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At least 1,171 records · Page 65

Comparative Analysis of ML Techniques for Data-Driven Anomaly Detection, Classification and Localization in Distribution System

High penetration of Distributed Energy Resources (DERs), fundamental load behavior changes, controllable loads, and significant increase in Electrical Vehicles (EVs) lead to complex dynamic behavior of the electric distribution system. Increasing number of components also means more measurements, more data and more data anomalies. Detecting, classifying and localizing these anomalies are important for situational awareness, and at the same time, very challenging given increasing complexity of the system. Highly accurate and high-resolution analytical techniques are needed to support anomaly detection, classification and localization (AD-C-L) for monitoring, root cause analysis and decision making. This paper provides comprehensive review and analysis of the existing spatio-temporal AD-C-L techniques within the distribution system. Challenges for specific problems in AD-C- L have been also discussed in this paper. Existing AD-C- L techniques have been categorized and synthesized for specific merits and limitations of multiple Machine Learning (ML) methodologies using common developed metrics of performance. The comparative analysis is summarized and presented with the open research challenges and path forward for future research needs.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Additive Manufacturing of Controlled Porous Elastomeric Nanocomposites for Enhanced Sensing Function

Sensors manufactured using piezoresistive elastomeric nanocomposites have a wide range of applications in many fields such as structural health monitoring, robotics, and biomedical industries. Additive manufacturing of these sensors, achieved using Direct Ink Writing (DIW), has provided numerous advantages including increased sensitivity and the fabrication of complex geometry. However, solid sensors manufactured either additively or using conventional methods, display a greater level of hysteresis, especially at strains higher than 10%, which limits their sensitivity. The solid sensors also display a greater level of piezoresistive relaxation where the resistance change with time either in a static or dynamic loading environment. Porosity can be introduced in the form of varying infill patterns and densities to reduce bulk material effects. In this paper, a lattice structure with four infill densities are investigated, and the samples are printed using a grid infill pattern. The fabricated samples are characterized using a scanning electron microscope (SEM) to validate the microstructural features and layer bonding. The pressure-sensing capability of each sensor is investigated using cyclic compression loading at various strain levels. Sensing results show that an increase in sensitivity, as well as a decrease in hysteresis with an increase in porosity, compared to solid samples (100% in-fill). The highest porosity was measured to be 62.3% with a corresponding 112% increase in the gauge factor compared to the solid bulk. These results indicate that introducing porosity using DIW is a sensible strategy to improve the piezoresistive performance of nanocomposites and to allow for the tunability of sensing capacity in pressure sensors

Sukati, Musa↗

Application of a Fiber Optic Distributed Strain Sensor System to Woven E-Glass Composite

A distributed strain sensing system utilizing a series of identically written Bragg gratings along an optical fiber is examined for potential application to Composite Armored Vehicle health monitoring. A vacuum assisted resin transfer molding process was used to fabricate a woven fabric E-glass/composite panel with an embedded fiber optic strain sensor. Test samples machined from the panel were mechanically tested in 4-point bending. Experimental results are presented that show the mechanical strain from foil strain gages comparing well to optical strain from the embedded sensors. Also, it was found that the distributed strain along the sample length was consistent with the loading configuration.

Anastasi, Robert F.↗

Development of the Multi Purpose Transportation System for the Space Launch System (SLS) Core Stage (CS) Flight Article

NASA’s Multi Purpose Transportation System (MPTS) is designed to transport the Space Launch System vehicle segments by waterway and roadway. It is tasked with transporting the vehicle from where it is manufactured to its intermediate test location and final launch destination. Its design incorporates mechanisms that release degrees of freedom to prevent excessive loading during transit and ensure a successful delivery of the vehicle to its intended destination. In addition to the CS (Core Stage) flight article, the system will also move three structural test articles and a simulated CS Pathfinder (weight, center of gravity, outer mold line dimensions, and overall length) over road terrain on four NASA centers and on the Pegasus barge. The MPTS independently supports the article at both ends while communicating as a combined unit through automated monitoring of its released degree of freedom and corrective responses. This allows the system to constrain its payload in a statically determinate manner while traversing across highly variable terrain. Multi-body simulation of the transportation route is useful to predict free-body motion within the specified travel ranges. The MPTS has completed its design and analysis developmental cycles. This unpublished paper will describe the design challenges encountered in developing this system of large scale structure which incorporates complex mechanisms. The unique techniques and methodologies developed for analytical assessment of the hardware will be also be discussed. Key results from analytical evaluations as well as ongoing performance testing will be presented.

Sandridge, Sarah↗

3D printed graphene-based self-powered strain sensors for smart tires in autonomous vehicles

The transition of autonomous vehicles into fleets requires an advanced control system design that relies on continuous feedback from the tires. Smart tires enable continuous monitoring of dynamic parameters by combining strain sensing with traditional tire functions. Here, we provide breakthrough in this direction by demonstrating tire-integrated system that combines direct mask-less 3D printed strain gauges, flexible piezoelectric energy harvester for powering the sensors and secure wireless data transfer electronics, and machine learning for predictive data analysis. Ink of graphene based material was designed to directly print strain sensor for measuring tire-road interactions under varying driving speeds, normal load, and tire pressure. A secure wireless data transfer hardware powered by a piezoelectric patch is implemented to demonstrate self-powered sensing and wireless communication capability. Combined, this study significantly advances the design and fabrication of cost-effective smart tires by demonstrating practical self-powered wireless strain sensing capability.

33 ADVANCED PROPULSION SYSTEMS↗

Baselining Activity and Stability of ORR Catalysts and Electrodes for Proton Exchange Membrane Fuel Cells for Heavy-Duty Applications

Activity and stability of electrodes with Pt and PtCo alloy catalysts supported on high surface area carbon, hereafter to as a-Pt/C and d-PtCo/C, were evaluated for heavy-duty applications. Both catalysts had nearly identical Pt loading (50-wt% Pt on carbon and 0.25 mg Pt cm −2 ) and had undergone thermal treatment to stabilize them by growing the average particle size to 4–5 nm. Both were subjected to 90,000 (90 k) standard accelerated stress tests (AST) cycles consisting of 0.6–0.95 V square wave potentials, 3-s hold at upper and lower potential limits in H 2 /N 2 at 1.5 atm, 80 °C and 100% RH. Test protocols were developed to monitor the performance losses and characterize them in terms of activity for the oxygen reduction reaction (ORR), oxygen transport in the electrode and proton transport in the membrane and cathode catalyst layer. Despite the nearly double initial ORR activity, the PtCo/C electrode degraded faster due to the leaching of Co from the catalyst that had started even before the imposition of the AST potential cycles. Commensurate with Co leaching, Co poisoning of ionomer is responsible for the inferior performance of d-PtCo/C electrode at high current densities both before and after AST.

30 DIRECT ENERGY CONVERSION↗

Thermal Insulation Testing Method and Apparatus

A test apparatus and method of its use for evaluating various performance aspects of a test specimen is disclosed. A chamber within a housing contains a cold mass tank with a contact surface in contact with a first surface of a test specimen. The first surface of the test specimen is spaced from the second surface of the test specimen by a thickness. The second surface of the test specimen is maintained at a a constant temperature by a liquid disposed within the cold mass tank. A boil-off flow rate of the gas is monitored and provided to a processor along with the temperature of the first and second surfaces of the test specimen. The processor calculates thermal insulation values of the test specimen including comparative values for heat flux and apparent thermal conductivity k-value). The test specimen may be placed in any vacuum pressure level ranging from about 0.01 millitorr to 1,000,000 millitorr with different residual gases as desired. The test specimen may be placed under a mechanical load with the cold mass tank and another factors may be imposed upon the test specimen so as to simulate the actual use conditions.

Fesmire, James E.↗

Thermal insulation testing method and apparatus

A test apparatus and method of its use for evaluating various performance aspects of a test specimen is disclosed. A chamber within a housing contains a cold mass tank with a contact surface in contact with a first surface of a test specimen. The first surface of the test specimen is spaced from the second surface of the test specimen by a thickness. The second surface of the test specimen is maintained at a desired warm temperature. The first surface is maintained at a constant temperature by a liquid disposed within the cold mass tank. A boil-off flow rate of the gas is monitored and provided to a processor along with the temperature of the first and second surfaces of the test specimen. The processor calculates thermal insulation values of the test specimen including comparative values for heat flux and apparent thermal conductivity (k-value). The test specimen may be placed in any vacuum pressure level ranging from about 0.01 millitorr to 1,000,000 millitorr with different residual gases as desired. The test specimen may be placed under a mechanical load with the cold mass tank and another factors may be imposed upon the test specimen so as to simulate the actual use conditions.

Fesmire, James E.↗

G2 Autonomous Control for Cryogenic Delivery Systems

The Independent System Health Management-Autonomous Control (ISHM-AC) application development for cryogenic delivery systems is intended to create an expert system that will require minimal operator involvement and ultimately allow for complete autonomy when fueling a space vehicle in the time prior to launch. The G2-Autonomous Control project is the development of a model, simulation, and ultimately a working application that will control and monitor the cryogenic fluid delivery to a rocket for testing purposes. To develop this application, the project is using the programming language/environment Gensym G2. The environment is an all-inclusive application that allows development, testing, modeling, and finally operation of the unique application through graphical and programmatic methods. We have learned G2 through training classes and subsequent application development, and are now in the process of building the application that will soon be used to test on cryogenic loading equipment here at the Kennedy Space Center Cryogenics Test Laboratory (CTL). The G2 ISHM-AC application will bring with it a safer and more efficient propellant loading system for the future launches at Kennedy Space Center and eventually mobile launches from all over the world.

cryogenic fluids↗

Analysis of Process Gases and Trace Contaminants in Membrane-Aerated Gaseous Effluent Streams.

In membrane-aerated biofilm reactors (MABRs), hollow fibers are used to supply oxygen to the biofilms and bulk fluid. A pressure and concentration gradient between the inner volume of the fibers and the reactor reservoir drives oxygen mass transport across the fibers toward the bulk solution, providing the fiber-adhered biofilm with oxygen. Conversely, bacterial metabolic gases from the bulk liquid, as well as from the biofilm, move opposite to the flow of oxygen, entering the hollow fiber and out of the reactor. Metabolic gases are excellent indicators of biofilm vitality, and can aid in microbial identification. Certain gases can be indicative of system perturbations and control anomalies, or potentially unwanted biological processes occurring within the reactor. In confined environments, such as those found during spaceflight, it is important to understand what compounds are being stripped from the reactor and potentially released into the crew cabin to determine the appropriateness or the requirement for additional mitigation factors. Reactor effluent gas analysis focused on samples provided from Kennedy Space Center's sub-scale MABRs, as well as Johnson Space Center's full-scale MABRs, using infrared spectroscopy and gas chromatography techniques. Process gases, such as carbon dioxide, oxygen, nitrogen, nitrogen dioxide, and nitrous oxide, were quantified to monitor reactor operations. Solid Phase Microextraction (SPME) GC-MS analysis was used to identify trace volatile compounds. Compounds of interest were subsequently quantified. Reactor supply air was examined to establish target compound baseline concentrations. Concentration levels were compared to average ISS concentration values and/or Spacecraft Maximum Allowable Concentration (SMAC) levels where appropriate. Based on a review of to-date results, current trace contaminant control systems (TCCS) currently on board the ISS should be able to handle the added load from bioreactor systems without the need for secondary mitigation.

Gaseous Effluent Streams↗

Summary of Technical Interchange Meetings (TIMs) Designed to Enable Earth Independent Medical Operations (EIMO)

The Exploration Medical Capability Element (ExMC) in NASA’s Human Research Program hosted a series of TIMs in 2023-2024 designed to stimulate discussion around specific topics with the goal of enabling EIMO. In context of the thematic constituent elements of EIMO, namely pre-mission planning, acute/emergent/prolonged medical decision making, supply/resource management and task load management, subject matter experts from industry, academia and government (NASA and other Agencies) provided valuable and actionable guidance and recommendations. Earth-based medical experts will remain indispensable for pre-mission planning, however, management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets to enable support of astronaut health and performance and reduce overall mission risk. Key to achieving these enhancements is providing an integrated data system platform capable of utilizing multiple data streams in concert with a variety of on-board databases and passive monitoring of video and wearable sensors to enable a multi-modal, agentic AI-based clinical decision support system (CDSS) to support crew medical officer (CMO) medical decision-making. The EIMO series of TIMs (I-V) have proven to be instructive and portend a significant paradigm shift will be necessary to maintain crew health and performance on exploration class missions. Importantly, since the expected paradigm shift will be significantly different from the methods of operation that have been employed for the majority of missions from the inception of human spaceflight to date, any proposed methods must be deployed in the setting of ongoing operations early and be “tested, reviewed and practiced” while reliable back-up is available to facilitate an Enterprise-wide level of comfort and acceptance. Serious constraints on data transmission coupled with a large and expanding universe of on-board medical informatics data streams will necessitate implementation of a CDSS to supplant the current reliance on support provided by ground-based SMEs. Establishment of trust in the system by CMO/crew and the ground-based medical support team will be essential. Co-development of a CDSS with industry partners will assure that state of the art tools can be employed, and industry efficiencies can be leveraged. Training regimens, materials and tools must evolve to be responsive (just-in-time training) and facilitate autonomous execution of procedures. Proficiency metrics should be established and be based on validated competencies or milestones as opposed to a prescribed number of training hours. Training should be prioritized for broad, translatable skills that have universal application across a variety of medical conditions. Repetition was deemed to be the key to achieving proficiency and emphasis should lie in procedural training which is known to extinguish more rapidly than diagnostic skills. Advanced tools, e.g., extended reality, can provide more realistic and effective training. Use of advanced probabilistic risk assessment tools will be essential to optimize the medical system capability while carefully balancing risk relative to mass/power/volume limitations. Importance of factoring use-life of medical supplies and maintaining awareness of redundancy and opportunity to re-purpose under off nominal situations was emphasized. Consideration of adopting optimized performance standards vs. “good-enough” performance thresholds is warranted. The use of legacy systems as opposed to creating new systems may be preferable. Managing task load and associated cognitive load will be essential to maintain operational safety and behavioral health. ExMC aspires to create a shared EIMO paradigm and strategic vision for advancing medical system design through novel technologies, training, protocols, and support capabilities, built upon the spirit of successful strategies and innovations over the past six decades of space medicine operations.

Jay Lemery↗

Quantitative Effects of Disorder on Chemically Modified Amorphous Carbon Electrodes

Real materials are disordered. This disorder influences the properties of these materials and the chemical processes that occur at their interfaces. Gaining a molecular-level understanding of the underlying physical manifestations caused by disordered materials is crucial to unraveling and ultimately controlling the efficiency and performance of these materials in a range of energy-related devices. This understanding necessitates measurement techniques through which disorder can be detected, quantified, and monitored. However, such quantitative measurements are notoriously difficult, as effects often average out in ensemble measurements. In this work, we describe how a combination of electrochemical and spatially resolved surface spectroscopy measurements illuminate a molecular-level picture of disorder in materials. Using amorphous carbon as an intrinsically disordered material, we covalently attached a monolayer of ferrocene. Interfacial electron transfer across the amorphous carbon–ferrocene interface is highly sensitive to disruptions of order. Here, by systematically varying linker properties and surface loadings, the influence of lateral interactions between nonuniformly distributed ferrocene headgroups on ensemble electrochemical measurements is demonstrated. Electrochemical and imaging data collectively indicate that conformational flexibility of the ferrocene moieties provides a mechanism to elude repulsive and unbalanced lateral interactions, while rigid linkages provide direct information about the underlying disorder of the material. This study is the first of its kind to quantify and visualize molecular disorder and heterogeneity with an experimental model accessed through ensemble measurements.

36 MATERIALS SCIENCE↗

Lessons Learned from the Development and Implementation of the Atmosphere Resource Recovery and Environmental Monitoring Project

The Advanced Exploration Systems (AES) Program's Atmosphere Resource Recovery and Environmental Monitoring (ARREM) Project have been developing atmosphere revitalization and environmental monitoring subsystem architectures suitable for enabling sustained crewed exploration missions beyond low Earth orbit (LEO). Using the International Space Station state-of-the-art (SOA) as the technical basis, the ARREM Project has contributed to technical advances that improve affordability, reliability, and functional efficiency while reducing dependence on a ground-based logistics resupply model. Functional demonstrations have merged new process technologies and concepts with existing ISS developmental hardware and operate them in a controlled environment simulating various crew metabolic loads. The ARREM Project's strengths include access to a full complement of existing developmental hardware that perform all the core atmosphere revitalization functions, unique testing facilities to evaluate subsystem performance, and a coordinated partnering effort among six NASA field centers and industry partners to provide the innovative expertise necessary to succeed. A project overview is provided and the project management strategies that have enabled a multidiscipinary engineering team to work efficiently across project, NASA field center, and industry boundaries to achieve the project's technical goals are discussed. Lessons learned and best practices relating to the project are presented and discussed.

Roman, Monsi C.↗

Anomaly Detection, Localization and Classification using Drifting Synchrophasor Data Streams

With ongoing automation and digitization of the electric power system, several Phasor Measurement Units(PMUs) have been deployed for monitoring and control. PMU data can have multiple anomalies, and many of the researchers in the past have concentrated on training machine/deep learning algorithms offline for anomaly detection over PMU data (i.e., not in real time). These machine/deep learning algorithms, when trained offline on a sample rather than a population of the dataset, fail to consider the dynamic behavior of the power grid in real-time, resulting in low accuracy. Considering the dynamic behavior of the power grid (e.g., change in load, generation, distributed energy resources (DERs) switching, network, controls), the definition of data anomalies varies in time and requires online training. A fundamental challenge is to enable online (i.e., real-time) training of machine/deep learning algorithms for anomaly detection over streaming PMU data. While machine/deep learning is often desirable to manage data streams, training a deep learning algorithm over streaming PMU data is nontrivial due to changes in data statistics caused by dynamic streaming data. This paper proposes PMUNET: a novel device-level deep learning-based data-driven approach for anomaly detection, localization, and classification over streaming PMU data, using online learning and multivariate data-drift detection algorithm .Two variants of PMUNET, Dynamic data Change Driven Learning (DCDL) and Continuity Driven Learning (CDL), are proposed and compared. DCDL aims to train the deep learning algorithm whenever the definition of anomaly changes due to the power grid dynamics. On the other hand, CDL continuously trains the deep learning algorithm over the PMU data-stream. The experimental results verify that DCDL outperforms CDL and other efficient anomaly detection methods over multiple events such as faults and load/ generator/capacitor/DERs variations/switching for IEEE 14 and 39 Bus test system as well as real PMU industrial data. The result verifies that DCDL variant of PMUNET improves over existing approach with a gain of 2% - 10% in terms of accuracy, false-positive rate, and false-negative rate.

adversarial deep learning↗

Mitigating Moisture with High-R Walls

Energy-efficient building enclosures are key to decreasing energy load demand and enabling advanced space-conditioning systems for high-performance homes. One efficient enclosure design solution is high-R walls, which feature increased insulation levels as an effective solution for reducing air leakage and permeance of material layers. To increase builder confidence and encourage greater market adoption of high-R walls, Home Innovation Research Labs sought to demonstrate the long-term moisture performance of several high-R wall configurations in cold climate zones. The team monitored 22 newly constructed, occupied homes located throughout climate zones 4–7, where a substantial vapor drive to the exterior is present during the winter.

buildings↗

Expansion of the Fuel Motion Monitoring System

The Idaho National Laboratory (INL) Transient Reactor Test Facility (TREAT) Fuel Motion Monitoring System (FMMS) is a diagnostic tool used to visualize the movement and location of fuel within the nuclear reactor during transient experiments. TREAT is designed to test the behavior of nuclear fuel under accident conditions, such as a rapid power increase. The TREAT FMMS, also referred to as the “hodoscope” because of the system’s massive steel collimator component, is located on the North beam port of the reactor. While the FMMS system is capable of incorporating a total of 360 sensors, initial refurbishment efforts resulted in a array of 96 fast neutron detectors. This initial array was configured to provide a narrow (2 channels) full-length view of experiment vessels with an extended 4-channel–wide region in the center of the array capable of providing a full view of all initially planned experiments. Planning and efforts to expand the FMMS array began immediately following the restart of TREAT. An additional 96 detectors were evaluated, characterized, and prepared for installation at TREAT during the summer of 2018. Unfortunately, funding reductions forced the project towards a 5-year hiatus. In October of 2023 funding from the Department of Energy’s Nuclear Science User Facilities enabled the expansion to recommence. This report serves to document the activities performed to expand the FMMS detector array to 192 channels in order to provide a broader view of the larger and more sophisticated test capsules currently planned for future irradiation in the TREAT reactor. This expansion also included doubling the data acquisition capability, addressing the new heat load on the system, and synchronizing the time for all digital components.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Evaluation of SO2, SO4(2−) and an Updated SO2 Dry Deposition Parameterization in the United Kingdom Earth System Model

In this study we evaluate simulated surface SO2 and sulphate (SO4(2-)) concentrations from the United Kingdom Earth System Model (UKESM1) against observations from ground based measurement networks in the USA and Europe for the period 1987 to 2014. We find that UKESM1 captures the historical trend for decreasing concentrations of atmospheric SO2 and SO4(2-) in both Europe and the USA over the period 1987 to 2014. However, in the polluted regions of the eastern USA and Europe, UKESM1 over-predicts surface SO2 concentrations by a factor of 3, while under-predicting surface SO4(2-) concentrations by 25-35%. In the cleaner western USA, the model over-predicts both surface SO2 and SO4(2-) concentrations by a factor of 12 and 1.5 respectively. We find that UKESM1’s bias in surface SO2 and SO4(2-) concentrations is variable according to region and season. We also evaluate UKESM1 against total column SO2 from the Ozone Monitoring Instrument (OMI) using an updated data product. This comparison provides information about the model’s global performance, finding that UKESM1 over predicts total column SO2 over much of the globe, including the large source regions of India, China, the USA and Europe as well as over outflow regions. Finally, we assess the impact of a more realistic treatment of the model’s SO2 dry deposition parameterization. This change increases SO2 dry deposition to the land and ocean surfaces, thus reducing the atmospheric loading of SO2 and SO(sup 2-)(sub 4). In comparison with the ground-based and satellite observations, we find that the modified parameterization reduces the models over prediction of surface SO2 concentrations and total column SO2. Relative to the ground-based observations the simulated surface SO4(2-) concentrations are also reduced, while the simulated SO2 dry deposition fluxes increase.

SO2↗

Grid-connected heat pump water heater benefits for low-income households in the Southeastern United States

Energy efficient heat pump water heaters (HPWHs) reduce customer energy costs; grid-connected controls further increase HPWH value by enabling energy storage and shifting demand to cheaper, off-peak periods. However, a HPWH’s first cost premium can put it out of reach for low-income customers. This paper will present results of an ongoing study exploring the benefits of load shifting HPWHs for 24 low-income households, aiming to support increased product deployment in this demographic. The study uses EcoPort modules to shift HPWH load for a study sample consisting primarily of low-income adults and seniors in North Carolina. Building upon prior studies, the study’s HPWH operating schedules are designed to maximize shifted energy and minimize participant electricity costs based on local time-of-use rates. The study documents load profiles of grid-connected HPWHs in a less-studied demographic group and explores how certain groups may have particularly flexible loads due to their unique usage profiles. The results are relevant for HPWH product performance in the Southeast. The study will quantify HPWH load shifting performance during North Carolina’s hot summer, temperate shoulder, and occasionally sub-freezing winter seasons, and the study includes installations in conditioned, semi-conditioned, and unconditioned spaces. Finally, study results will also explore how controlled HPWHs can provide value to the regional grid by reducing seasonal peaks via demand response. Lessons learned include HPWH acceptance for low-income and senior users, best practices for maintaining HPWH connectivity among users without prior product experience, and strategies to leverage remote monitoring to ensure optimal operation and enhance HPWH reliability.

Urigwe, Daniela↗