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At least 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2023

Communication-Constrained Robust Control and Learning of Grid-Connected

The electric grid of things (EGoT) promises great potential for innovative grid services by tapping into vast load flexibility. However, the unique characteristics of EGoT, being a part of the cyber-physical electric power system, present both opportunities and challenges, especially concerning supply-demand balancing, stability, and communication constraints. Traditionally, centralized control was employed to ensure balance and stability in power systems. However, with the massive influx of EGoT devices, new strategies are needed to efficiently coordinate and control these distributed devices for optimal grid operations. While some studies have explored efficiency and economic models, there remains a gap in ensuring reliability under everyday operations and resilience during extreme conditions. Addressing this gap, this project develops the technology for an Energy Service Interface (ESI) that includes novel pricing, control, learning, and distributed optimization algorithms, which will enable utilities to recruit EGoT assets for crucial grid services such as load flexibility, voltage regulation, and situation-awareness. The key novelty of the proposed technology is the careful distribution of learning and control functions across utility and EGoT asset owners such that provably efficient and resilient grid operations are attained while respecting communication and information-exchange constraints. Specifically, the project team develops machine-learning enhanced load modeling methods to allow EGoT asset owners to learn their load capability and flexibility, and develops pricing-based and decentralized learning-based control so that asset owners can coordinate to meet system-wide demand-supply balance and reliability goals. For extreme situations involving high-impact, low-probability catastrophic events (termed the “black-sky” events), the team also develops (1) a “Feeder-Operating Center-on-a-Laptop” (FOCAL) software that can assist utility personnel in leveraging EGoT assets to accelerate the service recovery of damaged feeders, and (2) distributed optimization algorithms that can coordinate the operation points of EGoT devices under severe communication constraints. The proposed technology has been extensively tested and evaluated through simulations and on a testbed. In summary, as we transition into a more interconnected and digital power grid era, our project’s findings and developments offer a pivotal step toward guaranteeing both efficiency and resilience in the face of both everyday operations and rare “black-sky” events.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

The Financial Performance of Family versus Non-Family Firms Operating in Nautical Tourism

This article analyses the financial performance of family versus non-family firms operating in nautical tourism, in 2015–2019. The sample of 39 Portuguese companies was collected from the SABI database. We use a regression of financial performance, measured by three alternative proxies: return on assets, return on equity and operating profit margin, on liquidity, leverage, turnover of assets, asset structure, company size and age. The regressions are performed across Nuts II regions on mainland and across types of firms (family and non-family). The results uncover several patterns. First, family firms are larger and older, make higher investments and therefore are less liquid. Second, liquidity, leverage and investment in tangible assets impact negatively and significantly the corporate financial performance, while the turnover of assets, size and age impacts positively and significantly. Third, the sign of the impacts depends on the measure of performance. Finally, firms in the Northern region show superior performance, which can be explained by the higher share of family firms. These findings can serve as a roadmap for managers when selecting strategies to improve performance. Additionally, they will contribute to the understanding of tourism destination dynamics and competitiveness.

Santos, Eleonora (ORCID:0000000346930804)↗

NASA NTRS1999

Spares Management : Optimizing Hardware Usage for the Space Shuttle Main Engine

The complexity of the Space Shuttle Main Engine (SSME), combined with mounting requirements to reduce operations costs have increased demands for accurate tracking, maintenance, and projections of SSME assets. The SSME Logistics Team is developing an integrated asset management process. This PC-based tool provides a user-friendly asset database for daily decision making, plus a variable-input hardware usage simulation with complex logic yielding output that addresses essential asset management issues. Cycle times on critical tasks are significantly reduced. Associated costs have decreased as asset data quality and decision-making capability has increased.

Gulbrandsen, K. A.↗

NASA NTRS2003

Web Audio/Video Streaming Tool

In order to promote NASA-wide educational outreach program to educate and inform the public of space exploration, NASA, at Kennedy Space Center, is seeking efficient ways to add more contents to the web by streaming audio/video files. This project proposes a high level overview of a framework for the creation, management, and scheduling of audio/video assets over the web. To support short-term goals, the prototype of a web-based tool is designed and demonstrated to automate the process of streaming audio/video files. The tool provides web-enabled users interfaces to manage video assets, create publishable schedules of video assets for streaming, and schedule the streaming events. These operations are performed on user-defined and system-derived metadata of audio/video assets stored in a relational database while the assets reside on separate repository. The prototype tool is designed using ColdFusion 5.0.

Guruvadoo, Eranna K.↗

NASA NTRS2003

Surveillance system and method having parameter estimation and operating mode partitioning

A system and method for monitoring an apparatus or process asset including partitioning an unpartitioned training data set into a plurality of training data subsets each having an operating mode associated thereto; creating a process model comprised of a plurality of process submodels each trained as a function of at least one of the training data subsets; acquiring a current set of observed signal data values from the asset; determining an operating mode of the asset for the current set of observed signal data values; selecting a process submodel from the process model as a function of the determined operating mode of the asset; calculating a current set of estimated signal data values from the selected process submodel for the determined operating mode; and outputting the calculated current set of estimated signal data values for providing asset surveillance and/or control.

Bickford, Randall L.↗

NASA NTRS2011

Autonomous Coordination of Science Observations Using Multiple Spacecraft

This software provides capabilities for autonomous cross-cueing and coordinated observations between multiple orbital and landed assets. Previous work has been done in re-tasking a single Earth orbiter or a Mars rover in response to that craft detecting a science event. This work enables multiple spacecraft to communicate (over a network designed for deep-space communications) and autonomously coordinate the characterization of such a science event. This work investigates a new paradigm of space science campaigns where opportunistic science observations are autonomously coordinated among multiple spacecraft. In this paradigm, opportunistic science detections can be cued by multiple assets where a second asset is requested to take additional observations characterizing the identified surface feature or event. To support this new paradigm, an autonomous science system for multiple spacecraft assets was integrated with the Interplanetary Network DTN (Delay Tolerant Network) to provide communication between spacecraft assets. This technology enables new mission concepts that are not feasible with current technology. The ability to rapidly coordinate activities across spacecraft without requiring ground in the loop enables rapid reaction to dynamic events across platforms, such as a survey instrument followed by a targeted high resolution instrument, as well as regular simultaneous observations.

Estlin, Tara A.↗

NASA NTRS2011

MaROS: Information Management Service

This software is provided by the Mars Relay Operations Service (MaROS) task to a variety of Mars projects for the purpose of coordinating communications sessions between landed spacecraft assets and orbiting spacecraft assets at Mars. The Information Management Service centralizes a set of functions previously distributed across multiple spacecraft operations teams, and as such, greatly improves visibility into the end-to-end strategic coordination process. Most of the process revolves around the scheduling of communications sessions between the spacecraft during periods of time when a landed asset on Mars is geometrically visible by an orbiting spacecraft. These relay sessions are used to transfer data both to and from the landed asset via the orbiting asset on behalf of Earth-based spacecraft operators. This software component is an application process running as a Java virtual machine. The component provides all service interfaces via a Representational State Transfer (REST) protocol over https to external clients. There are two general interaction modes with the service: upload and download of data. For data upload, the service must execute logic specific to the upload data type and trigger any applicable calculations including pass delivery latencies and overflight conflicts. For data download, the software must retrieve and correlate requested information and deliver to the requesting client. The provision of this service enables several key advancements over legacy processes and systems. For one, this service represents the first time that end-to-end relay information is correlated into a single shared repository. The software also provides the first multimission latency calculator; previous latency calculations had been performed on a mission-by-mission basis.

Allard, Daniel A.↗

NASA NTRS2010

Implementation of a Relay Coordination System for the Mars Network

Mars network relay operations involve the coordination of lander and orbiter teams through long-term and short-term planning, tactical changes and post-pass analysis. Much of this coordination is managed through email traffic and point-to-point file data exchanges. It is often difficult to construct a complete and accurate picture of the relay situation at any given moment, as there is no centralized store of correlated relay data. The Mars Relay Operations Service (MaROS) is being implemented to address the problem of relay coordination for current and next-generation relay missions. The service is provided for the purpose of coordinating communications sessions between landed spacecraft assets and orbiting spacecraft assets at Mars. The service centralizes a set of functions previously distributed across multiple spacecraft operations teams, and as such greatly improves visibility into the end-to-end strategic coordination process. Most of the process revolves around the scheduling of communications sessions between the spacecraft during periods of time when a landed asset on Mars is geometrically visible by an orbiting spacecraft. These "relay" sessions are used to transfer data both to and from the landed asset via the orbiting asset on behalf of Earth-based spacecraft operators. This paper will discuss the relay coordination problem space, overview the architecture and design selected to meet system requirements, and describe the first phase of system implementation

Allard, Daniel A.↗

NASA NTRS2015

NASA Conjunction Assessment Organizational Approach and the Associated Determination of Screening Volume Sizes

NASA is committed to safety of flight for all of its operational assets Performed by CARA at NASA GSFC for robotic satellites Focus of this briefing Performed by TOPO at NASA JSC for human spaceflight he Conjunction Assessment Risk Analysis (CARA) was stood up to offer this service to all NASA robotic satellites Currently provides service to 70 operational satellites NASA unmanned operational assets Other USG assets (USGS, USAF, NOAA) International partner assets Conjunction Assessment (CA) is the process of identifying close approaches between two orbiting objects; sometimes called conjunction screening The Joint Space Operations Center (JSpOC) a USAF unit at Vandenberg AFB, maintains the high accuracy catalog of space objects, screens CARA-supported assets against the catalog, performs OD tasking, and generates close approach data.

Conjunction↗

NASA NTRSDate not supplied

The Lunar Lab Initiative

Analogous to terrestrial Antarctic basecamps at our south pole, space exploration outposts will be a combination of habitation and science-focused assets. The Artemis Exploration Roadmap endeavors to establish a sustained human presence on the lunar south pole starting in 2028. Within the planned Artemis Base Camp, most efforts to date have focused on the habitation assets or a mixture of science and habitation assets . With the recent advent of commercial lunar landing capabilities, the trade space can be further expanded to include dedicated science focused assets that ensure adequate science capability. To that end, the Forge, an innovation team initiative created at the Johnson Space Center (JSC), explored alternative ways to increase the science capabilities within the basecamp. The Forge uses structured brainstorming and facilitation to increase innovation within the assigned team, while executing its process in a rapid turnaround fashion for ideation and design study. As a pilot study for the Forge, a multi-disciplinary team investigated a lunar lab module to augment the lunar surface plans for Artemis. This formulation study evaluated the scientific and operational considerations for this dedicated crew space that would increase the capabilities of lunar surface science operations. The trade space evaluated single lunar lander delivery versus multiple lunar lander delivery for module assembly, alternative structural designs, the science instrument outfitting with mapping to scientific goals to maximize lunar science objectives, and how the lab would be operated. The lab operations options explored a stand-alone facility, as well as a facility integrated into a larger lunar surface complex, while evaluating crew usage and habitability considerations. The resulting alternatives were evaluated by the Forge team with a set of metrics to determine their originality, feasibility, and science performance, and thereby provide a recommendation to the lunar architecture planning team. Lessons learned from the lunar lab study were compiled to improve the process and tools for future Forge studies. Forward work, open issues, and challenges to further refine the design of the various science-focused asset options were also documented. By providing a robust science capability at humanity’s furthest outpost, we can plan for a sustained human lunar presence once the initial lunar surface access capability is well established.

Lunar outpost↗

NASA NTRSDate not supplied

A New Statistical Estimate of the Radar Coverage of the Low Earth Orbit Debris Environment

For over three decades, the NASA Orbital Debris Program Office (ODPO) has used the Goldstone Orbital Debris Radar, Haystack Ultrawideband Satellite Imaging Radar (HUSIR), and Haystack Auxiliary (HAX) radar assets to collect data on the low Earth orbit (LEO) debris environment. Each radar, with its unique beamwidth, altitude and inclination coverage, and limiting size threshold, operates in a beam park mode to statistically sample the orbital debris population in LEO. Provided that these assets are shared with other users, the orbital debris data collection is not continuous; rather, intermittent data collects are acquired and sent to the NASA ODPO. To understand the sampling process conducted by each radar over time and any related observational biases, a Statistical Confirmation of Radar Uniformity or Bias (SCRUB) code has been implemented to model the coverage of these assets for informing future operations, as well as usage of the data collected from these ground-based sensors. For this analysis, Right Ascension of the Ascending Node (RAAN) is used as a metric to measure statistical coverage. A complete survey of the LEO environment is understood to be measurements that sufficiently sample all values of RAAN for each altitude-inclination pair visible from the radar asset. Regions of incompleteness or statistical bias can help inform future observation campaigns. The SCRUB tool evaluates the coverage of the LEO environment, not by examining individual objects that may pass through a sensor’s field of view (FOV), but by determining which orbit planes pass through the FOV. Once a pointing geometry for a radar site, observation time, and range extent are configured by the user, SCRUB computes the inclinations and altitudes that are visible by the sensor. For each inclination-altitude pair, there is a distinct pair of possible RAAN values, corresponding to the ascending and descending orbit passing through that point in the sensor cone. Repeating this process for all points in the beam, and for multiple time periods during an observation window, creates a matrix of all inclination-altitude-RAAN combinations that are visible during a sensor run. This process can then be repeated for all observations within a year (for an annual survey), propagating all the RAAN values to a common epoch, typically the start of a calendar year, and combining the observations to assemble a full estimate of the RAAN coverage of the LEO environment. This RAAN coverage can then be analyzed for uniformity of sampling, within a certain inclination-altitude pair, or between larger regions of the space environment. This paper provides a general overview of the radar assets utilized by ODPO, typical analysis data products assuming circular orbits, and a discussion of the algorithms that feed between modeling and measurement operations. Following the description of the algorithm, estimates of coverage using HUSIR radar data collected from multiple years are developed and compared.

Chris L Ostrom↗

NASA NTRSDate not supplied

Establishing a Lunar Surface Power Grid

The National Aeronautics and Space Administration (NASA) is seeking to develop a lunar surface power system for its Artemis missions as well as for planned future commercial lunar surface activities. This power system is composed of a number of assets which contain electrical loads and many of these contain energy resources. These assets can be connected to form a grid, which allows power to be shared between the system’s distributed energy resources. This grid can provide backup power to existing assets as well as primary power to future assets. A trade study was conducted for an Artemis scale grid, which has identified 3 kV AC as an ideal transmission voltage. Development work has also begun for a universal modular interface converter (UMIC) which connects assets and microgrids operating at 120 V DC space power to the grid. Products from this grid definition and UMIC development work will enable highly reliable and available power for Artemis and future planetary surface missions, which is key to achieving NASA’s long term vision of space exploration.

Planetary surface power↗

NASA NTRSDate not supplied

Human-Autonomy Teaming Research in Support of m:N Operations

Since 2019, the NASA and industry partners have been involved in research focused on a novel paradigm for operations of remotely piloted aircraft. This paradigm involves multiple people sharing a fleet of multiple vehicles between them. Referred to as m:N (pronounced “em-to-en”), this configuration describes a ratio where m is the number of operators and N is the number of vehicles. Through force and asset multiplication, the m:N concept seeks to enable a scalable and resilient operation of remotely crewed vehicles. The primary means of obtaining such a robust operation is through allowing a flexible crew of variable size to dynamically attend to the needs of assets in while performing real-time operator workload management. It is in that sense that assets are shared between operators: as needed (such as in events of elevated workload) an operator in an m:N context can “handoff” the responsibility for some amount of assets, nh < N, to be absorbed by the m – 1 crew members on staff. At some time later, these nh assets could be returned to their original owner or they may be further distributed to other crew if called for by the mission. During this panel, I will elaborate on the research activity undertaken by the Human-Autonomy Teaming (HAT) Laboratory at NASA Ames Research Center over the previous three years. The studies conducted by the HAT Lab range from interviews with subject matter experts, a cognitive walkthrough, a task analysis, and two simulation experiments to-date. During experimentation, pilots made use of an advanced Ground Control Station developed by the HAT Lab and industry partners to simulate m:N operations in two large, metropolitan areas of Southern California: Los Angeles and San Diego. Further experimentation planned over the next few years. Results from our research to-date indicates that pilots of a moderately sized fleet of about a dozen remotely crewed aircraft adequately maintained safety performance and situation awareness of their aircraft, even when presented with unexpected situations of heightened workload.

multi-vehicle control↗

DOE OSTI2023

Validation of Interconnection and Interoperability of Grid-Forming Inverters Sourced by Hydrogen Technologies in View of 100% Renewable Microgrids

Grid-forming assets are required in microgrids to act as voltage-frequency masters. These grid-forming assets can operate in two modes of operation: grid-following mode and grid-forming mode. In grid-following mode of operation, these assets will follow real power and reactive power setpoints and in grid-forming mode of operation these assets will follow voltage and frequency setpoints. Traditionally, diesel generators or natural gas-based generators are widely used to act as a voltage-frequency master. However, many utilities are aiming to replace generators with grid forming-inverters supplied by solar photovoltaics (PV), batteries or fuel cells. Since grid-forming assets need a long-term reliable energy source, fuel cells are a reasonable and viable choice to supply the grid-forming inverters, but some of the challenges facing the wide deployment of grid-forming fuel cell inverters need to be addressed. Specifically, in our proposed work, we aim to focus on the interconnection and interoperability requirements of grid-forming fuel cell inverters. Currently, state-of-the-art fuel cell inverters follow the general interconnection requirements of distributed energy resources (DERs) and general interoperability requirements of DERs, but these requirements were built with PV and battery systems in mind. Fuel cells have different operational requirements, and therefore these requirements need to be appropriately modified for the grid operators to use. These additional steps add to the investment and operational cost to the grid operators. Through the ARIES platform, this proposed project aims to bridge this gap and use power hardware-in-the-loop (PHIL) and controller hardware-in-the-loop (CHIL) experiments to inform the creation of open-source interconnection and interoperability information that can aid in faster and cheaper installation and operation of grid-forming fuel cell inverters.

grid-forming↗

DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Validation of Interconnection and Interoperability of Grid-Forming Inverters Sourced by Hydrogen Technologies in View of 100% Renewable Microgrids

controller hardware-in-the-loop↗

DOE OSTI2026

U.S. Distribution Transformer Demand Phase III - Key Drivers and Managing Demand [Slides]

This presentation demonstrates a significant analysis, on forecasting the demand for distribution transformers. The analysis is conducted for the United States, estimating the initial in-service capacity of these assets, and forecasting demand for these assets through 2050 with several sensitivities conducted. It examines not only demand for these assets, but importantly, how utility planning practices can impact the demand for theses assets in time. Under load growth scenarios, whether utilities practice like-for-like replacement strategies as failures occur, or whether they practice proactive up-sizing, anticipating electric load growth, can have major impacts on future demand. It also examines several other growth factors, such as the increasing demand for step-up transformers, which share many of the same characteristics as distribution transformers, and the demand for specific transformers for large project growth from data centers.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Image Provenance Analysis

The literature of multimedia forensics is mainly dedicated to the analysis of single assets (such as sole image or video files), aiming at individually assessing their authenticity. Different from this, image provenance analysis is devoted to the joint examination of multiple assets, intending to ascertain their history of edits, by evaluating pairwise relationships. Each relationship, thus, expresses the probability of one asset giving rise to the other, through either global or local operations, such as data compression, resizing, color-space modifications, content blurring, and content splicing. The principled combination of these relationships unveils the provenance of the assets, also constituting an important forensic tool for authenticity verification. This chapter introduces the problem of provenance analysis, discussing its importance and delving into the state-of-the-art techniques to solve it.

Moreira, Daniel↗