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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 505 records · Page 28

Spatio-Temporal Characterization of Synchrophasor Data Against Spoofing Attacks in Smart Grids

“Source ID Mix” has emerged as a new type of highly deceiving attack which can alter the source information of synchrophasor data measured by multiple phasor measurement units, thereby paralyzing many wide-area measurement systems applications. To address such sophisticated cyber attacks, we have exploited the spatio-temporal characteristics of synchrophasor data for authenticating measurements’ source information. Specifically, the source authentication is performed when the measurements are subjected to three types of spoofing attacks. Some practical difficulties in applying the proposed method on real-time authentication caused by insufficient measurement data have also been solved. Experimental results with real synchrophasor measurements have validated the effectiveness of the proposed method in detecting such complicated data spoofing and improving power systems’ cyber security.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Characterizing Time Series Data Diversity for Wind Forecasting

Wind forecasting plays an important role in integrating variable and uncertain wind power into the power grid. Various forecasting models have been developed to improve the forecasting accuracy. However, it is challenging to accurately compare the true forecasting performances from different methods and forecasters due to the lack of diversity in forecasting test datasets. This paper proposes a time series characteristic analysis approach to visualize and quantify wind time series diversity. The developed method first calculates six time series characteristic indices from various perspectives. Then the principal component analysis is performed to reduce the data dimension while preserving the important information. The diversity of the time series dataset is visualized by the geometric distribution of the newly constructed principal component space. The volume of the 3-dimensional (3D) convex polytope (or the length of 1D number axis, or the area of the 2D convex polygon) is used to quantify the time series data diversity. The method is tested with five datasets with various degrees of diversity.

big data visualization↗

Data Sharing Report Characterization of Isotope Row Facilities Oak Ridge National Laboratory Oak Ridge TN

The U.S. Department of Energy (DOE) Oak Ridge Office of Environmental Management (EM-OR) requested that Oak Ridge Associated Universities (ORAU), working under the Oak Ridge Institute for Science and Education (ORISE) contract, provide technical and independent waste management planning support using funds provided by the American Recovery and Reinvestment Act (ARRA). Specifically, DOE EM-OR requested ORAU to plan and implement a survey approach, focused on characterizing the Isotope Row Facilities located at the Oak Ridge National Laboratory (ORNL) for future determination of an appropriate disposition pathway for building debris and systems, should the buildings be demolished. The characterization effort was designed to identify and quantify radiological and chemical contamination associated with building structures and process systems. The Isotope Row Facilities discussed in this report include Bldgs. 3030, 3031, 3032, 3033, 3033A, 3034, 3036, 3093, and 3118, and are located in the northeast quadrant of the main ORNL campus area, between Hillside and Central Avenues. Construction of the isotope production facilities was initiated in the late 1940s, with the exception of Bldgs. 3033A and 3118, which were enclosed in the early 1960s. The Isotope Row facilities were intended for the purpose of light industrial use for the processing, assemblage, and storage of radionuclides used for a variety of applications (ORNL 1952 and ORAU 2013). The Isotope Row Facilities provided laboratory and support services as part of the Isotopes Production and Distribution Program until 1989 when DOE mandated their shutdown (ORNL 1990). These facilities performed diverse research and developmental experiments in support of isotopes production. As a result of the many years of operations, various projects, and final cessation of operations, production was followed by inclusion into the surveillance and maintenance (S&M) project for eventual decontamination and decommissioning (D&D). The process for D&D and final dismantlement of facilities requires that the known contaminants of concern (COCs) be evaluated and quantified and to identify and quantify any additional contaminants in order to satisfy the waste acceptance criteria requirements for the desired disposal pathway. Known facility contaminants include, but are not limited to, asbestos-containing material (ACM), radiological contaminants, and chemical contaminants including polychlorinated biphenyls (PCBs) and metals.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

AVTA federal fleet PEV readiness data logging and characterization study for the National Park Service: Grand Canyon National Park

This report focuses on the Grand Canyon National Park (GCNP) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for NASA White Sands Test Facility

This report focuses on the NASA White Sands Test Facility (WSTF) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for the National Park Service: Sleeping Bear Dunes National Lakeshore

This report focuses on the Sleeping Bear Dunes National Lakeshore (SLBE) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for Department of Veterans Affairs – VA Manhattan Campus

This report focuses on the Department of Veterans Affairs, VA Manhattan Campus (VA- Manhattan) fleet to identify the daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support successful introduction of plug-in electric vehicles (PEVs) into the agency’s fleet. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively called PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for National Institute of Health

This report focuses on the National Institute of Health (NIH) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of plug-in electric vehicles (PEVs) into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle (BEV) or plug-in hybrid electric vehicle (PHEV) (collectively plug-in electric vehicles, or PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for Department of Veterans Affairs – James J. Peters VA Medical Center, Bronx, New York

This report focuses on the Department of Veterans Affairs, James J. Peters VA Medical Center (VA - Bronx) fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agencies’ fleets. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for Department of Health and Human Services – ASPR

This report focuses on the Department of Health and Human Services, Assistant Secretary for Preparedness and Response fleet to identify daily operational characteristics of select vehicles and report findings on vehicle and mission characterizations to support the successful introduction of PEVs into the agency’s fleet. Individual observations of these selected vehicles provide the basis for recommendations related to electric vehicle adoption and whether a battery electric vehicle or plug-in hybrid electric vehicle (collectively referred to as PEVs) can fulfill the mission requirements.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for NASA Stennis Space Center

Federal agencies are mandated to purchase alternative fuel vehicles, increase consumption of alternative fuels, and reduce petroleum consumption. Available plug-in electric vehicles (PEVs) provide an attractive option in the selection of alternative fuel vehicles. PEVs, which consist of both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have significant advantages over internal combustion engine (ICE) vehicles in terms of energy efficiency, reduced petroleum consumption, and reduced production of greenhouse gas (GHG) emissions, and they provide performance benefits with quieter, smoother operation. This study intended to evaluate the extent to which NASA Stennis Space Center (Stennis) could convert part or all of their fleet of vehicles from petroleum-fueled vehicles to PEVs.

33 ADVANCED PROPULSION SYSTEMS↗

AVTA Federal Fleet PEV Readiness Data Logging and Characterization Study for United States Coast Guard Headquarters

Federal agencies are mandated to purchase alternative fuel vehicles, increase consumption of alternative fuels, and reduce petroleum consumption. Available plug-in electric vehicles (PEVs) provide an attractive option in the selection of alternative fuel vehicles. PEVs, which consist of both battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs), have significant advantages over internal combustion engine (ICE) vehicles in terms of energy efficiency, reduced petroleum consumption, and reduced production of greenhouse gas (GHG) emissions, and they provide performance benefits with quieter, smoother operation. This study intended to evaluate the extent to which the United States Coast Guard Headquarters (USCG HQ) could convert part or all of their fleet of vehicles from petroleum-fueled vehicles to PEVs.

33 ADVANCED PROPULSION SYSTEMS↗