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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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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), Version 5. This data record spans from 1981 to 2018 using data from eight NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18 and -19. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR Version 5 products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗
DOE OSTI2026

Monthly Quality-filtered Aggregation of NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR)

This dataset contains gridded monthly Leaf Area Index (LAI) derived from the daily NOAA Climate Data Record (CDR) of AVHRR (Version 5) and VIIRS (Version 1) Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR). This data record spans from 1981 to 2024 using data from NOAA polar orbiting satellites: NOAA-7, -9, -11, -14, -16, -17, -18, -19 and S-NPP. The data are projected on a 0.05 degree x 0.05 degree global grid, as in the original CDR. The original CDR is one of the Land Surface CDR products produced by the NASA Goddard Space Flight Center (GSFC) and the University of Maryland (UMD), which is accompanied by algorithm documentation, data flow diagram and source code for the NOAA CDR Program. This dataset is in the netCDF-4 file format following ACDD and CF Conventions. This dataset has applied quality assurance information to only include "OK" data from the original CDR in the monthly aggregation.

Vermote, Eric [NASA Goddard Space Flight Center (G↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Sapflow and xylem water isotopes from Snodgrass Mountain, East River Watershed, Colorado USA

This dataset includes sapflux and stable water isotopes of soil water and xylem water for aspen, fir and spruce trees along the Snodgrass Mountain transect in the East River Watershed, Colorado USA. The purpose of generating this dataset was to understand: (1) the total flux of water being used by trees in the watershed, (2) separate the component of transpiration that was derived from recent precipitation vs. older water such as winter precipitation of groundwater and (3) understand how total water use and water sources for the trees varies between species and position on a hillslope. The data were collected from May 2019 until October 2022. The sap flux data were collected using ICT SFM1 sensors and are presented in both units of cm h^-1 and as kg h^-1 by multiplying the sap flux by the sapwood area of the tree. All sap flow data has been been corrected using estimates of wounding diameter, water content of wood and sap wood depth. The xylem water isotope data were collected approximately weekly from each of the trees instrumented with sap flux. The stems were collected and the water extracted using classic cryogenic methods. Isotope measurements were done using a Picarro 2140i analyzer. We provide an estimate of the Seasonal Origin Index for each measurements following Allen et al., 2019 (10.5194/hess-23-1199-2019) where values of -1 equate to trees relying on winter precipitation and +1 tree relying on summer precipitation. We also provide the simultaneous sap flux for each isotope measurement when this data was available. This is an update to an earlier data repository with the same name that only included 2019 data. The new dataset was posted in January 2023 and is inclusive of the original 2019 data but now includes 2021 and 2022 data. Please note there was an error in the units of transpiration in the original dataset. It was incorrectly listed as mm h-1 when it should have been kg h-1. This change was made in May 2024.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Coordinated Thermal Safety Attack and Defense on EV Battery Management Systems

Battery temperature sensor and battery current sensor data which are key sensing inputs to the Battery Management Controllers in electric vehicles, are vulnerable to possible cyber/ physical manipulation due to known vulnerabilities inherited from CAN bus technology that is used for in-vehicle communications between electronic control units that transfer sensing and control data. In this paper, we first create a simulation that enables us to evaluate impact of cyber physical attacks on electric vehicle battery management system in a controlled environment that violates thermal safety. Specifically, we emulate a Level 3 - DC fast charging system with SAE J1772/CCS, integrated with standard charging controls and thermal safety controls on EVs, and various sensing data flows. Second, we propose a coordinated current and battery temperature attack that has crippling economic, and safety impacts. Third, we quantify the usability, economic and safety impacts of such attacks as a function of the extent of data manipulation. Finally, we propose a physics model driven detection technique to detect presence of such attacks.

25 ENERGY STORAGE↗
DOE OSTI2021

Improving Performance of Tape Restore Request Scheduling in the Storage System dCache

Given the anticipated increase in the amount of scientific data, it is widely accepted that primarily disk based storage will become prohibitively expensive. Tape based storage, on the other hand, provides a viable and affordable solution for the ever increasing demand for storage space. Coupled with a disk caching layer that temporarily holds a small fraction of the total data volume to allow for low latency access, it turns tape based systems into active archival storage (write once, read many) that imposes additional demands on data flow optimization compared to traditional backup setups (write once, read never). In order to preserve the lifetime of tapes and minimize the inherently higher access latency, different tape usage strategies are being evaluated. As an important disk storage system for scientific data that transparently handles tape access, dCache is making efforts to evaluate its recall optimization potential and is introducing a proof-of-concept, high-level stage request scheduling component within its SRM implementation.

Morschel, Lea↗
DOE OSTI2021

Open Radiation Monitoring: Histogram Builder Module Design

The Open Radiation Monitoring Project seeks to develop and demonstrate a modular radiation detection architecture designed specifically for use in arms control treaty verification (ACTV) applications that will facilitate rapid development of trusted systems to meet the needs of potential future treaties. A modular architecture can be used to reduce more complex systems to a series of single purpose building blocks, thereby facilitating equipment inspection and in turn building trust in the equipment by all treaty parties. Furthermore, a modular architecture can be used to control data flow within the measurement system, reducing the risk of "hidden switches" and constraining the amount of sensitive information that could potentially be inadvertently leaked. This report details the first revision of a prototype circuit that will convert analog pulses directly into a histogrammed data set for further processing. The circuit was designed with both spectroscopy and multiplicity analysis in mind but can, in principle, be used to reduce any raw data stream into a histogram. The number of output channels is limited, and the histogram bin ranges are user configurable to allow for non-uniform and discontinuous bins, which makes it possible to restrict the information being passed down stream if desired. Pulse processing relies entirely on analog circuitry and non- programmable logic, which enables operation without the need for a central processor or other programmable control unit. The circuit remains untested under the Open Radiation Monitoring project due to the closure of the sponsoring program. However, further development and testing is scheduled to take place in support of a purpose-built trusted verification system development effort known as COGNIZANT, which demonstrates the potential benefit of developing a suite of modular trusted system components.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors

Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.

data processing↗
DOE OSTI2023

Characterizing the performance of node-aware strategies for irregular point-to-point communication on heterogeneous architectures

Supercomputer architectures are trending toward higher computational throughput due to the inclusion of heterogeneous compute nodes. These multi-GPU nodes increase on-node computational efficiency, while also increasing the amount of data to be communicated and the number of potential data flow paths. In this work, we characterize the performance of irregular point-to-point communication with MPI on heterogeneous compute environments through performance modeling, demonstrating the limitations of standard communication strategies for both device-aware and staging-through-host communication techniques. Presented models suggest staging communicated data through host processes then using node-aware communication strategies for high inter-node message counts. Notably, the models also predict that node-aware communication utilizing all available CPU cores to communicate inter-node data leads to the most performant strategy when communicating with a high number of nodes. Furthermore, model validation is provided via a case study of irregular point-to-point communication patterns in distributed sparse matrix–vector products. Importantly, we include a discussion on the implications model predictions have on communication strategy design for emerging supercomputer architectures.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A National Infrastructure for Artificial Intelligence on the Grid (NI4AI) (Final Scientific/Technical Report)

Electric utilities have traditionally taken a very pragmatic yet myopic approach with grid sensors and the resulting collected data. Sensors are purchased and deployed to solve a specific, known problem that has risen to sufficient awareness as to justify the effort of deploying sensors and the needed capital investment. This sensor data flows into proprietary software packages with limited functionality intended only to address the initial problem. This approach aligns with the financial incentives of the utility to deploy capital into fixed hardware assets for which the corporations earn a rate of return. This mentality stands in stark contrast to the big data revolution that started nearly 25 years ago with the rise of Google. In this worldview, data is a fundamental business asset; successful organizations collect, store, explore, merge, and exploit as much data as possible to not only solve problems well understood today but also to tackle new problems that will inevitably rise tomorrow. The ARPA-E Open Innovation 2018 project entitled A National Infrastructure for Artificial Intelligence on the Grid or NI4AI for short was designed to demonstrate this alternative paradigm for using data. To do this, the project was composed of three key thrust areas. The first major component deployed a variety of high-frequency grid sensors and captured terabytes of both wide-scale and localized grid measurements, generating high-value datasets for grid research and algorithm development. The second aspect made available PingThings’ PredictiveGridTM, a horizontally scalable, cloud-based data management and AI platform built for time series data to explore and exploit the collected data. Finally, the project fostered a diverse and open research community composed of experts from numerous fields through focused educational content, code sharing, and data science competitions. Shifting away from “single use” sensors and closed data silos within electric utilities is a major benefit to the public at large. This legacy approach to data is incredibly (1) capital intensive (new sensors must be deployed for each new problem and problems tend to arise continuously) and (2) painfully slow (new problems must be identified first and then new sensors must be deployed to collect data to begin to address the issue). The transition to a carbon neutral grid requires a massive transformation of the existing grid infrastructure and will continue to challenge the legacy grid in unforeseen ways. The only way to make the energy transition cost effective is for utilities to abandon this dated data paradigm and adopt more contemporary approaches. NI4AI has shown that it is technically possible and economically feasible to ingest, explore, and exploit grid data collected from even very high frequency sensing, such as continuous point on wave sensors collecting measurements 10,000 times a second. In fact, the PredictiveGrid platform used is commercially available and deployed at several utilities in the United States. Project accomplishments were numerous and included (1) making available a state of the art time series platform to the community, (2) collecting over 520 streams of time series data from grid sensors totaling over 1 trillion grid measurements, and (3) developing and nurturing a community within the industry focused on the use of data to create value for utilities and, ultimately, end consumers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Core Model Proposal #410: Updates to Socioeconomic and Macroeconomic Data, Processing Structure, and Visualization

This Core Model Proposal (CMP) comprehensively restructures and updates the macroeconomic and socioeconomic modules in gcamdata. It includes visualizations of key data inputs, accounting identities, and data flows in the context of GCAM-Macro-KLEM. Major improvements include: (1) updating the Penn World Table (PWT) to version 10 and incorporating a new source, the Global Macro Database (GMD); (2) updating the SSP socioeconomics database from version 3.0.1 to 3.2; (3) introducing SSP-specific differentiation of employment and labor force data; (4) improving data integration between national accounts (from PWT, GMD, and GTAP) and GDP/population data from external sources; and (5) general data cleaning and structural refinements. We document the data sources and key assumptions used throughout the processing. These updates establish the foundation for the forthcoming KLEAM version of GCAM-macro.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Real-time streaming tomographic reconstruction with on-demand data capturing and 3D zooming to regions of interest

Complex dynamic tomographic experiments at brilliant X-ray light sources require real-time feedback on the sample changes with respect to environmental conditions, selecting representative regions of interest for high-resolution scanning, and on-demand data saving mechanisms for storing only relevant projections acquired by fast area detectors and reducing data volumes. Here the implementation details of a 3D real-time imaging monitoring instrument, with zooming to a volume of interest with easy-to-use visualization via ImageJ , a tool familiar to most beamline users, is presented. The instrument relies on optimized data flow between the detector and processing machines and is implemented on commodity computers. The instrument has been developed at beamline 2-BM of the Advanced Photon Source, where the automatic lens changing mechanism for zooming is implemented with an Optique Peter microscope. Performance tests demonstrate the ability to process more than 3 GB of projection data per second and generate real-time 3D zooming with different magnification. These new capabilities are essential for new APS Upgrade instruments such as the projection microscope under development at beamline 32-ID. The efficacy of the proposed instrument was demonstrated during an in situ tomographic experiment on ice and gas hydrate formation in porous samples.

3D zooming↗
DOE OSTI2020

AGC-4 Experiment Irradiation Monitoring Data Qualification Final Report

The Graphite Technology Development Program ran a series of six experiments to quantify the effects of irradiation on nuclear grade graphite. This report focuses on the fourth experiment, Advanced Graphite Creep 4 (AGC 4). The Advanced Reactor Development (ARD) Technology Development Office (TDO) Program for research and development activities require documentation of qualified monitoring data to design and license the first high-temperature reactor nuclear plant. Qualified data meets the requirements for use as described in the experiment planning and quality assurance documents. Failed data do not meet the requirements and provide no useable information. Trend data may not meet all requirements but still provide some useable information. Use of Trend data requires assessment of how any deficiencies affect a particular use of the data. AGC-4 began with Advanced Test Reactor (ATR) Cycle 157D on May 30, 2015. After irradiating the graphite for two cycles, the capsule was removed from the reactor after ATR Cycle 158A, which ended on January 2, 2016, due to interference with another experiment. Irradiation was resumed with Cycle 162A on October 7, 2017 after the interfering experiment was removed from the reactor. AGC-4 irradiation completed by the end of Cycle 166B on January 10, 2020. Between Cycles 162A and 166B, AGC-4 capsule was removed from the reactor core during two Powered Axial Locator Mechanism (PALM) Cycles 163A and 165A to avoid overheating. All thermocouples (TCs) have functioned throughout the AGC-4 experiment. A total of 9,256,184 out of all 11,167,213 temperature records (or 82.9%) are Qualified for use by the ARD Program and 1,911,029 missing values are Failed. Argon, helium, and total gas flow data were within expected ranges, except only eight out-of-range values occurred during outages. A total of 21,240,627 out of all 22,288,5103 gas flow rates (or 92.8%) are Qualified for use by the ARD Program and 1,644,476 are Failed records mostly due to missing values. Discharge gas line moisture values were consistently low during full ATR power, except for 162B, when moisture content was increasing to more than 200 ppmv by the end of this cycle. During outages, moisture content reached as high as 700 ppmv. 1,089,941 out of a total of 1,107,331 moisture values (or 98.4%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 17,390 missing moisture values are Failed. Graphite creep specimens were subjected to one of three loads: 393, 491, or 589 lbf. For a brief period during Cycle 157D between 12:19 on June 2, 2015, and 08:23 on June 11, 2015, the load cells were wired incorrectly, resulting in missing stack load data. Missing stack loads were estimated from measured ram pressures using regression equations developed from the existing data from Cycle 157D. Estimated stack loads during this period are considered to be an accurate representation of actual load applied to the stacks. These loads deviate slightly from the planned loads. This deviation does not prevent the data from being Qualified for use but must be taken into account when analyzing the effect of load on creep. 6,095,728 out of a total of 6,403,985 moisture values (or 95.2%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 308,257 missing load values are Failed. Stack displacement increased consistently throughout the eight cycles, with total displacement reached highest value of 2.4 in by the end of irradiation. During ATR outages, a set of pneumatic rams raised the stacks of graphite creep specimens to ensure the specimens were not stuck within the test train. This stack raising was performed seven times throughout irradiation. All stacks were raised successfully each time. 4,744,974 out of a total of 6,094,513 displacement values (or 77.9%) are within the measurement range of the instrument and are Qualified for use by the ARD Program and 1,349,539 displacement values are Failed mostly due to missing values. Analyses were conducted on correlations between TCs to look for trends and step changes that might indicate instrument degradation or failure. Correlation analysis was used to identify instances when TCs form short circuits, referred to as virtual junctions, which result in TCs reporting temperatures from some location in the capsule other than the location where they were intended to read. No evidence of virtual junctions was found. Analyses were also conducted on control charts of temperature differences of between two TCs, which are expected to behave consistently throughout the entire irradiation period. Upward or downward trend over time indicates at least one TC in the pair was drifted. Examining control charts for all 66 possible pairs out of twelve TCs installed in the AGC-4 capsules reveal no clear drift failures occurred, except unstable behavior of two TCs in Zone 3, TC-7 and TC-8, over irradiation time. In conclus

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Portable interactive visualization of large-scale simulations in geotechnical engineering using Unity3D

Development in large-scale geotechnical engineering simulation places tremendous demand for efficient visualization of such simulation data. This study presents a lightweight software tool, i.e. Geotechnical Interactive Visualization (GIV), as a solution to this challenge, which achieves efficient interactive visualization of large-scale simulations data in geotechnical engineering. Visualization data flow and algorithms specifically optimized for common geotechnical engineering applications are implemented in GIV. GIV can visualize geotechnical structure models with time-varying attributes attached to mesh with fixed topology, and also models with time-varying mesh topologies but no attributes attached, the two most common visualization tasks in geotechnical engineering. Furthermore, challenges for large-scale simulation data visualization, including parallel simulation data redundancy, massive data size, dynamic user interaction, and portability are overcome via specifically designed algorithms for simulation data preprocessing and optimized visualization modules using the powerful 3D rendering and interactive game engine Unity3D. Comparison of GIV with several widely used visualization tools for the visualization of large-scale idealized datasets and realistic geotechnical simulations highlights the visualization efficiency, smooth interactivity, and lightweight features of GIV.

42 ENGINEERING↗
DOE OSTI2022

Size and Distribution of Parr Produced from Natural‐ and Hatchery‐Origin Steelhead Spawning Naturally in a Small Pacific Northwest Coastal Stream

Abstract Recent studies suggest that steelhead Oncorhynchus mykiss produced from local hatchery‐origin (HOR) adults have lower lifetime fitness than their natural‐origin (NOR) counterparts. To increase our understanding of this pattern, we compared age‐1 parr size and distribution produced by a local integrated population of HOR and NOR adults spawning naturally across a range of environmental conditions. Across 8 years, we used genetic parentage assignments and field measurements of parr in conjunction with creek temperature and flow data to find small, nonbiologically significant differences in size between parr of different parent origins. This suggests that parr produced by HOR adults are acquiring enough resources to grow at a rate similar to that of parr produced by NOR adults. In contrast to origin, we found strong positive associations between mean size of parr and annual mean water temperature and summer flow. Additionally, the distribution of parr was similar and HOR‐produced parr were not skewed relative to the location of the hatchery. Parr occupying the full extent of accessible and suitable habitat might be a positive outcome for hatchery programs seeking to supplement an existing population or replace an extirpated population. However, these results also imply that HOR fish could be competing with NOR fish for food and space, which might be less desirable if the goal of the hatchery program is harvest and not supplementation. Our results highlight the importance of clearly articulated goals for the hatchery program, as the observed pattern could be deemed a benefit or a risk depending on the hatchery's purpose. Lastly, projections for higher water temperatures and reduced summer flows, when considered in the context of the correlations we observed for size of parr, imply that coastal steelhead populations could start experiencing negative climate impacts with respect to steelhead parr growth metrics during the summer in the next few decades.

Kennedy, Benjamen M.↗
DOE OSTI2024

Automated Network Services for Exascale Data Movement

The Large Hadron Collider (LHC) experiments distribute data by leveraging a diverse array of National Research and Education Networks (NRENs), where experiment data management systems treat networks as a “blackbox” resource. After the High Luminosity upgrade, the Compact Muon Solenoid (CMS) experiment alone will produce roughly 0.5 exabytes of data per year. NREN Networks are a critical part of the success of CMS and other LHC experiments. However, during data movement, NRENs are unaware of data priorities, importance, or need for quality of service, and this poses a challenge for operators to coordinate the movement of data and have predictable data flows across multi-domain networks. The overarching goal of SENSE (The Software-defined network for End-to-end Networked Science at Exascale) is to enable National Labs and universities to request and provision end-to-end intelligent network services for their application workflows leveraging SDN (Software-Defined Networking) capabilities. This work aims to allow LHC Experiments and Rucio, the data management software used by CMS Experiment, to allocate and prioritize certain data transfers over the wide area network. In this paper, we will present the current progress of the integration of SENSE, Multi-domain end-to-end SDN Orchestration with QoS (Quality of Service) capabilities, with Rucio, the data management software used by CMS Experiment.

Balcas, Justas↗
DOE OSTI2025

Temporally-consistent koopman autoencoders for forecasting dynamical systems

Absence of sufficiently high-quality data often poses a key challenge in data-driven modeling of high-dimensional spatio-temporal dynamical systems. Koopman Autoencoders (KAEs) harness the expressivity of deep neural networks (DNNs), the dimension reduction capabilities of autoencoders, and the spectral properties of the Koopman operator to learn a reduced-order feature space with simpler, linear dynamics. However, the effectiveness of KAEs is hindered by limited and noisy training datasets, leading to poor generalizability. To address this, we introduce the Temporally-Consistent Koopman Autoencoder (tcKAE), designed to generate accurate long-term predictions even with limited and noisy training data. This is achieved through a consistency regularization term that enforces prediction coherence across different time steps, thus enhancing the robustness and generalizability of tcKAE over existing models. We provide analytical justification for this approach based on Koopman spectral theory and empirically demonstrate tcKAE’s superior performance over state-of-the-art KAE models across a variety of test cases, including simple pendulum oscillations, kinetic plasma, and fluid flow data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Power Grid Contingency Analysis with Machine Learning: A Brief Survey and Prospects

We briefly review previous applications of machine learning (ML) in power grid analyses and introduce our ongoing effort toward developing a generative-adversarial (GA) model for fast and reliable grid contingency analyses. According to our review, the persisting limitation of traditional ML techniques in grid analyses is the need for an exhaustive amount of training data for model generalization and accurate predictions. GA models overcome this limitation by first learning true data distribution from a small training set, from which new samples assimilating true data are generated with some variations. Subsequently, GA models can transfer learn or super-generalize with increased accuracy, that is, accurately predict n - (k + 2) contingencies from a small n - k training set and generated n - (k + 1) data. The joint effort between Idaho National Lab and Florida State University strives to develop a zero-shot and deep learning-based contingency analysis tool, named Smart Contingency Analysis Neural Network (SCANN), by leveraging the aforementioned advantages of GA models. The basic architecture of SCANN stems from the Latent Encoding of Atypical Perturbations network combined with an adversarial network, and it is designed to generate imbalanced power flow data from learned true data distributions for prediction purposes. Here we also introduce the abstract concept of resilience-chaos plots, a new resilience characterization tool proposed to complement SCANN by aiding in the assessment of large amounts of high-order contingency predictions.

24 POWER TRANSMISSION AND DISTRIBUTION↗