SEARCH · Engineering Papers

Results for “distribution system monitoring”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 307 records · Page 17

All sources NASA NTRS DOE OSTI

DOE OSTI2025

SolarSTARTS: Solar-Assisted State-Aware and ResilienT infrastructure System

This final technical report provides a description and results of the design, development, testing, and validation of the Automated Resilience Management System (ARMS) solution, which integrates different systems to collect, store and process relevant information from the power distribution system to automatically manage the operation of multiple energy assets to enhance the resilience of the power grid against high-impact physical incidents (e.g., hurricanes) and cyber threats. The developed solution comprises a suite of novel AI-based algorithms designed to first detect, locate, and classify anomalies by collecting and analyzing data from multiple monitoring and control devices

14 SOLAR ENERGY↗

DOE OSTI2022

Using AI for Management of Field Emission in SRF Linacs

Field emission control, mitigation, and reduction is critical for reliable operation of high gradient superconducting radio-frequency (SRF) accelerators. With the SRF cavities at high gradients, the field emission of electrons from cavity walls can occur and will impact the operational gradient, radiological environment via activated components, and reliability of CEBAF’s two linacs. A new effort has started to minimize field emission in the CEBAF linacs by re-distributing cavity gradients. To measure radiation levels, newly designed neutron and gamma radiation dose rate monitors have been installed in both linacs. Artificial intelligence (AI) techniques will be used to identify cavities with high levels of field emission based on control system data such as radiation levels, cryogenic readbacks, and vacuum loads. The gradients on the most offending cavities will be reduced and compensated for by increasing the gradients on least offensive cavities. Training data will be collected during this year’s operational program and initial implementation of AI models will be deployed. Preliminary results and future plans are presented.

Carpenter, Adam↗

DOE OSTI2021

Using AI for Management of Field Emission in SRF Linacs

Carpenter, Adam↗

DOE OSTI2021

Control approach for power modulation of end-use loads

Methods, apparatus, and systems are disclosed herein for power modulation control of power grids using end-use loads. At the beginning of a coordination period typically lasting a few minutes, a population of end-use loads (water heaters, air conditioners, plug-in electric vehicles, etc.) is interrogated for their willingness to participate in power modulation by undergoing state transitions from on to off, or off to on. Distribution system flexibilities are calculated from the sums of device powers within distribution systems. Area flexibilities are calculated from the sums of device powers within areas. Thresholds for various devices to change state in response to inter-area oscillation damping or frequency regulation are derived from these flexibilities. Two power functions are monitored in real-time and compared with the device thresholds to trigger state changes which then remain locked throughout the remainder of the coordination period, then the devices are released from the lockout state.

Lian, Jianming↗

DOE OSTI2021

Probabilistic Voltage Sensitivity based Preemptive Voltage Monitoring in Unbalanced Distribution Networks

With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.

Abujubbeh, Mohammad↗

DOE OSTI2023

A Siamese CNN + KNN-Based Classification Framework for Non-intrusive Load Monitoring

Through the development of smart grids, programs such as demand side response, have been presented as auxiliary services to the real-time operation of distributed networks. In order to provide consumers information on their energy consumption, so that a modulation in consumption is possible, non-intrusive load monitoring has been introduced as an solution to this pattern recognition problem. Non-intrusive load monitoring enables the modeling of electrical loads connected to the low-voltage system, considering only a single measurement point. Presented state-of-the-art solutions though, consider availability of data as well as representation of all possible classes of the environment. This is of course a most conservative hypothesis, since in real-life applications availability of such data is much difficult, as well as the dynamic behavior of models is implicitly evolving in time. Here, a framework that uses neural Siamese networks with k-nearest neighbor clustering is presented toward non-intrusive load monitoring. Online learning feature is implemented, which relaxes the hypothesis of data requirements as well addresses the evolving nature of load profile. k-nearest clustering allows nonlinear characteristic space modelling. Test results using synthetics and real-life data show that the solution, besides obtaining a good generalizability in the classification, also obtained results with an accuracy of 95.77%.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2024

An Overview of Renewable Energy Desk Activities for Power Grid Operations and Planning

This document summarizes how grid operators can address gaps in their planning and operations to maintain reliability as they pursue clean energy goals. When transitioning to higher renewable energy levels, many system operators configure a dedicated renewable energy desk to manage variable renewable energy resource operation. Establishing such a desk in the control room can be a key step in the modernization effort. A renewable energy desk in a control room is a specialized hub focused solely on monitoring, predicting, and managing the influx of energy from renewable sources.

ENERGY PLANNING, POLICY, AND ECONOMY,POWER TRANSMI↗

DOE OSTI2021

Characterization of Surrogate Molten Salt Reactor Aerosol Streams

Measuring the aerosol evolution from MSRs is important for monitoring the off-gas system of the reactor and is particularly important for detecting off-normal conditions. In a molten salt reactor (MSR) accident scenario, an aerosol release would be a major factor in the source term. This aerosol stream would likely be generated from a breach in the cover gas system, which causes particles produced from fission itself to escape, or from a salt spill that produces aerosols through splashing and secondary reactions. The particle size of the produced aerosols is anticipated to vary greatly and range from 0.01 to 10 µm. The transport of these aerosols would be dependent on the particle size. A better understanding of aerosol generation, size, and monitoring methods are needed to inform estimation and mitigation of potential aerosolized source terms from MSRs. While salt spill experiments are being performed at Argonne National Laboratory, the development of aerosol characterization and monitoring methods are being developed at Oak Ridge National Laboratory. To generate prototypic aerosols for use in testing monitoring instruments and mitigation methods, a surrogate aerosol stream was produced with a Collison nebulizer, and the particle size distributions were measured with a cascade impactor. The results demonstrated that by changing the nebulizer pressure, the aerosol particle size distribution can be adjusted to best match the region of interest for experiments with higher pressures, driving the particle size down. However, nearly all aerosols formed exceeded 1 µm in diameter, providing a lower bound for the surrogate aerosol stream. In addition to verifying the applicability of this surrogate aerosol stream, this work has shown that a laser induced breakdown spectroscopy monitoring system that is under development is resilient to changes in particle sizes, increasing its robustness for off-gas monitoring.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2024

Distributed water desalination and purification systems: perspective and future directions

Distributed water treatment and desalination (DWTD) systems are critical for the development of a diverse water portfolio of the desired quality and intended use at the target location. Widespread adoption of DWTD has been hampered given the need for round-the-clock monitoring and the lack of local technical expertise for system management. However, self-adaptive operation, real-time remote monitoring, supervisory control, and asset management of DWTD systems are now feasible with the implementation of advanced local system control, cyberinfrastructure that facilitates real-time cloud-based analytics, data management, and artificial intelligence–powered decision support. Such an approach will introduce transformative virtual networks of DWTD systems to provide needed water to locations that are not served by centralized and satellite water treatment and desalination systems.

Cohen, Yoram [University of California, Los Angele↗

DOE OSTI2021

Discovery of two bright high-redshift gravitationally lensed quasars revealed by Gaia

We present the discovery and preliminary characterisation of two high-redshift gravitationally lensed quasar systems in Gaia Data Release 2 (DR2). Candidates with multiple close-separation Gaia detections and quasar-like colours in WISE, Pan-STARRS, and DES are selected for follow-up spectroscopy with the New Technology Telescope. We confirm DES J215028.71-465251.3 as a $z$ = 4.130 ± 0.006 asymmetric, doubly imaged lensed quasar system and model the lensing mass distribution as a singular isothermal sphere. The system has an Einstein radius of 1.202 ± 0.005 arcsec and a predicted time delay of ~122.0 d between the quasar images, assuming a lensing galaxy redshift of $z$ = 0.5, making this a priority system for future optical monitoring. We confirm PS J042913.17+142840.9 as a $z$ = 3.866 ± 0.003 four-image quasar system in a cusp configuration, lensed by two foreground galaxies. The system is well modelled using a singular isothermal ellipsoid for the primary lens and a singular isothermal sphere for the secondary lens with Einstein radii 0.704 ± 0.006 and 0.241 ± 0.030 arcsec, respectively. A maximum predicted time delay of 9.6 d is calculated, assuming lensing galaxy redshifts of $z$ = 1.0. Furthermore, PS J042913.17+142840.9 exhibits a large flux ratio anomaly, up to a factor of 2.66 ± 0.37 in i band, that varies across optical and near-infrared wavelengths. We discuss LSST and its implications for future high-redshift lens searches and outline an extension to the search using supervised machine learning techniques.

Astronomy & Astrophysics↗

DOE OSTI2021

Report on Field Test at INL Cask Farm of Single Detector Fast Neutron Spent Fuel Cask Verification System

Detecting diversion of spent fuel elements in dry storage casks is challenging due to the thick shielding used in cask construction. Measurements on top of the cask to map the underlying arrangement of the fuel elements and looking for anomalous changes over time has proven difficult to achieve using gamma rays due to the high scattering and attenuation from the thick steel structure, weakening information on the present or absence of fuel bundles. Simulations and laboratory experiments suggest that the high-energy neutron flux (>200 keV) measured directly above each fuel bundle is sufficient to produce a position map that enables detection of the present or absence of fuel bundles, and therefore diversion of a spent fuel bundle. A single-detector spent-fuel monitoring technique based on this principle was development at the Lawrence Livermore National Laboratory (LLNL). The INL Cask Farm in the INTEC technical area at Idaho National Laboratory (INL) offers the capability to test this technique on an MC-10 storage cask which has a distribution of full and empty fuel positions. An experimental test plan for the single-detector verification system was developed in consultation with INL personnel to be completed in FY2021. Due to travel advisories related to COVID-19, the experimental test plan was adapted to enable INL personnel to carry out the measurements in consultation with LLNL personnel following shipment of the LLNL system to INL. Field test measurements of the single detector verification system were successfully carried out at the INL cask farm on September 7-9, 2021. This document summarizes results of the field test.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2025

Distributed IELI, Rebuilding IELI for Scalability

IELI is an NLP-based system designed to transform text into structured knowledge graphs, integrate domain-specific ontologies, and answer conceptual logic-based queries. This poster talks about how redesigning IELI can help address scalability and modularity challenges, as well as improving responsiveness and health monitoring of the system.

Trejo, Edwin Horacio [Sandia National Laboratories↗

DOE OSTI2020

Machine Learning-Based Prediction of Distribution Network Voltage and Sensor Allocation

Increasing penetration levels of fast-varying energy resources might negatively affect power system operation. At the same time, sensor deployment throughout distribution networks improves system awareness and enables the development of new and advanced voltage control solutions. Such control techniques rely on accurate prediction in anticipation of voltage violation scenarios. This paper analyzes various approaches to voltage prediction in a distribution system, and it is shown that combining multiple techniques into a single regressor improves its predictive power. Moreover, a two-step regressor is proposed in which initial predictions based on a global regressor are refined by local regressors; in this case, prediction errors decrease significantly. Additionally, a clustering approach is employed to perform sensor allocation so that only the most influential buses are selected for monitoring without diminishing prediction accuracy.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation

Long-term seismic monitoring of carbon capture and storage projects is needed to verify that the injected gas is safely stored in the subsurface until permanence can be assured. Conventional surface seismic monitoring techniques are usually expensive, require highly invasive surface operations, and need significant time investments on the part of personnel for both the field effort and processing the acquired data. For these reasons, permanent reservoir monitoring technologies are preferred, as they can offer a cost-effective solution for long-term monitoring. As part of the monitoring program of the Archer Daniels Midland’s large-scale injection of CO 2 in Decatur, Illinois, USA, a continuous seismic monitoring array was installed using a combination of surface orbital vibrator (SOV) sources and fiber-optic cables for distributed acoustic sensing (DAS) acquisition with the objective to build a continuous monitoring array. The aim of the presented project was to build a monitoring array and platform that integrates real-time seismic data with conventional data streams and provides continuous data analysis using dynamic computational models to deliver a comprehensive real-time assessment of subsurface conditions. It is in this context that the Intelligent Monitoring Systems and Advanced Well Integrity and Mitigation project was proposed with the objective to develop an integrated architecture that utilizes a permanent seismic monitoring network, combines the real-time geophysical and process data with reservoir flow and geomechanical models to create a comprehensive monitoring, visualization, and control system that delivers critical information for process surveillance and optimization.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Aerosol emissions from water-lean solvents for post-combustion CO 2 capture

Advanced water-lean solvents (WLS) for post-combustion CO 2 capture have been gaining interest due to their ability to reduce the parasitic penalty from energy needed for solvent regeneration. Commercial implementation of these novel CO 2 capture technologies hinges on successful control of amine emissions. RTI conducted a parametric study of fundamental and operational variables influence on overall amine aerosol and vapor emissions from our water-lean solvent eCO 2 Sol™ using our 6-kW equivalent bench-scale gas absorption system. The parametric testing used a simulated flue gas with 15 % CO 2 , 2.3–4.2 % H 2 O, and 0–6 ppm sulfite (SO 3 ) to examine the impact of the presence of aerosols to the capture performance and amine emissions from the system. The SO 3 reacts with water in the flue gas to create H 2 SO 4 , which forms liquid aerosol droplets and provide nucleation sites for growth of aerosols. Scanning Mobility Particle Sizer and Aerodynamic Particle Sizer instruments monitored the aerosol particle size distribution. Parametric testing results suggested that the presence of the aerosols in the flue gas could increase the overall amine emissions by 10X compared to the baseline emissions from WLS’s vapor pressure. Principal component analysis (PCA) and projection to latent squares (PLS) developed models to predict the aerosol-based amine emissions from process data. The predictive PLS model had a correlation coefficient (Q 2 ) of 0.92 and could predict the aerosol-based emissions from the NAS process with ±15 % accuracy (average absolute deviation, AAD). The PLS regression model also identified key variables affecting aerosol-based emissions from WLS.

42 ENGINEERING↗

DOE OSTI2021

State-of-the-art of data collection, analytics, and future needs of transmission utilities worldwide to account for the continuous growth of sensing data

Nowadays, transmission system operators require higher degree of observability in real-time to gain situational awareness and improve the decision-making process to guarantee a safe and reliable operation. Digitalization of energy systems allows utilities to monitor the system dynamic performance in real-time at fast time scales. The use of such technologies has unlocked new opportunities to introduce new data driven algorithms for improving the stability assessment and control of the system. Motivated by these challenges, a group of experts have worked together to highlight and establish a baseline set of these common concerns, which can be used as motivation to propose innovative analytics and data-driven solutions. In this document, the results of a survey on 10 transmission system operators around the world are presented and it aims to understand the current practices of the participating companies, in terms of data acquisition, handling, storage, modelling and analytics. The overall objective of this document is to capture the actual needs from the interviewed utilities, thereby laying the groundwork for setting valid assumptions for the development of advanced algorithms in this field.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

A Simple and Accurate Energy-Detector-Based Transient Waveform Detection for Smart Grids: Real-World Field Data Performance

Integration of distributed energy sources, advanced meshed operation, sensors, automation, and communication networks all contribute to autonomous operations and decision-making processes utilized in the grid. Therefore, smart grid systems require sophisticated supporting structures. Furthermore, rapid detection and identification of disturbances and transients are a necessary first step towards situationally aware smart grid systems. This way, high-level monitoring is achieved and the entire system kept operational. Even though smart grid systems are unavoidably sophisticated, low-complexity algorithms need to be developed for real-time sensing on the edge and online applications to alert stakeholders in the event of an anomaly. In this study, the simplest form of anomaly detection mechanism in the absence of any a priori knowledge, namely, the energy detector (also known as radiometer in the field of wireless communications and signal processing), is investigated as a triggering mechanism, which may include automated alerts and notifications for grid anomalies. In contrast to the mainstream literature, it does not rely on transform domain tools; therefore, utmost design and implementation simplicity are attained. Performance results of the proposed energy detector algorithm are validated by real power system data obtained from the DOE/EPRI National Database of power system events and the Grid Signature Library.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2020

Operationalizing Synchrophasors for Enhanced Grid Reliability and Asset Utilization

The report discusses the development of production-grade synchrophasor software applications. The objective is to improve system reliability and asset utilization for offline as well as real-time considerations, as per the Department of Energy (DOE) guidelines specified by Funding Opportunity Announcement (FOA) DE-FOA-0001492. On the one hand, we have developed grid management applications on how to transform synchrophasor-based monitoring into operator guidance, and commercial grade tools addressing industry needs such as NERC Reliability Standards (MOD 26, MOD 27 and MOD 33). On the other hand, with the wide industrial adoption in mind, the developed applications have been fully discussed with utility partners and installed in the utility partners site including 6 field tests. The Model Validation and Calibration software module has been integrated into GE’s PhasorAnalyticsTM. The first commercial contract has been signed even before the close date of this project. The Angle-based Grid Management (AGM), Fast Voltage Security Assessment (FastVSA), Enhanced Islanding Monitoring(EIM) and Enhanced Disturbance Management (EDM) have been integrated into GE’s Grid Stability Assessment line of applications in e-terra Suite of EMS product.

24 POWER TRANSMISSION AND DISTRIBUTION↗