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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

Power Surge Testing for Polymer Tantalum Capacitors

Due to dry environments, anomalous charging currents (ACC) in polymer tantalum capacitors (PTC) might cause malfunctions and failures in space systems. Currently, there is no standard metrics to assess this effect and factors affecting ACC are not well understood. This paper discusses benefits and drawbacks of different methods used to reveal ACC and suggest a power surge testing (PST) as a procedure for screening and qualification of PTCs. The suggested test is similar to the surge current testing that is currently used for MnO2 tantalum capacitors but assures dissipation of high power in the part during the whole period of testing. Using various types of capacitors, the reproducibility of test results for different lots of PTCs and from sample to sample in a lot were estimated. The impact of moisture content, test temperature, stress voltages, and preconditioning is assessed. Thermal effects associated with ACC and the possibility of catastrophic failures were studied experimentally using an IR camera and calculated at adiabatic heating conditions. Possible mechanisms of the phenomenon are discussed and recommendations for testing to avoid failures related to ACC are suggested.

Alexander A Teverovsky↗
DOE OSTI2023

Privacy-Preserving Robust Consensus for Distributed Microgrid Control Applications

Consensus-based distributed control has been proposed for coordinating distributed energy resources (DERs) in microgrids (MGs). As one key component, distributed average observers are used to estimate the average of a group of reference signals (e.g., voltage, current, or power). State-of-the-art distributed average observers could lead to loss of privacy due to information exchange on the communication channels. The DERs' reference signals, which contain private information, could be inferred by an eavesdropper. In this article, a privacy-preserving distributed average observer is proposed that is based on robust consensus and uses the state decomposition method to preserve privacy. Compared to the existing methods, the proposed observer does not require the knowledge of the reference signal's derivative and gives accurate and smooth estimation, and is thus applicable for MG distributed control applications. A detailed analysis regarding the convergence and privacy properties of the proposed observer is presented. Here, the proposed observer is implemented on hardware controllers and validated in the context of distributed MG control applications through hardware-in-the-loop (HIL) tests.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS2018

Extraction of Doppler Observables from Open-Loop Recordings for the Juno Radio Science Investigation

The goal of the Juno Gravity Science investigation is to estimate the gravitational field of Jupiter by measurement of the spacecraft velocity during periods of closest approach. Velocity is measured by the Doppler shift of dual X- and Ka-band radio links between the Juno spacecraft, in orbit around Jupiter, and the DSS-25 antenna of the Deep Space Network (DSN). During times of closest-approach, Juno experiences large dynamic ranges caused by the orbital dynamics and spin signatures caused by the spin-stabilized spacecraft that are detectable by the receivers at DSS-25. Open-loop recordings of received voltages are processed to compute Doppler observables utilized in the estimation of the gravity field. Presented is a method to process open-loop data collected by the DSN to compensate for the spin signature of the spacecraft, removal of artifacts from Doppler observables caused by the high dynamic environment, and improve performance of the digital phase-locked loop utilized in the data processing.

Buccino, Dustin R.↗
DOE OSTI2020

A Hybrid-Learning Algorithm for Online Dynamic State Estimation in Multimachine Power Systems

With the increasing penetration of distributed generators in the smart grids, having knowledge of rapid real-time electromechanical dynamic states has become crucial to system stability control. Conventional Supervisory Control and Data Acquisition (SCADA)-based dynamic state estimation (DSE) techniques are limited by the slow sampling rates, while the emerging phasor measurement units (PMUs) technology enables rapid real-time measurements at network nodes. Using generator bus terminal voltages, we propose a hybrid-learning DSE (HL-DSE) algorithm to estimate the synchronous machine rotor angle and speed in real time. The HL-DSE takes the power system model into account and trains neuroestimators with real-time data in an online manner. Compared with traditional DSE methods, the HL-DSE overcomes limitations by using a data-driven approach in conjunction with the physical power system model. The time efficiency, accuracy, convergence, and robustness of the proposed algorithm are tested under noises and fault conditions in both small- and large-scale test systems. Simulation results show that the proposed HL-DSE is much more computationally efficient than widely used Kalman filter (KF)-based methods while maintaining comparable accuracy and robustness. In particular, HL-DSE is over 100 times faster than square-root unscented KF (SR-UKF) and 80 times faster than extended KF (EKF). The advantages and challenges of the HL-DSE are also discussed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A Medium-/Low-Voltage Joint State Estimator Through Linear Uncertainty Propagation

Traditionally, distribution system state estimations (DSSE) are challenged by the lack of measurements at both primary and secondary sides of the system. The widely available cable television (CATV) voltage sensors installed in low-voltage (LV) networks bring opportunities to achieve higher quality DSSE covering a broader area of the distribution network. This study proposes a medium-/low-voltage (MV/LV) joint distribution system state estimation approach using the untapped CATV measurements. It aims at addressing the need for system situational awareness at the grid edge while improving the estimation accuracy at both the primary and secondary sides compared to its disjointed counterpart. Linearized measurement functions and boundary condition uncertainty propagation rules are derived to ensure the computational efficiency and accuracy of the joint state estimator. Numerical experiments are conducted on an IEEE test feeder to demonstrate the efficacy of the proposed method and the value of CATV measurements.

joint state estimation↗
DOE OSTI2025

Particle Swarm Optimization of Dynamic Load Model Parameters in Large Systems

This paper considers two dynamic load models that are widely used in industry to account for induction motor behavior: CMLD and CLOD. These models must be parametrized for the specific utility system in a general way so that they can be used in planning studies and provide a conservative but realistic representation of load behavior. This study considers a measurement-based approach to tuning both models. The load modeling study compares the response of the tuned models to generic candidate models using historical events. This study considers one area-based subsystem to simplify the modeling approach and reduce the number of models required for simulations. Additionally, because dynamic load models often produce similar results for different sets of parameters, a sensitivity study was conducted to assess the parameter impacts on the voltage response. The sensitivity study covers the parameters that are tuned using event measurements. The process to estimate the parameters uses the particle-swarm optimization algorithm. Overall, the performance of the tuned model more accurately captures recovery voltage, delayed recovery, and settling voltage than its predecessor models while not being overly tuned so that it remains general for peak summer conditions.

dynamic load modeling↗
DOE OSTI2024

Shot-noise-induced lower temperature limit of the nonneutral plasma parallel temperature diagnostic

Abstract We develop a new algorithm to estimate the temperature of a nonneutral plasma in a Penning-Malmberg trap. The algorithm analyzes data obtained by slowly lowering a voltage that confines one end of the plasma and collecting escaping charges, and is a maximum likelihood estimator based on a physically-motivated model of the escape protocol presented in (Beck in Measurement of the magnetic and temperature dependence of the electron-electron anisotropic temperature relaxation rate. PhD thesis, 1990). Significantly, our algorithm may be used on single-count data, allowing for improved fits with low numbers of escaping electrons. This is important for low-temperature plasmas such as those used in antihydrogen trapping. We perform a Monte Carlo simulation of our algorithm, and assess its robustness to intrinsic shot noise and external noise. The assumptions in this paper allow for a lower bound for measurable plasma temperatures of approximately $3\,\mathrm{K}$ 3 K for plasmas of length $1\,\mathrm{cm}$ 1 cm , with approximately 100 particle counts needed for an accuracy of $\pm 10 \%$ ± 10 % .

Zhong, Adrianne (ORCID:0000000162618736)↗
NASA NTRS1972

Concept for a high voltage solar array with integral power conditioning.

Description of a general case solution that synthesizes a high voltage solar array system from a switchable building block concept which makes possible system optimization for specific load requirements. A specific optimized solution is demonstrated, with performance estimates relating array area, weight, and power. Significant technology problems peculiar to a high-voltage switchable solar array design are discussed, along with special requirements anticipated during a hardware development effort.

Wiener, P.↗
DOE OSTI2022

Practical Event Location Estimation Algorithm for Power Transmission System Based on Triangulation and Oscillation Intensity

Event location in power systems is quite essential information for system operators to enhance control-room situational awareness capability. Therefore, it is of great importance to develop an event location estimation algorithm for transmission systems with high accuracy. With the development of wide-area measurement system (WAMS) such as FNET/GridEye, and the synchrophasor measurement devices (SMDs) such as frequency disturbance recorders (FDRs), the synchronous measurement data including frequency, voltage amplitude and phase angle can be collected and used for event location estimation. First, the phase angle and rate of change of frequency (RoCoF) trajectories are respectively used for determining two sets of wave arrival time associated with each FDR. Then, a convolutional neural network (CNN) is utilized to determine the wave arrival order to select the more suitable set of wave arrival times for a given case and to perform corresponding modifications. Next, the oscillation intensity associated with each FDR is determined based on phase angle trajectories in the center of inertia (COI) coordinate system. Finally, the multiple criteria for event location estimation are represented. In conclusion, case studies and comparisons between the proposed and previous algorithms using actual and confirmed cases in U.S. power systems are performed to demonstrate the effectiveness and improvement of the proposed algorithm in practical applications.

frequency disturbance recorder (FDR)↗
DOE OSTI2021

Highly Integrated Wide Bandgap Power Module for Next Generation Plug-In Vehicles (Final Report)

Wide bandgap devices are key enablers for high power density traction inverters. A highly integrated wide bandgap (WBG) power module enables higher temperature operation, higher inverter efficiency and overall smaller die area with the same power delivery. However, it requires careful design of the power module to take advantage of all the superior material properties that silicon carbide (SiC) provides over conventional silicon (Si) Insulated Gate Bipolar Transistor (IGBT) based design. This project developed an efficient and densely packed SiC power module for an automotive traction inverter application. Extensive study was performed to select the best performing SiC die considering a wide range of temperature variation, suitability to integrate into target packaging, robust body diode performance and overall high electrical efficiency. Multiple design ideas were explored to achieve lower stray inductance for both power loop and signal loop, uniform current distribution, compact form factor, and very high thermal performance. The final power module package includes all six inverter switches in the same module (six-pack) with die sintered at both bottom and top surfaces utilizing high performance substrate to achieve high electrical and thermal performance. Two power module variants were developed using 900 V and 1200 V devices for operating at two different high voltage levels (> 600 V HV battery). Both power modules were comprehensively characterized, and high efficiency operation of the inverter was confirmed through analysis. Finally, a complete functional inverter was built with the 1200V power modules using other essential components (HV capacitors, gate driver board, controller board, current sensor, connectors etc.) and tested using GM’s standard test procedure. There are several challenges associated with adopting WBG devices for high voltage operation. In order to assess and mitigate those challenges, GM and subrecipients of this project have conducted multiple detailed studies. This includes phase current reconstruction using integrated Rogowski coil, device junction temperature estimation using on-state gate resistance, modeling and testing the effect of high voltage and fast switching on motor insulation and bearings, partial discharge inception voltage measurement under different environmental conditions. Findings of all these studies are summarized in this report.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Impact of Different Thermal Gradients on the Dynamics of Cylindrical Lithium-ion Cells Subject to Accelerated Aging and on Module Performance

This study investigates the impacts of applying different thermal gradient patterns to cylindrical lithium-ion cells in a module on cell dynamics (temperatures, current flows, state of charge), module performance (evolution of resistance, capacity, and energy versus cycle number), and module lifetime. The thermal gradients were generated using cooling plates (CPs) with three different flow-field designs, namely, straight, perpendicular, and U-turn. The study uses computational fluid dynamics (CFD), the pseudo-two-dimensional (P2D) battery model, capacity loss and increased impedance due to the growth of a solid-electrolyte-interphase, and the electric current distribution from module terminals to cells that depends on the series-parallel electrical connections among the cells. The impact of the thermal gradient (resulting from the CP designs) on the variability in resistance, current, state of charge, and voltage among the cells was analyzed and linked to differences in the module's performance. Applying a thermal gradient to parallel-connected strings of series-connected cells led to variation in the current through each parallel string and an imbalance in the voltage of series-connected cells. Module performance is poorer when the thermal gradient causes a voltage imbalance than when it causes a current imbalance. Module performance becomes the worst when both current variation and voltage imbalance happen together. For instance, the module's lifetime (estimated as reaching 80% of its initial capacity) varied by 5% to 17.5%, depending on the magnitude and pattern of the imposed thermal gradient. As the relative orientation between thermal gradients and cells' electrical connectivity influences the module's performance, appropriate consideration should be given to the choice of the CP, especially if large thermal gradients are allowed.

Battery thermal management↗
DOE OSTI2024

Optimization-Based Model Reduction Scheme for Renewable Energy Power Plants Using Standardized Testing Scenarios

This paper presents an optimization-based model reduction scheme for renewable energy (RE) power plants consisting of inverter-based resources (IBRs) operating in grid-following (GFL) or grid-forming (GFM) modes. More importantly, the datasets feeding the optimization-based model reduction scheme are generated and re-used through the standardized grid-interactive testing scenarios. Particularly, the proposed scheme makes use of the power plant point of common coupling (PCC) measurements of various quantities specified by standardized tests (e.g., voltage and frequency ride through) as per IEEE 2800, to estimate the parameters of the reduced-order model such that its dynamic performance aligns with the original detailed power plant model. The proposed model reduction approach does not require the parameters of individual IBRs and using standardized test data as input to the formulated optimization problem simplifies the reduced-order modelling scheme. Extensive case studies following standardized test scenarios verified the remarkable accuracy of the proposed approach.

Yallamilli, Ram S. [Purdue University]↗
DOE OSTI2022

Aluminum Electrolytic Capacitor Vulnerability Evaluation in DC Power Supplies at the Spallation Neutron Source

The service life of electrolytic capacitors is a concern for long-term reliable operation of power supplies. The Power Conversion Group at the Spallation Neutron Source (SNS) facility of Oak Ridge National Laboratory is responsible for more than 500 power supplies with over 1000 electrolytic capacitors installed in these power supplies. Most of the electrolytic capacitors have been in operation since 2007 and are either well over or close to the manufacturer provided lifetime. This paper addresses this vulnerability to ensure continued reliable operation of the SNS. The power supply circuit is simulated in a MATLAB/Simulink environment to quantify the capacitor ripple current and operating voltage. Combined with manufacturer data, this information is used to estimate capacitor lifetime. Utilizing the simulation results and lifetime projections of the capacitors, capacitor replacements for the power supplies is prioritized. To assess end-of-life, the ambient temperature and ripple current of the capacitor is used. Based on the manufacturer's data and scientific literature review, capacitor end-of-life is established. The results of the simulations and analysis is presented. Data on the highest priority supplies will also be presented, and statistics on the capacitor performance, reliability and remaining lifetime will be shown.

Harave, Sudarshan↗
DOE OSTI2020

Conductive Adhesives for Metallization of Interdigitated Back Contact Solar Cells

The metallization and interconnection of Si solar cells to form a module is usually a multi-step process: cells are metallized individually, soldered into strings, and subsequently assembled into a module. Furthermore, the metallization process for individual cells must be compatible with the doped surfaces being contacted, which creates additional challenges when moving from diffused junctions to heterojunctions and/or passivated contact structures. To mitigate these problems and streamline module manufacturing for the case of interdigitated back contact (IBC) cell modules, we developed an approach wherein unmetallized IBC cells are bonded directly to a circuitized backsheet using a conductive adhesive (CA) that only conducts out-of-plane. The metal/CA/silicon stack is bonded by hot-pressing at 100-200 degrees C.For a CA consisting of ethylene vinyl acetate (EVA) filled with Ag-coated spheres or indium powder we find that contact resistivities in the range of 1-10 Wcm 2 are obtained, regardless of which conductive filler and which hot pressing parameters are used. XPS revealed that about 1-2 nm EVA residue bonds to the Si in these samples, suggesting the formation of an insulating barrier that is responsible for the high contact resistance. Using indium powder alone, improved contact resistivities <0.6 Wcm 2 , high shunt resistances, and an implied open-circuit voltage loss below 10 mV were obtained. Furthermore, a cost estimate is presented, indicating that substantial cost savings of up to $10/m 2 , or about $0.05/W DC , are possible through CA-based IBC module manufacturing.

14 SOLAR ENERGY↗
DOE OSTI2022

Development and Applications of an eReaxFF Force Field for Graphitic Anodes of Lithium-Ion Batteries

Graphene is one of the most promising materials for lithium-ion battery anodes due to its superior electronic conductivity, high surface area for lithium intercalation, fast ionic diffusivity and enhanced specific capacity. A reliable description of many battery processes requires an explicit description of electrochemical interactions involving electrons. A detailed atomistic modeling of electronic conduction and non-zero voltage simulations of graphitic materials require the inclusion of an explicit electronic degree of freedom. To enable large length- and time-scale simulations of electron conduction in graphitic anodes, we developed an eReaxFF force field concept describing graphitic materials with an explicit electron. The newly developed force field, verified against quantum chemistry-based data describing, amongst others, electron affinities and equation of states, reproduces the qualitative behavior of electron conductivity in pristine and imperfect graphitic materials at different applied temperatures and voltages. In addition, excess electron localization near a defect site estimated from eReaxFF simulations agree quite well with the corresponding density functional theory calculations. Here, our eReaxFF simulations show the initiation of lithium-metal-plating driven by electron transfer from the graphene surface to the exposed lithium ions demonstrating the method’s potential for studying lithium-graphene interactions with explicit electrons and explain many unresolved electrode and electrode-electrolyte interface processes.

25 ENERGY STORAGE↗
DOE OSTI2025

Optimizing TinyTPC Test Stand for Low Energy Event Reconstruction

Introducing dopants into liquid argon offers a promising avenue to enhance the sensitivity and performance of LAr detectors such as DUNE. To explore this opportunity, we are optimizing and deploying the TinyTPC test stand, a compact time projection chamber with LArPix readout to study the effects of photosensitive dopants on the charge detected. TinyTPC enables comparative measurements of low-energy responses under various operational configurations and provides a platform to study the behavior of pixel readouts at low energy. We ensured system reliability through continuous current and voltage monitoring, modeling the detector's electrical behavior, and indirectly estimating total resistance under testing conditions. We ran the Blanche cryostat under various thresholds and gain configurations to evaluate their potential impact to low-energy event reconstruction and advancing R&D efforts for dopant integration in neutrino detectors

Van Loenen, Drew [Franciscan U. Steubenville]↗
NASA NTRS1969

Estimation of signal-to-noise ratios

Statistical method estimates signal-to-noise ratios in an observed random voltage, such as the output of a telemetry receiver. Signals from a distant transmitting source, overlaid by noise signals, are monitored continuously.

Couvillon, L. A., Jr.↗
NASA NTRS2019

Machine Learning, Markov Chain Monte Carlo, and Optimal Algorithms to Characterize the AdvACT Kilopixel Transition-Edge Sensor Arrays

Next-generation focal planes comprising dozens of kilopixel transition-edge sensor (TES) arrays require new methods to rapidly screen candidate arrays, evaluate array non-idealities in the field, identify outlier devices for removal, and optimize the array performance in the field. We demonstrate robust methods to estimate TES parameters (critical temperatures and thermal conductivity parameters) and their uncertainties using a custom Markov Chain Monte Carlo (MCMC) algorithm. We also constrain systematic effects in estimating the TES parameters from non-isothermal current-voltage curves (IVs) at approximately a ~3% level. Additionally, for the first time, we have applied Machine Learning (ML) algorithms to tune detector arrays and optimize their performance.

Maria Salatino↗