Towards a next generation of NASA fire information system: Harnessing the full power of NASA fire data and models
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fire emissions↗
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Results for “power system data”
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.
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FPGA-based computing system for processing data in size, weight, and power constrained environments
Technologies that are well-suited for use in size, weight, and power (SWAP)-constrained environments are described herein. A host controller dispatches data processing instructions to hardware acceleration engines (HAEs) of one or more field programmable gate arrays (FPGAs) and further dispatches data transfer instructions to a memory controller, such that the HAEs perform processing operations on data stored in local memory devices of the HAEs in parallel with other data being transferred from external memory devices coupled to the FPGA(s) to the local memory devices.
Napier, Matthew↗
CyberDep: Enhanced Generation of Bayesian Networks Through The Inclusion of Bidirectional Data Flow Dependencies in Cyber-Physical Power Systems
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Al-Homoud, Leen [Texas A&M University]↗
Bayesian High-Rank Hankel Matrix Completion for Nonlinear Synchrophasor Data Recovery
Phasor measurement units (PMUs) provide high temporal-resolution synchrophasor measurements for power system monitoring and control. The frequent data quality issues, such as missing and bad data, prevent the incorporation of synchrophasor data in real-time operations. Most existing data-driven data recovery methods assume the power system dynamics can be approximated by a linear dynamical system, and the recovery performance degrades significantly when the power system is experiencing nonlinear dynamics during significant events. Here, this paper proposes a data-driven Bayesian nonlinear synchrophasor data recovery method (Ba-NSDR) that can recover a consecutive time period of simultaneous data losses or errors across all channels, even when the underlying system is highly nonlinear. The idea is to lift the Hankel matrix of the spatial-temporal synchrophasor data to a higher dimension such that the lifted Hankel matrix is low-rank in that space and can be processed with the kernel trick. Our proposed Bayesian method then infers the probabilistic distributions of synchrophasor from the partial observations. Some distinctive features of Ba-NSDR include an uncertainty index to measure the accuracy of the recovery result and the robustness to parameter selections. Our method is verified on both synthetic and recorded event datasets.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Voltage stability smart meter for analyzing voltage data and controlling an electrical power source and/or an electric appliance
Systems and methods for voltage stability monitoring and active/reactive power support are disclosed herein. In some embodiments, a smart electric meter of an end user in a grid power system can measure the voltage supplied to the end user via the grid power system, and can analyze the voltage data to detect critical voltage characteristics. The critical voltage characteristics may indicate that a voltage collapse event is likely. The smart electric meter can further estimate a voltage stability margin based on the voltage data. If necessary, the smart electric meter can control an electrical power source and/or an electric appliance positioned at or near the end user to increase the voltage stability margin.
Min, Liang↗
Noise-Resilient Quantum Machine Learning for Stability Assessment of Power Systems
Transient stability assessment (TSA) is a cornerstone for resilient operations of todays interconnected power grids. This paper is a confluence of quantum computing, data science and machine learning to potentially address the power system TSA issue. Here, we devise a quantum TSA (QTSA) method to enable scalable and efficient data-driven transient stability prediction for bulk power systems, which is the first attempt to tackle the TSA issue with quantum computing. Our contributions are three-fold: 1) A high expressibility, low-depth (HELD) quantum circuit is designed for accurate and noise-resilient TSA; 2) A quantum natural gradient descent algorithm is developed for efficient HELD circuit training; 3) A systematical analysis on QTSAs performance under various quantum factors is per-formed. QTSA underpins a foundation of quantum-enabled and data-driven power grid stability analytics. It renders the intractable TSA straightforward and effortless in the Hilbert space, and therefore provides stability information for power system operations. Extensive experiments on quantum simulators and real quantum computers verify the accuracy, noise-resilience, scalability and universality of QTSA.
29 ENERGY PLANNING, POLICY, AND ECONOMY↗
Resilient Information Architecture Platform for Smart Grid (RIAPS)
A number of emerging trends will substantially alter the operation and control of the electric grid over the next several decades. These trends include ensuring resiliency under severe weather events, increasing integration of renewable electricity generation, supporting changing electricity demand patterns, and the improving cost effectiveness of distributed energy resources. To address these challenges, the future “Smart Grid” management will need to transition from centralized to coordinated distributed control paradigm. Reliable operation of the Smart Grid depends on distributed intelligence realized through software applications that run on distributed computing devices attached to the power system to collect data and collaboratively manage resources. However, much of the existing software for Smart Grid-enabled devices is either proprietary or developed with custom solutions, which limits interoperability among the heterogeneous devices and hinders the ability to manage system-level reliability, security, and resiliency requirements. Additionally, this approach makes Smart Grid applications hard to maintain, evolve, verify, and replace; resulting in high development and deployment costs. Further development of the Smart Grid requires a reusable software base-layer to move from hard-coded functionality to a plug-and-play architecture capable of managing system-level objectives and constraints in addition to providing consistent common services across heterogeneous devices and applications. Vanderbilt University, in collaboration with North Carolina State University and Washington State University has developed a foundation ‘software platform’ for developing and deploying robust, reliable, effective and secure software applications for the Smart Grid. The Resilient Information Architecture Platform for the Smart Grid (RIAPS) provides core services for building effective and powerful smart grid applications. It offers unique services for real-time data dissemination, fault tolerance, and coordination across apps distributed over the network.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Database Management Module Framework for Dynamic Contingency Analysis and Visualization
The Dynamic Contingency Analysis Tool (DCAT) is being developed to mimic the response of generation dynamics, protection, and load components to extreme contingencies in power systems. Currently, DCAT generates a huge data set, with the analysis results stored in multiple subfolders using a great many files in different formats (power flow cases, time series, figures, spreadsheets). To help power system engineers thoroughly understand and analyze system behavior under many scenarios and contingencies, we developed a database management module (DBM) that enables future integration of DCAT with industry-grade Big Data analytical and interactive visualization platforms. In this paper, we demonstrate the DBM framework with interactive analytics and visualization for DCAT on the synthetic South Carolina 500-bus power system model using hurricane contingencies that capture possibilities for an extreme contingency. DCAT’s advanced data management and interactive visualization can help planning engineers to visually explore and analyze system behavior, all in one view.
Cascading, dynamic simulations, database managemen↗
Revealing power, energy and thermal dynamics of a 200PF pre-exascale supercomputer
As we approach the exascale computing era, the focused understanding of power consumption and its overall constraint on HPC architectures and applications are becoming increasingly paramount. Summit, located at the Oak Ridge Leadership Computing Facility (OLCF), is one of the fastest and largest pre-exascale platforms in operation today. This paper provides a first-order examination and analysis of power consumption at the component-level, node-level, and system-level, from all 4,626 Summit compute nodes, each with over 100 metrics at 1Hz frequency over the entire year of 2020. We also investigate the power characteristics and energy efficiency of over 840k Summit jobs and 250k GPU failure logs for further operational insights. To the best of our knowledge, this is the first systematic analysis of power data of HPC system at this scale.
Shin, Woong↗
Exploring Diverse Data Sets and Developing New Theories and Ideas With Project Integration Architecture
The development of new ideas is the essence of scientific research. This is frequently done by developing models of physical processes and comparing model predictions with results from experiments. With models becoming ever more complex and data acquisition systems becoming more powerful, the researcher is burdened with wading through data ranging in volume up to a level of many terabytes and beyond. These data often come from multiple, heterogeneous sources and usually the methods for searching through it are at or near the manual level. In addition, current documentation methods are generally limited to researchers pen-and-paper style notebooks. Researchers may want to form constraint-based queries on a body of existing knowledge that is, itself, distributed over many different machines and environments and from the results of such queries then spawn additional queries, simulations, and data analyses in order to discover new insights into the problem being investigated. Currently, researchers are restricted to working within the boundaries of tools that are inefficient at probing current and legacy data to extend the knowledge of the problem at hand and reveal innovative and efficient solutions. A framework called the Project Integration Architecture is discussed that can address these desired functionalities.
Benyo, Theresa L.↗
GT SUITE Fuel Cell Model Validation with Power Module Test Data
The AES Modular Power Systems (AMPS) Fuel Cell team at the NASA Glenn Research Center (GRC) has created a Microsoft Excel model of a regenerative fuel cell (RFC) system for potential lunar and Martian applications. Due to some limitations of this model, including the ability to predict transient effects in the thermal and fluidic performance of the system, a system-level transient thermal/fluids model was desired. GT-SUITE is a transient multi-physics simulation tool that is popular in the automotive industry and has a built in template for proton exchange membrane (PEM) fuel cells. However, terrestrial fuel cells used in the automotive industry vary significantly from aerospace fuel cells. A validation effort was undertaken to compare GT SUITE model results to actual data collected during the 2015 AMPS Power Module Demonstration in which the AMPS team used a PEM fuel cell system to power a 478 kg rover assembly at the Dunes test site located at GRC. The purpose of the model was to evaluate the efficacy of using GT SUITE to model a non-flow through PEM fuel cell system. The validation effort examined electrochemical performance including fuel cell stack voltage and power supplied, the thermal performance of the stack namely stack temperature and heat rejection rates, and the fluidic performance of the system including pressures, reactant consumption, and water production rates.
Gilligan, Ryan↗
Discovery of Signatures, Anomalies, and Precursors in Synchrophasor Data with Matrix Profile and Deep Recurrent Neural Networks (Final Project Report)
The widespread deployment of phasor measurement unit (PMU) across the U.S. together with the burgeoning machine learning technology made it possible to develop data-driven PMU data analytics to improve grid security and reliability in a more insightful and effective manner. Although PMU applications have been explored for over a decade, the representative PMU usage is limited to the bulk power system monitoring mainly due to the data integrity issues associated with PMUs (typically missing, fragmented, and wrongly amplified data). To forge a breakthrough on this stalemate and embrace PMUs for power system control and protection as well, we applied various advanced machine learning and big data analysis technology to the power system event detection and classification as the first step toward the power system control and protection pertaining to grid security enhancement.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Space Technology 5: Pathfinder for Future Micro-Sat Constellations
The Space Technology 5 (ST-5) Project, currently in the implementation phase, is part of the National Aeronautics and Space Administration (NASA) s New Millennium Program (NMP). ST-5 will consist of a constellation of three miniature satellites, each with mass less than 25 kg and size approximately 60 cm by 30 cm. ST-5 addresses technology challenges, as well as fabrication, assembly, test and operations strategies for future micro-satellite missions. ST-5 will be deployed into a highly eccentric, geo-transfer orbit (GTO). This will expose the spacecraft to a high radiation environment as well as provide a low level magnetic background. A three-month flight demonstration phase is planned to validate the technologies and demonstrate concepts for future missions. Each ST-5 spacecraft incorporates NMP competitively-selected breakthrough technologies. These include Cold Gas Micro-Thrusters for propulsion and attitude control, miniature X-band transponder for space-ground communications, Variable Emittance Coatings for dynamic thermal control, and CULPRiT ultra low power logic chip used for Reed-Solomon encoding. The ST-5 spacecraft itself is a technology that can be infused into future missions. It is a fully functional micro-spacecraft built within tight volume and mass constraints. It is built to withstand a high radiation environment, large thermal variations, and high launch loads. The spacecraft power system is low-power and low-voltage, and is designed to turn on after separation &om the launch vehicle. Some of the innovations that are included in the ST-5 design are a custom spacecraft deployment structure, magnetometer deployment boom, nutation damper, X-band antenna, miniature spinning sun sensor, solar array with triple junction solar cells, integral card cage assembly containing single card Command and Data Handling and Power System Electronics, miniature magnetometer, and lithium ion battery. ST-5 will demonstrate the ability of a micro satellite to perform research-quality science. Each ST-5 spacecraft will deploy a precision magnetometer to be used both for attitude determination and as a representative science instrument. The spacecraft has been developed with a low magnetic signature to avoid interference with the magnetometer. The spacecraft will be able to detect and respond autonomously to science events, i.e. significant changes in the magnetic field measurements. The three spacecraft will be a pathfinder for future constellation missions. They will be deployed to demonstrate an appropriate geometry for scientific measurements as a constellation. They will be operationally managed as a constellation, demonstrating automation and communication strategies that will be useful for future missions. The technologies and future mission concepts will be validated both on the ground and in space. Technologies will be validated on the ground by a combination of component level and system level testing of the flight hardware in a thermal vacuum environment. In flight, specific validation runs are planned for each of the technologies. Each validation run consists of one or more orbits with a specific validation objective. This paper will describe the ST-5 mission, and the applicability of the NMP technologies, spacecraft, and mission concepts to future missions. It will also discuss the validation approach for the ST-5 technologies and mission concepts.
Carlisle, Candace↗
MBX V1.2: Accelerating Data-Driven Many-Body Molecular Dynamics Simulations
The MBX software provides an advanced platform for molecular dynamics simulations, leveraging state-of-the-art MB-pol and MB-nrg data-driven many-body potential energy functions. Developed over the past decade, these potential energy functions integrate physics-based and machine-learned many-body terms trained on electronic structure data calculated at the "gold standard" coupled-cluster level of theory. Recent advancements in MBX have focused on optimizing its performance, resulting in the release of MBX v1.2. While the inherently many-body nature of MB-pol and MB-nrg ensures high accuracy, it poses computational challenges. MBX v1.2 addresses these challenges with significant performance improvements, including enhanced parallelism that fully harnesses the power of modern multicore CPUs. In conclusion, these advancements enable simulations on nanosecond time scales for condensed-phase systems, significantly expanding the scope of high-accuracy, predictive simulations of complex molecular systems powered by data-driven many-body potential energy functions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Hybrid Solid Oxide Fuel Cell/Gas Turbine Model Development for Electric Aviation
A thermodynamic model was developed and validated to analyze a high-performance solid oxide fuel cell and gas turbine (SOFC-GT) hybrid power system for electric aviation. This study used a process simulation software package (ProMax) to study the role of SOFC design and operation on the feasibility and performance of the hybrid system. Standard modules, including compressor, turbine, heat exchanger, reforming reactor, and combustor were used from the ProMax tool suite while a custom module was created to simulate the SOFC stack. The model used an SOFC test data set as an input. Additional SOFC stack performance effects, such as pressure, temperature, and utilization of air and fuel, were added from open source data. System performance predictors were SOFC specific power, fuel-to-electricity conversion efficiency, and hybrid system efficiency. Using these input data and predictors, a static thermodynamic performance model was created that can be modified for different system configurations and operating conditions. Prior to creating the final aircraft performance model, initial demonstration models were developed to validate output results. We used the NASA SOFC model as a benchmark, which was created with their Numerical Propulsion System Simulator (NPSS) software framework. Our output results matched within 1% of both the NASA model and open source SOFC performance data. With confidence gained in the accuracy of this model, a 1-MW SOFC-GT hybrid power system was constructed for an aircraft propulsion concept. Overall hybrid system efficiencies of > 75% FTE were observed during standard 36,000 feet cruise flight conditions.
30 DIRECT ENERGY CONVERSION↗
Characterization of Oribtal Debris via Hyper-Velocity Ground-Based Tests
Existing DoD and NASA satellite breakup models are based on a key laboratory-based test, Satellite Orbital debris Characterization Impact Test (SOCIT), which has supported many applications and matched on-orbit events involving older satellite designs reasonably well over the years. In order to update and improve the break-up models and the NASA Size Estimation Model (SEM) for events involving more modern satellite designs, the NASA Orbital Debris Program Office has worked in collaboration with the University of Florida to replicate a hypervelocity impact using a satellite built with modern-day spacecraft materials and construction techniques. The spacecraft, called DebriSat, was intended to be a representative of modern LEO satellites and all major designs decisions were reviewed and approved by subject matter experts at Aerospace Corporation. DebriSat is composed of 7 major subsystems including attitude determination and control system (ADCS), command and data handling (C&DH), electrical power system (EPS), payload, propulsion, telemetry tracking and command (TT&C), and thermal management. To reduce cost, most components are emulated based on existing design of flight hardware and fabricated with the same materials. All fragments down to 2 mm is size will be characterized via material, size, shape, bulk density, and the associated data will be stored in a database for multiple users to access. Laboratory radar and optical measurements will be performed on a subset of fragments to provide a better understanding of the data products from orbital debris acquired from ground-based radars and telescopes. The resulting data analysis from DebriSat will be used to update break-up models and develop the first optical SEM in conjunction with updates into the current NASA SEM. The characterization of the fragmentation will be discussed in the subsequent presentation.
Cowardin, H.↗
Verdant Power TriFrame, Gen5 KHPS Turbine, and BOP System Content Models
This submission includes System Content Models with data following manufacture for the Verdant Power TriFrame, Gen5 Kinetic Hydropower System (KHPS) Turbines, and Balance of Plant (BOP). The TriFrame installs 3 Gen5 KHPS Turbines. The BOP system controls the turbines and interconnects the TriFrame to shoreline with supervisory control and data acquisition (SCADA).
16 TIDAL AND WAVE POWER↗
Spatiotemporal Pattern Recognition in the PMU Signals in the WECC system
Phasor measurement unit (PMU) data has been used by multiple power system applications, including state estimation, post event analysis, oscillation detection, model validation, and many others. Still, due to its big data nature and availability to general research institutions, comprehensive understanding of the spatiotemporal patterns and underlying mechanisms are incomplete. This study applies a set of signal processing and machine learning approaches aiming at deciphering the characteristic behaviors of multiple phasor measurement units (PMUs) attributes (e.g., voltage, frequency, rate of change of frequency, phase angle), including their auto-correlation, cross-dependence, similarities and discrepancies across units and temporal scales, and distributions of anomalies and their linkages to potential external factors such as weather events. Data analytics are applied to PMUs from the U.S. Western Electricity Coordinating Council (WECC) system. The PMU measurements, recorded events, outages, and weather extremes are all from real world datasets. The findings from the study and mechanistic understanding of the PMU dynamics help provide guidance on system control or preventing blackouts. The derived metrics can be directly used for adjusting or filtering simulated PMU data used for advanced algorithm development.
Hou, Zhangshuan↗