LO‐OOA, HOA and nitrate) along with ammonium sulfate.

Meteorology & Atmospheric Sciences↗
DOE OSTI2021

Design and evaluation of a cyber‐physical testbed for improving attack resilience of power systems

Abstract A power system is a complex cyber‐physical system whose security is critical to its function. A major challenge is to model, analyse and visualise the communication backbone of the power systems concerning cyber threats. To achieve this, the design and evaluation of a cyber‐physical power system (CPPS) testbed called Resilient Energy Systems Lab (RESLab) are presented to capture realistic cyber, physical, and protection system features. RESLab is architected to be a fundamental platform for studying and improving the resilience of complex CPPS to cyber threats. The cyber network is emulated using Common Open Research Emulator (CORE), which acts as a gateway for the physical and protection devices to communicate. The physical grid is simulated in the dynamic time frame using Power World Dynamic Studio (PWDS). The protection components are modelled with both PWDS and physical devices including the SEL Real‐Time Automation Controller (RTAC). Distributed Network Protocol 3 (DNP3) is used to monitor and control the grid. Then, the design is exemplified and the tools are validated. This work presents four case studies on cyberattack and defence using RESLab, where we demonstrate false data and command injection using Man‐in‐the‐Middle and Denial of Service attacks and validate them on a large‐scale synthetic electric grid.

Sahu, Abhijeet↗
DOE OSTI2025

Real-Time Lifetime Prediction of Semiconductor Devices Using Hardware-in-the-Loop

This paper presents a unique approach to enable real-time lifespan prediction of semiconductor power modules using a Hardware-in-the-Loop (HIL) system. By integrating the module's overall loss characteristics-specifically switching and conduction losses-with a thermoelectric model of the thermal management system, this research demonstrates that the model can dynamically estimates the junction temperature profile of the semiconductor devices in response to a changing torque demand profile for the motor drive system. This capability enables continuous monitoring of the module's operational time and cumulative stress induced on the devices to compute accumulated remaining lifetime or time-to-failure (TTF). This study provides an architectural framework for the HIL system with high-fidelity component models of multiple physical domains, allowing simulation of dynamic behaviors of a closely-coupled motor drive system. The advanced real-time computation and measurement functionalities of the HIL system allow for both dynamic lifetime calculations based on simulated data and aggregate lifetime predictions utilizing historical data. Moreover, this paper details an algorithm that not only computes cumulative damage but also synthesizes these data into a comprehensive aggregated lifetime metric. This methodology can enhance the maintenance scheduling strategies and operational reliability of semiconductor devices in critical applications, ultimately extending their service life while optimizing performance.

hardware-in-the-loop (HIL)↗
DOE OSTI2020

2020 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe)

Recent proliferation of distributed energy sources in distribution or sub-transmission systems necessitates close monitoring of these three-phase power grids which typically operate under unbalanced loading conditions. Unlike the transmission systems where the network equations are commonly based on the positive sequence component models, a detailed three phase model will have to be used in implementing network applications for these systems. In the specific case of the state estimator, where measurement and parameter errors may bias the solution, bad data and parameter error detection algorithms should also be incorporated. Implementing the state estimator and error detection algorithms for three-phase systems impose additional computational burden and modifications to the state estimation code. This paper proposes a practical solution to avoid these issues by using synchronized phasor measurements and modal decoupling. The previously developed parameter error detection algorithm based on the normalized Lagrange multipliers (NLM) test is applied to the measurements independently in each mode in parallel, not only saving CPU time but also avoiding new code development for a three-phase estimator. Different parameter error scenarios are created and tested to verify the effectiveness of the proposed error detection approach.

Khalili, Ramtin↗
DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗
DOE OSTI2023

Trust Model System for the Energy Grid of Things Network Communications

Network communication is crucial in the Energy Grid of Things (EGoT). Without a network connection, the energy grid becomes just a power grid where the energy resources are available to the customer uni-directionally. A mechanism to analyze and optimize the energy usage of the grid can only happen through a medium, a communications network, that enables information exchange between the grid participants and the service provider. Security implementers of EGoT network communication take extraordinary measures to ensure the safety of the energy grid, a critical infrastructure, as well as the safety and privacy of the grid participants. With the dynamic nature of network communication of the EGoT, the information provided by the customer or the service provider can be falsified by a malicious attacker. Therefore, a trust model is necessary to monitor any abnormal activities. This paper describes a distributed trust model system that meets the need of the EGoT. This paper describes methods for evaluating and improving the distributed trust model using standard hypothesis testing metrics such as true positive, false positive, true negative, false negative, equal error rate, and F1 score. Example calculations are shown based on generated sample data.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Utah FORGE: GES Well 16A(78)-32 and Well 16B(78)-32 Stimulation Seismic Event Catalogs

This dataset contains seismic event catalogs from the hydraulic stimulation of wells 16A(78)-32 and 16B(78)-32 at the Utah FORGE site in April 2024. The data was collected by Geo Energy Suisse (GES) using a variety of seismic monitoring technologies, including 3-component (3C) geophones and distributed acoustic sensing (DAS) systems. These technologies were deployed across several locations, including wells 16A, 16B, and Delano-1, with sensor arrays at multiple depths to capture microseismic activity during the stimulations. The catalogs provide both real-time and manually checked seismic event locations, with detailed parameters such as trigger conditions, velocity models, and data acquisition settings. The dataset includes information on the stimulation stages, event rates, and hydraulic injection conditions for each well, with a report detailing the data acquisition configuration and seismic event location methodologies. Users will need to reference the included report for a complete understanding of the sensor network, data processing techniques, and accuracy considerations.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic stimulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporting electrical resistivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies. Pre-stimulation characterization of the groundwater conditions identified only two hydraulically significant fractures, neither of which transects the central portion of the testbed. Flow and pressure monitoring indicated low preexisting pore pressure conditions likely affected by the mine openings.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Complementary Subsurface Characterization Methods to Develop a Geologic Model for the EGS Collab Experiment, Sanford Underground Research Facility

The EGS (Enhanced Geothermal Systems) Collab project was performed within the Sanford Underground Research Facility (SURF) with a goal of understanding processes and evaluation of models related to hydraulic simulation of rock at depth. The present work deals with the development of Testbed 2 where experiments were conducted at a depth of 1.25 km and were located within a well-characterized testbed in a metamorphic, amphibolite host rock. A total of eleven boreholes varying in length between 10.6 m and 81.2 m were continuously cored to develop the testbed. In addition to the continuous coring of the amphibolite host rock, geophysical instrumentation supporing electrical resisivity tomography (ERT), microearthquake (MEQ) detection, and optical fiber providing distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) were installed in the monitoring boreholes, all of which produced a comprehensive complementary suite of characterization and monitoring technologies.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

Aquifer Hydraulic Testing and Characterization Plan for the Ringold Formation Unit A in the Hanford 200-ZP-1 Groundwater Operable Unit and Vicinity

This document presents a plan for testing and characterizing the hydraulic properties of the Ringold Formation, member of Wooded Island – unit A (Rwia) in the 200-ZP groundwater operable unit. The testing approach includes multiple field methods and associated analyses designed to investigate and understand aquifer hydraulic properties over a range of scales. Testing was designed to achieve specific testing objectives identified previously in the 200 ZP-1 Groundwater Operable Unit Ringold Formation Unit A Characterization Sampling and Analysis Plan (Ringold A SAP). The test plan provides necessary technical and operational detail for writing subsequent field test instruction documents specific to each testing location. Hydraulic characterization activities will proceed in a phased manner focusing on areas identified to have knowledge gaps and also critical for remedy modifications and/or selection. These activities are designed to minimize impact to pump-and-treat (P&T) operations and to use existing P&T injection and extraction wells as stress wells, and include use of the new Rwia monitoring wells installed for Ringold A SAP activities. ZP-1 hydraulic testing will include slug testing in new Rwia wells, shutdown-recovery tests in multiple Rwia and composite Ringold Formation member of Wooded Island – unit E (Rwie)-Rwia aquifer test locations, installation of new Automated Water Level Network stations in Rwia monitoring wells, characterization of P&T and barometric responses, identifying the vertical distribution of hydraulic conductivity in the Rwia and Rwie units using electromagnetic borehole flowmeter tests, and single-well tracer testing in select Rwia monitoring wells. Slug testing to be conducted during drilling is described under the Ringold A SAP. The remaining testing activities are to be conducted after well drilling and completion and are described in this test plan.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Subtask 1.5 – CO2 Injection Monitoring with an Optimized Scalable, Automated, Semipermanent Seismic Array

The scalable, automated, semipermanent seismic array (SASSA) method is a flexible and relatively cost-effective surface geophysical method for regular time-lapse monitoring of the movement of injected carbon dioxide (CO2) in a reservoir for CO2 enhanced oil recovery (EOR) or geologic CO2 storage operations. It has the advantages of a low-environmental-footprint while monitoring regions of a reservoir from the surface without the need for a regular grid distribution of receivers. Automated data collection is possible. As only time-lapse amplitude changes at the reservoir level due to CO2 movement within the reservoir are monitored, the turnaround time to deliver results from the SASSA method can be short, without the need for long, time-consuming data-processing workflows. As data is collected and processed, incremental information can be provided to the field operator. The Energy & Environmental Research Center (EERC) conducted a SASSA field test from September 2018 to November 2020 in a portion of the Bell Creek Field in Montana, which implemented new CO2 EOR field activities during the study period. Lessons learned from a proof-of-concept study were incorporated to improve the data quality of the SASSA method and demonstrate the viability of the technology. The EERC implemented several enhancements to improve data quality, including 1) an iterative survey design, which allowed placing the receivers in strategic locations where the movement of the CO2 in the reservoir could be tracked with minimum interference by the cultural noise in the study area; 2) the use of powerful seismic sources in the form of surface orbital vibrators, and 3) data acquisition during optimal periods. History-matched reservoir simulation was performed to predict gas saturation and pressure response induced by CO2 injection in the study area. The results were compared with the SASSA-measured responses to CO2 injection as a partial validation technique. A match between the two methods was observed for most of the SASSA points predicted to have intersected a CO2 saturation change. The validated results provide confidence that the SASSA method can be used independently as a CO2 saturation monitoring technique. As data are collected and processed, incremental information can be provided to the field operator. The critical components of the SASSA workflow for a successful application of the method are the following: Iterative survey design with information about CO2 injection activities from the oilfield operator. A detailed CO2 injection plan is the key driver to select the strategic monitoring location of the SASSA sensors. After this information is incorporated in the initial distribution of sources and receivers in the study area, high-resolution satellite images are used to identify ground locations not affected by cultural noise sources, such as power lines, pipelines/flow lines, or roadways. In the next iteration of the survey design, a scouting trip to the study area is needed to understand more details of the noise sources identified in the previous step and the intensity of the field activities that can also generate noise during the monitoring. Integrating the information from the scouting trip into the survey design to select the optimum source and receiver locations is the final step. Noise attenuation. The variety of noise types during seismic monitoring of an oil field is enormous. Tailored noise characterization and processing at a node-by-node level can enhance the performance and sensitivity of the SASSA technique. Future advancements that could improve the efficiency and application of the SASSA technology include: Gaining a better understanding of the noise field produced by the seismic source to aid the choice of receiver location. Surface noise from the source can overwhelm the small signal changes due to CO2 that the SASSA method measures. Improved data-processing workflow to automatically analyze and adapt to dynamic noise conditions associated with industrial settings. This subtask was funded through the EERC–DOE Joint Program on Research and Development for Fossil Energy-Related Resources Cooperative Agreement No. DE- FE0024233.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2020

Development of HELICS-Based High-Performance Cyber-Physical Co-Simulation Framework for Distributed Energy Resources Applications

The rapid growth of distributed energy resources (DERs) has prompted increasing interest in the monitoring and control of DERs through hybrid smart grid communications resulting in the typical smart grid cyber-physical system. To fully understand the interdependency between them, we propose to integrate the Network Simulator 3 (NS3) into the High Engine for Large-Scale Infrastructure Co-Simulation (HELICS), a new open-source, cyber-physical-energy co-simulation platform. This paper aims to the development and case study of the HELICS-based high performance distribution-communication co-simulation framework for the DER coordination. The novel co-simulation framework for the NS3 integrating into the HELICS is developed. The DER monitoring application about hybrid smart grid communication network design is simulated and validated on this proposed HELICS-based cyber-physical co-simulation platform.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2025

End-to-end microgrid protection using distributed data-driven methods

This paper introduces an end-to-end microgrid protection framework that offers real-time system monitoring, fault-related decision making, and circuit breaker control. This is achieved through the design of distributed data-driven techniques based on the support vector machine method, where each relay is responsible for distributed data collection, fault detection, fault localization, and fault isolation. Local communication is established among neighboring relays, fostering cooperative fault localization and isolation. This decentralized design not only reduces the computational and communication requirements but also enables the adaptability of each relay under varying operational dynamics. The proposed end-to-end protection framework was validated using MATLAB/Simulink simulations on a 100% renewable microgrid, achieving an accuracy of 93.1% with response time of 0.0523 s, in protecting against a range of fault scenarios that are characterized by various types, locations, impedances, load conditions, photovoltaic power levels, and microgrid operating modes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Efficient Anomaly Detection Driven By Different Machine Learning Architectures And Models

The rapid growth and ubiquitous adoption of the internet and cyber-physical systems (CPS) have fundamentally transformed modern communication, work, and human-system interactions. While networks now form the backbone of critical digital ecosystems, enabling seamless data transmission across diverse, interconnected systems, this increased connectivity also expands the attack surface, making real-time detection of network intrusions and anomalies a pressing challenge. Detecting unusual activities within network infrastructure requires advanced data traffic analysis to differentiate between legitimate and malicious interactions. Traditional approaches to network anomaly detectionâ??such as rule-based and signature-based systemsâ??often depend on predefined patterns to identify known anomalies, limiting their effectiveness against emerging, stealthy, or previously unseen threats. These conventional methods suffer from high false alarm rates and fail to adapt to the ever-evolving nature of network traffic, particularly in large-scale, decentralized environments where data volume, velocity, and variety are constantly increasing. This dissertation presents artificial intelligence (AI)-driven approaches to anomaly detection that leverage graphics processing unit (GPU)-enabled high-performance computing (HPC) platforms for processing massive network traffic data and monitoring the components of cyber-physical systems (CPS) for potentially hazardous conditions. The research advances several key contributions: (1) Designing efficient machine learning techniques for CPS condition monitoring and anomaly detection; (2) enabling federated learning (FL) frameworks that enable distributed detection while preserving data privacy and system resilience; (3) exploring graph-based methodologies combining graph neural networks (GNN) and graph machine learning (ML) approaches for the Internet of Things (IoT) and automotive network security, and (4) performing distributed edge computing optimizations that integrate FL with scalable technologies for reduced communication overhead. Through extensive experiments, these methodologies demonstrate that complex anomaly detection and condition monitoring tasks can be achieved while balancing computational efficiency and detection accuracy through fine-grained network information processing. The frameworks developed in this research establish a robust foundation for network anomaly detection, providing scalable, adaptive, and privacy-preserving solutions for safeguarding CPS and IoT networks in an increasingly interconnected digital landscape. The practical implications of these research findings are significant, as they can inform the development of next-generation network security systems and contribute to the protection of critical infrastructure against sophisticated cyber attacks.

Marfo, William↗
DOE OSTI2021

Joint impedance and facies inversion of time-lapse seismic data for improving monitoring of CO 2 incidentally stored from CO 2 EOR

Time-lapse seismic monitoring is an effective and proven technology for mapping the distribution of CO 2 in a subsurface reservoir. When injected CO 2 displaces other reservoir fluids, porous-medium properties are changed and thus the seismic impedance changes, causing time-lapse seismic amplitude differences in the injection zones. The analysis and interpretation of images created from these amplitude differences can provide information about reservoir architecture and the CO 2 migration within the reservoir. Incorporating seismic inversion and rock physics into the interpretation of time-lapse seismic data can considerably improve the modeling and monitoring to detect and assess the location of CO 2 over time. The joint inversion method presented in this paper has an integral representation of the geology in the inversion algorithm using elastic facies, which provides information about the spatial distribution of the geologic heterogeneities controlling the movement of fluids in the reservoir. The method was successfully applied to time-lapse seismic data from a mature oil field undergoing CO 2 enhanced oil recovery. The estimated seismic acoustic impedances and facies reflect the characteristics of individual geologic facies and fluid conditions of the reservoir subject to CO 2 injection. Finally, the probabilities estimated by the joint impedance and facies inversion for the reservoir's litho-fluid facies can be used for forecasting CO 2 saturation and pressure changes within the target reservoir.

4D seismic↗
DOE OSTI2023

Bayesian GAN-Based False Data Injection Attack Detection in Active Distribution Grids With DERs

Advancements in information and communication technologies have revolutionized monitoring and control capabilities within smart grids. However, it also brings new vulnerabilities to data acquisition systems and state estimation functions, which attackers can subtly tamper with the measurement data through compromising the communication network. Moreover, the high penetration of renewable energy sources with the inherited characteristics of uncertainty and variability further complicates the design of effective intrusion detection systems. In this paper, a Bayesian deep learning-based approach is developed to detect cyber attacks and maintain the security of smart grids. Our method specifically addresses the prevalent issue of imbalanced data in real power systems, which arises from the predominance of normal system operations over compromised or attacked states. Employing a novel Bayesian GAN-based technique, our approach successfully discriminates between secure and compromised measurement data, even in scenarios with significant data imbalance. Furthermore, the proposed method accommodates various practical application factors, ensuring accurate intrusion detection despite the presence of measurement noise. The feasibility and effectiveness of the proposed detection mechanism are validated by testing on IEEE 13-node and 123-node test systems. Simulation results and comparisons with literature methods demonstrate the superiority of proposed cybersecurity solutions.

Bayesian GAN↗