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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Cyber-Attack Detection and Accommodation for the Energy Delivery System

The goals of this project were to create a software system with a suite of key algorithms for cyber-attack detection and accommodation providing domain layer protection for critical power generation assets. Example assets included gas and steam turbines, heat recovery steam generators, and electrical generators. The aggressive algorithm goals were aimed at reducing the false positive rates in threat detection to <1% using learnings from many evolving disciplines (power turbine and generator physics, power system modeling, modern control theory, system identification, machine learning, deep learning, mathematics and data science). Additional goals for the algorithms involved localizing threats on-the-fly to know in which monitoring node the effects of attacks are present, and then providing accommodation to keep the system running uninterrupted much of the time in the presence of the attack. Accommodation had a performance goal of providing resiliency when up to 50% of monitoring nodes are in an attack state.

cybersecurity, cyber-physical↗
DOE OSTI2022

Epistemology of voltage control in DER-rich power system

Despite the recent development of several scalable, robust, and resilient control approaches with superior convergence properties considering an increasing penetration of distributed energy resources (DERs), cognitive oversights often simplify several aspects of the cyber–physical power system in the controller development. Here, following the identification of the limitations of classical controller definitions, we justify alternative definitions of voltage control approaches classifiers considering three inter-disciplinary domains: (i) power system, (ii) optimization and decision-making, and (iii) networking and cyber-security, to develop a taxonomy for helping in real-world comparative performance analysis and deployability of these controllers. We observe that classical and introduced domain-based definitions together can better classify the control algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Machine Learning in Power System Operations: Training Data

Reliability and stability of the electric grid today has depended upon operations of the grid which include the protective relay. Today, the electricity sector faces new challenges with the shift of generation resource characteristics away from the traditional “big iron” generation to inverter-based resources (IBR) which shift the physics and assumption used in grid operation and protection. These changing conditions represent new challenges for protective relays (identification of faults) and increased challenges for protection engineers (correct settings and configuration, reduction of mis-operations), both issues recognized in research and industry. Finding new approaches to reduce mis-operations in relaying and new approaches to fault identification is critical to grid operations. Using today’s modern technology of embedded systems, edge computing, machine learning (ML), and communications we can help address challenges and augment and improve on existing power system operations methodologies.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2012

Intelligent Systems Approach for Automated Identification of Individual Control Behavior of a Human Operator

Results have been obtained using conventional techniques to model the generic human operator?s control behavior, however little research has been done to identify an individual based on control behavior. The hypothesis investigated is that different operators exhibit different control behavior when performing a given control task. Two enhancements to existing human operator models, which allow personalization of the modeled control behavior, are presented. One enhancement accounts for the testing control signals, which are introduced by an operator for more accurate control of the system and/or to adjust the control strategy. This uses the Artificial Neural Network which can be fine-tuned to model the testing control. Another enhancement takes the form of an equiripple filter which conditions the control system power spectrum. A novel automated parameter identification technique was developed to facilitate the identification process of the parameters of the selected models. This utilizes a Genetic Algorithm based optimization engine called the Bit-Climbing Algorithm. Enhancements were validated using experimental data obtained from three different sources: the Manual Control Laboratory software experiments, Unmanned Aerial Vehicle simulation, and NASA Langley Research Center Visual Motion Simulator studies. This manuscript also addresses applying human operator models to evaluate the effectiveness of motion feedback when simulating actual pilot control behavior in a flight simulator.

Zaychik, Kirill B.↗
NASA NTRSDate not supplied

Powering the Lunar Surface: Managing Dust, Extreme Environments, and Power Needs

Power availability remains one of the primary constraints for lunar surface science. This talk reviews power requirements from previously flown instruments to help prepare future payloads for upcoming CLPS opportunities and highlights the testing and environmental simulation capabilities at NASA JSC that enable reliable lunar payload development. It also outlines the power needs, environmental challenges, and emerging technologies required to support sustained human and robotic operations on the lunar surface as part of NASA’s Moon to Mars strategy. Key challenges include variable solar illumination at polar and equatorial regions, extreme thermal environments, and dust driven degradation that limit current surface power systems. The science data needed for resource identification and landing site planning will allow for the successful preparation of crewed Artemis activities and long-term presence. Building on recent missions, current test infrastructure, and emerging power technology pathways, this presentation equips industry, academia, and government teams with the information needed to design robust lunar payloads, reduce development risk, and fully leverage the increasing cadence of CLPS missions. These developments will form a critical technical foundation for long duration lunar presence and future Mars exploration.

Anastasia Ford↗
NASA NTRSDate not supplied

Powering the Lunar Surface: Managing Dust, Extreme Environments, and Power Needs

Power availability remains one of the primary constraints for lunar surface science. This talk reviews power requirements from previously flown instruments to help prepare future payloads for upcoming CLPS opportunities and highlights the testing and environmental simulation capabilities at NASA JSC that enable reliable lunar payload development. It also outlines the power needs, environmental challenges, and emerging technologies required to support sustained human and robotic operations on the lunar surface as part of NASA’s Moon to Mars strategy. Key challenges include variable solar illumination at polar and equatorial regions, extreme thermal environments, and dust driven degradation that limit current surface power systems. The science data needed for resource identification and landing site planning will allow for the successful preparation of crewed Artemis activities and long-term presence. Building on recent missions, current test infrastructure, and emerging power technology pathways, this presentation equips industry, academia, and government teams with the information needed to design robust lunar payloads, reduce development risk, and fully leverage the increasing cadence of CLPS missions. These developments will form a critical technical foundation for long duration lunar presence and future Mars exploration.

lunar power↗
NASA NTRS2016

Technical Capability Upgrades to the NASA Langley Research Center 8 ft. by 15 ft. Thermal Vacuum Chamber

The 8 ft. by 15 ft. thermal vacuum chamber (TVAC), housed in Building 1250 at the NASA Langley Research Center (LaRC), and managed by the Systems Integration and Test Branch within the Engineering Directorate, has undergone several significant modifications to increase testing capability, safety, and quality of measurements of articles under environmental test. Significant modifications include: a new nitrogen distribution manifold for supplying the shroud and other cold surfaces to liquid nitrogen temperatures; a new power supply and distribution system for accurately controlling a quartz IR lamp suite; a suite of contamination monitoring sensors for outgassing measurements and species identification; a new test article support system; signal and power feed-throughs; elimination of unnecessary penetrations; and a new data acquisition and control commanding system including safety interlocks. This paper will provide a general overview of the LaRC 8 ft. by 15 ft. TVAC chamber, an overview of the new technical capabilities, and will illustrate each upgrade in detail, in terms of mechanical design and predicted performance. Additionally, an overview of the scope of tests currently being performed in the chamber will be documented, and sensor plots from tests will be provided to show chamber temperature and pressure performance with actual flight hardware under test.

Thornblom, Mark N.↗
NASA NTRS1979

The problem of solar induced currents

The production of surface electric fields by geomagnetic variations is discussed with reference to magnetotelluric theory; and evidence is presented to show that the severity of solar-induced currents (SIC) at Cornerbrook, Newfoundland is due to channeling of currents induced in the sea through a region adjacent to the power line. An 'isolated loop' approximation is proposed for calculating the quasi-dc currents produced in the power system by the surface electric fields. Identification of the processes causing the geomagnetic perturbations responsible for SIC would be aided by investigation of the local time dependence of SIC occurrence. Such knowledge could potentially be used to provide several hours warning of SIC.

Boteler, D. H.↗
NASA NTRS1986

Space power development impact on technology requirements

The paper is concerned with the selection of a specific spacecraft power technology and the identification of technology development to meet system requirements. Requirements which influence the selection of a given technology include the power level required, whether the load is constant or transient in nature, and in the case of transient loads, the time required to recover the power, and overall system safety. Various power technologies, such as solar voltaic power, solar dynamic power, nuclear power systems, and electrochemical energy storage, are briefly described.

Cassidy, J. F.↗
NASA NTRS1987

Identification of salt-alloy combinations for thermal energy storage applications in advanced solar dynamic power systems

Thermodynamic calculations based on the available data for flouride salt systems reveal that a number of congruently melting compositions and eutectics exist which have the potential to meet the lightweight, high energy storage requirements imposed for advanced solar dynamic systems operating between about 1000 and 1400 K. Compatibility studies to determine suitable containment alloys to be used with NaF-22CaF2-13MgF2, NaF-32CaF2, and NaF-23MgF2 have been conducted at the eutectic temperature + 25 K for each system. For these three NaF-based eutectics, none of the common, commercially available high temperature alloys appear to offer adequate corrosion resistance for a long lifetime; however mild steel, pure nickel and Nb-1Zr could prove useful. These latter materials suggest the possibility that a strong, corrosion resistant, nonrefractory, elevated temperature alloy based on the Ni-Ni3Nb system could be developed.

Whittenberger, J. D.↗
NASA NTRSDate not supplied

Advancing Air Mobility: Few-Shot Learning in Airspace Research and Development

The advancement of Air Mobility, particularly in the context of Advanced Air Mobility (AAM) and Urban Air Mobility (UAM), represents a transformative shift in aviation's role in modern society. A comprehensive understanding of requirement consistency is paramount for fostering interoperability, standardization, and cost-effectiveness within airspace systems. This paper introduces a novel approach utilizing a pretrained Sentence Transformers model and few-shot learning to address this crucial aspect task of flagging potentially inconsistent requirements. Few-shot learning supports the development of this future through ensuring the accuracy and consistency of identified requirements with little human oversight. This approach offers a promising solution to the challenges of requirement consistency identification in airspace systems. By harnessing the power of advanced NLP techniques with fine-tuned models, stakeholders can enhance efficiency, accuracy, and scalability; ultimately fostering improved interoperability, standardization, and cost-effectiveness in airspace management.

Natural Language Processing↗
NASA NTRS2001

Optical Amplifier Based Space Solar Power

The objective was to design a safe optical power beaming system for use in space. Research was focused on identification of strategies and structures that would enable achievement near diffraction limited optical beam quality, highly efficient electrical to optical conversion, and high average power in combination in a single system. Efforts centered on producing high efficiency, low mass of the overall system, low operating temperature, precision pointing and tracking capability, compatibility with useful satellite orbits, component and system reliability, and long component and system life in space. A system based on increasing the power handled by each individual module to an optimum and the number of modules in the complete structure was planned. We were concerned with identifying the most economical and rapid path to commercially viable safe space solar power.

Fork, Richard L.↗
DOE OSTI2023

Robust Real-Time Modeling of Distribution Systems with Data-Driven Grid-Wise Observability (Final Technical Report)

The overall objective of this project is to leverage existing and emerging sensor measurements to develop data-driven observability enhancement algorithms as well as robust state estimation and parameter identification techniques to enable real-time grid-wise monitoring and modeling of loads and distributed energy resources (DERs). The project resulted in a holistic framework with the following key components: 1) data-driven and machine learning-based grid-edge visibility enhancement, 2) robust branch-current-based state estimation (BCSE), and 3) robust real-time steady-state and dynamic-state modeling of loads and DERs. The research outcomes have provided utility companies better network visibility, higher-fidelity load/DER models, and more accurate assessment of DERs’ impacts, thus, facilitating the integration of renewable energy sources. The synergistic collaborative project among Iowa State University (ISU), Argonne National Laboratory (ANL), Electric Power Research Center (EPRC), SIEMENS Industry, Alliant Energy, Cedar Falls Utilities (CFU), and Maquoketa Valley Electric Cooperative (MVEC) to leverage the team’s extensive expertise and experience in power distribution systems, state estimation, online modeling and identification, and DER integrations. The proposed frameworks have been verified with industry adopted software and attempted integrated with existing tool wherever possible

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1978

Photovoltaic power systems workshop

Discussions are presented on apparent deficiencies in NASA planning and technology development relating to a standard power module (25-35 kW) and to future photovoltaic power systems in general. Topics of discussion consider the following: (1) adequate studies on power systems; (2) whether a standard power system module should be developed from a standard spacecraft; (3) identification of proper approaches to cost reduction; (4) energy storage avoidance; (5) attitude control; (6) thermal effects of heat rejection on solar array configuration stability; (7) assembly of large power systems in space; and (8) factoring terrestrial photovoltaic work into space power systems for possible payoff.

Killian, H. J.↗
DOE OSTI2024

Advanced Sensor Deployment for Distribution System State Estimation and Fault Identification

Distribution systems are currently facing steep operational challenges as a result of the rapidly increasing integration of renewables and other distributed energy resources (DERs) at both the primary and secondary circuit levels. Distribution utilities and system operators have traditionally had some visibility of their primary circuits using low-frequency supervisory control and data acquisition systems, and they have had very poor if not zero visibility of the secondary circuits where the presence of DERs is constantly increasing. Therefore, this paper presents simulation studies to demonstrate the benefits of an advanced, high-fidelity sensor technology, called as the Meta-Alert System (MAS), developed by Electrical Grid Monitoring, Ltd. (EGM), on the distribution grid. First, a reliable model of the EGM sensors is developed, and then two use cases, distribution system state estimation (DSSE) and fault identification are simulated to evaluate the performance of the MAS technology. Simulation results on the Electric Power Research Institute J1 feeder demonstrate that the MAS can effectively participate in system-level DSSE programs and can detect and locate faults faster than traditional distribution protection schemes.

distribution system↗
DOE OSTI2024

CNN-Based Phase Fault Classification in Real and Simulated Power Systems Data

This study proposes a convolutional neural network (CNN)–based two-step phase fault detection and identification method to classify anomalies in the power grid signal. Specifically, the first step checks the fault’s existence and determines the need for the second step. Subsequently, in the case of anomalies in the power grid signal, the second step identifies the type of fault, including line-to-line, single-line-to-ground, double-line-to-ground, and triple-line. Accordingly, the CNN architecture is both designed for the classification layers and trained with simulated data. To provide maximum prediction accuracy with minimum processing time, this study investigates the combinations of various feature extraction (FE) techniques, such as fast Fourier transform (FFT), amplitude and phase (AP), auto-correlation function, power spectral density, and wavelet transform (WT). Consequently, simulated and real-world results demonstrate that the proposed two-step method outperforms conventional one-step techniques, with the best performance obtained by using the combination of AP-AP, AP-WT, FFT-AP, and FFT-WT–based FE methods.

Alaca, Ozgur↗