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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Loose Belt Fault Detection and Virtual Flow Meter Development Using Identified Data-driven Energy Model for Fan Systems

An energy model that correlates fan airflow, head, speed, and system power input is essential to detect device faults and optimize control strategies in fan systems. Since the application of variable-frequency drives (VFDs) makes the motor-efficiency data published by manufacturers inapplicable for VFD–motor–fan systems, the fan efficiency and drive (belt–motor–VFD) efficiency must be identified for each individual system to obtain accurate energy models. The objectives of this paper are to identify an energy model of existing VFD–motor–fan systems using available experimental data and demonstrate its applications in loose belt fault detection and virtual airflow meter development for optimal control. First, an approach is developed to identify the fan head, fan efficiency, and drive-efficiency curves using available fan head, speed, and system power input as well as temporarily measured airflow rate without measuring shaft power. Then, the energy model is identified for an existing VFD–motor–fan system. Finally, the identified model is applied to detect the slipped belt faults and develop the virtual airflow meter. The experiment results reveal that the developed approach can effectively obtain the energy model of VFD–motor–fan systems and the model can be applied to effectively detect slipped belt faults and accurately calculate the fan airflow rate.

42 ENGINEERING↗
NASA NTRS1979

Laser power conversion system analysis, volume 1

The orbit-to-orbit laser energy conversion system analysis established a mission model of satellites with various orbital parameters and average electrical power requirements ranging from 1 to 300 kW. The system analysis evaluated various conversion techniques, power system deployment parameters, power system electrical supplies and other critical supplies and other critical subsystems relative to various combinations of the mission model. The analysis show that the laser power system would not be competitive with current satellite power systems from weight, cost and development risk standpoints.

Jones, W. S.↗
DOE OSTI2025

Artificial Intelligence and Machine Learning Applications in Modern Power Systems

Machine learning (ML) and artificial intelligence (AI) algorithms offer valuable tools for the analysis and interpretation of large datasets. These tools have the capability to uncover insights that may not be readily apparent within these datasets. In recent years, the integration of ML and AI has become increasingly prevalent in various applications within the power system domain. One of the earliest instances of machine learning in power systems can be traced back to demand forecasting, where artificial neural networks were employed for short-term load forecasting. In contemporary power systems, an abundance of high-resolution geospatial and temporal data is generated at various time intervals, ranging from sub-seconds (Phasor Measurement Units or PMUs) to seconds (Supervisory Control and Data Acquisition or SCADA), minutes (Process Information or PI), and extending to days, months, and years. These datasets contain valuable information concerning system reliability and performance. This information holds the potential to offer critical insights into system operations, as well as solutions for predicting and mitigating contingencies to prevent cascading outages. Despite the immense power of machine learning tools, system operators, planners, and utilities often exhibit hesitancy in fully embracing AI-enabled system operations and planning. This cautious approach persists, even as numerous diverse applications of machine learning continue to emerge in the realm of power systems. In this chapter, our focus will delve deep into ML and AI applications tailored for power systems. These applications aim to furnish system operators with enhanced situational awareness and augment their decision-making capabilities, especially during challenging operating conditions. Specific areas of interest encompass root cause analyses of electricity market datasets and the strategic selection of representative samples from vast power system databases for training ML/AI models. Finally, the chapter will conclude with a short discussion on the future of ML/AI in power systems and possible directions that the industry is moving towards.

power system applications, machine learning (ML), ↗
DOE OSTI2020

Model-Free Data Authentication for Cyber Security in Power Systems

With the development and wide deployment of measurement equipment, data can be automatically measured and visualized for situation awareness in power systems. However, the cyber security of power systems is also threated by data spoofing attacks. This letter proposed a measurement data source authentication (MDSA) algorithm based on feature extraction techniques including ensemble empirical mode decomposition (EEMD) and fast Fourier transform (FFT), and machine learning for real-time measurement data classification. Compared with previous work, the proposed algorithm can achieve higher accuracy of MDSA using a shorter window of data from closely located synchrophasor measurement sensors.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1983

Spectrophotovoltaic orbital power generation

A subscale model of a photovoltaic power system employing spectral splitting and 1000:1 concentration was fabricated and tested. The 10-in. aperture model demonstrated 15.5% efficiency with 86% of the energy produced by a GaAs solar cell and 14% of the energy produced by an Si cell. The calculated efficiency of the system using the same solar cells, but having perfect optics, would be approximately 20%. The model design, component measurements, test results, and mathematical model are presented.

Knowles, G.↗
NASA NTRS2005

Performance and Mass Modeling Subtleties in Closed-Brayton-Cycle Space Power Systems

Contents include the following: 1. Closed-Brayton-cycle (CBC) thermal energy conversion is one available option for future spacecraft and surface systems. 2. Brayton system conceptual designs for milliwatt to megawatt power converters have been developed 3. Numerous features affect overall optimized power conversion system performance: Turbomachinery efficiency. Heat exchanger effectiveness. Working-fluid composition. Cycle temperatures and pressures.

Barrett, Michael J.↗
NASA NTRS2018

An Approach for Utilizing Power Flow Modeling for Simulations of Hybrid Electric Propulsion Systems

This paper describes an approach to creating simulations of the electric components for a hybrid electric propulsion system. The proposed modeling technique is based on power/load flow modeling and is designed to provide a modular framework that includes buses, lines, and other electrical components that can be connected together to form the electrical distribution system. The purpose of this paper is to detail an electric distribution system modeling technique and to demonstrate how these models may be integrated with turbomachinery simulations. These general modeling techniques were created to be utilized for system and control design studies. Additionally, steady-state and dynamic performance for a proposed model example is compared with data from a hardware in the loop simulation.

Chapman, Jeffryes W.↗
DOE OSTI2021

A physics-based parametric regression approach for feedwater pump system diagnosis

Here in this paper, we assessed the performance of a model-based approach for fault diagnosis of a nuclear power plant feedwater pump system. Physics-based models were constructed to monitor the performance of the system components. Plant data were used for the calibration of the models and subsequently in the diagnosis of abnormal events. We considered two real-time events representing scenarios of a pump fault and component performance degradation. The two events were correctly diagnosed, and the results demonstrated the high detection sensitivity of the physics-based models. Various sensor fault scenarios were simulated to show the capability of the approach to detect and uniquely identify sensor faults. Results for a scenario in which the plant operated in flexible power mode also showed that the diagnostic approach is insensitive to changes of operating conditions, which is one of the advantages of the model-based approach using physics-based models over purely data-driven approach.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Load altering attack-tolerant defense strategy for load frequency control system

Cyber attacks are emerging threats to every information-oriented energy management system. By violating the cyber systems, the hacker can disrupt the security and stability due to the strong coupling between the cyber and physical facilities. In this paper, one type of cyber attacks designated as the load altering attack is studied for the power system frequency control, and corresponding defense strategies are proposed to improve the frequency control performance. Considering the difficulty of the application of model-based controller into large-scale power systems, a novel model-free defense framework is for the first time presented. Under this framework, both active defense and passive defense strategies are designed. The former assumes that the defender has the initiative to learn different attack scenarios. Adaptive defense strategies are implemented using the online attack identification information and off-line trained strategy pool. The latter assumes that the defender passively tolerates various attack scenarios via the pre-trained off-line strategy. Both approaches prove to be effective through validation based on the IEEE benchmark systems. The proposed defense framework and defense strategies can be extended to other energy control systems to enhance their attack tolerance capability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2004

Battery Resistance Analysis of ISS Power System

The computer package, SPACE (Systems Power Analysis for Capability Evaluation) was created by the members of LT-9D to perform power analysis and modeling of the electrical power system on the International Space Station (ISS). Written in FORTRAN, SPACE comprises thousands of lines of code and has been used profficiently in analyzing missions to the ISS. LT-9D has also used its expertise recently to investigate the batteries onboard the Hubble telescope. During the summer of 2004, I worked with the members of LT-9D, under the care of Dave McKissock. Solar energy will power the ISS through eight solar arrays when the ISS is completed, although only two arrays are currently connected. During the majority of the periods of sunlight, the solar arrays provide enough energy for the ISS. However, rechargeable Nickel-Hydrogen batteries are used during eclipse periods or at other times when the solar arrays cannot be used (at docking for example, when the arrays are turned so that they will not be damaged by the Shuttle). Thirty-eight battery cells are connected in series, which make up an ORU (Orbital Replacement Unit). An ISS "battery" is composed of two ORUs. a great deal of time into finding the best way to represent them in SPACE. During my internship, I investigated the resistance of the ISS batteries. SPACE constructs plots of battery charge and discharge voltages vs. time using a constant current. To accommodate for a time-varying current, the voltages are adjusted using the formula, DeltaV = DeltaI * Cell Resistance. To enhance our model of the battery resistance, my research concentrated on several topics: investigating the resistance of a qualification unit battery (using data gathered by LORAL), comparing the resistance of the qualification unit to SPACE, looking at the internal resistance and wiring resistance, and examining the impact of possible recommended changes to SPACE. The ISS batteries have been found to be very difficult to model, and LT-9D has dedicated

Newstadt, Gregory E.↗
DOE OSTI2025

Scalability analysis of heavy-duty gas turbines using data-driven machine learning

With the increasing integration of variable renewable energy sources into power systems, the role of flexible power generation technologies like gas turbines (GT) in rapid grid balancing remains crucial. This sustained importance underscores the need for scaled and precise modeling of GT to ensure effective integration within evolving energy frameworks. While physics-driven GT models integrate thermodynamics, fluid dynamics, and combustion principles, they often rely on approximate mathematical representations to accommodate scaling that may not capture the actual complex dynamics for GTs and inertial effects associated to GTs with different ratings. In this study, a data-driven model is proposed using machine learning (ML) techniques to conduct GT scalability analysis and performance evaluation with high accuracy. The ML model, trained on data from various operating conditions and performance parameters, aims to uncover intricate relationships and patterns, resembling GT characteristics at different scales (ratings). The model is developed to capture complex system interaction and to adapt to changing operational scenarios at different capacities, providing valuable insights of power system dynamics. In this study, the real-time digital simulator platform was employed to generate training data for the ML model and assess its dynamic characteristics. The ultimate objective was to develop a detailed modeling framework based on governing equations and data-driven ML capable of predicting key performance indicators, in thermal systems such as GTs, including power output, speed, fuel consumption, and exhaust temperature under diverse operating conditions at different scales. The developed ML framework demonstrated high accuracy, with mean relative errors for GT power prediction, reference speed, exhaust temperature, and compressor pressure ratio (CPR) parameters consistently below 0.1% across typical load fluctuation scenarios. Maximum deviations were limited to approximately 0.5 K for exhaust temperature and 0.009 for CPR, underscoring the model’s ability to replicating dynamic GT behavior with high precision. The adaptability of the ML model enables its application across diverse operational conditions and its extension to other thermal systems. By leveraging advanced ML techniques, this study presents a robust and scalable modeling framework that enhances GT simulation precision, facilitating improved integration into evolving power systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1992

Lunar PMAD technology assessment

This report documents an initial set of power conditioning models created to generate 'ballpark' power management and distribution (PMAD) component mass and size estimates. It contains converter, rectifier, inverter, transformer, remote bus isolator (RBI), and remote power controller (RPC) models. These models allow certain studies to be performed; however, additional models are required to assess a full range of PMAD alternatives. The intent is to eventually form a library of PMAD models that will allow system designers to evaluate various power system architectures and distribution techniques quickly and consistently. The models in this report are designed primarily for space exploration initiative (SEI) missions requiring continuous power and supporting manned operations. The mass estimates were developed by identifying the stages in a component and obtaining mass breakdowns for these stages from near term electronic hardware elements. Technology advances were then incorporated to generate hardware masses consistent with the 2000 to 2010 time period. The mass of a complete component is computed by algorithms that calculate the masses of the component stages, control and monitoring, enclosure, and thermal management subsystem.

Metcalf, Kenneth J.↗
NASA NTRS1988

Modelling the performance of the tapered artery heat pipe design for use in the radiator of the solar dynamic power system of the NASA Space Station

The paper presents a computer program developed to model the steady-state performance of the tapered artery heat pipe for use in the radiator of the solar dynamic power system of the NASA Space Station. The program solves six governing equations to ascertain which one is limiting the maximum heat transfer rate of the heat pipe. The present model appeared to be slightly better than the LTV model in matching the 1-g data for the standard 15-ft test heat pipe.

Evans, Austin Lewis↗
NASA NTRS2006

Validation Database Based Thermal Analysis of an Advanced RPS Concept

Advanced RPS concepts can be conceived, designed and assessed using high-end computational analysis tools. These predictions may provide an initial insight into the potential performance of these models, but verification and validation are necessary and required steps to gain confidence in the numerical analysis results. This paper discusses the findings from a numerical validation exercise for a small advanced RPS concept, based on a thermal analysis methodology developed at JPL and on a validation database obtained from experiments performed at Oregon State University. Both the numerical and experimental configurations utilized a single GPHS module enabled design, resembling a Mod-RTG concept. The analysis focused on operating and environmental conditions during the storage phase only. This validation exercise helped to refine key thermal analysis and modeling parameters, such as heat transfer coefficients, and conductivity and radiation heat transfer values. Improved understanding of the Mod-RTG concept through validation of the thermal model allows for future improvements to this power system concept.

numerical model↗