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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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For this presentation, we looked into using uncertainty quantification to understand the uncertainty in model parameters in predicting voltage and time response of a lithium-air battery.
In this paper, we propose a predictive coordinated and cooperative voltage control method in a power distribution system with high penetration of photovoltaic (PV) units. First, an integrated coordinated voltage control of voltage regulators (VRs) tap positions and cooperative distributed control of the reactive power output from PV inverters are used to maintain system voltages within an appropriate bandwidth. Next, solar power forecasting is applied to predict voltage changes, which are used to set the VR tap positions and capacitor switch status to prevent large voltage fluctuations. The fine tuning of voltage adjustment is then achieved by cooperative control of PV inverters to maintain a uniform voltage profile across the system. The proposed method is tested on a modified IEEE 123-node test feeder with high penetration of PVs using real measurement data and compared with the base case. Simulation results demonstrate the effectiveness of the integrated voltage control, as well as the enhancement from the predictive control through solar power forecasting-enabled voltage change estimates. Comparison to previous work in the literature shows significant improvement in terms of voltage deviation and reduction in excessive tap changes.
Power system operation will encounter numerous voltage variabilities as the proliferation of renewable energy continues. Real-time monitoring and communication technologies can potentially improve voltage stability by enabling the rapid detection of voltage deviations and the implementation of corrective actions. These corrective actions will only be effective in restoring stability if they are chosen in a timely and scalable manner. This paper considers the problem of power systems’ load voltage control in order to simultaneously address both magnitude and time voltage specifications. In order to comply with grid codes and avoid unnecessary relay protection actions, a model predictive control (MPC)-based control strategy employing temporal logic specifications (TLSs) is proposed. The TLSs strategy is introduced as a formalism to control the voltage variation at a critical load bus against operational bounds over time. The control objective is to schedule optimal control input signals from the available supportive energy storage systems, which provide reactive power injections, leading to satisfying the specified finite-time restoration described by the TLSs with minimal control efforts. The simulation results display that the TLSs strategy for power systems’ voltage control synthesis is extremely significant.
Surface photovoltage spectroscopy measures the contribution of solution-processed hole transport layers to the photovoltage of GaP solar cells.
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With increasing penetration of renewable energy and active consumers, control and management of power distribution networks has become challenging. Renewable energy sources can cause random voltage fluctuations as their output power depends on weather conditions. Conventional voltage control schemes such as tap changers and capacitor banks lack the foresight required to quickly alleviate voltage violations. Thus, there is an urgent need for effective approaches for predicting and mitigating voltage violations as a result of random fluctuations in power injections. This work proposes a novel voltage monitoring approach based on low-complexity, data-driven probabilistic voltage sensitivity analysis. The usefulness of this work is not only in predicting voltage violations in unbalanced distribution grids, but also in opening up the door for optimal voltage control. Using system data and forecasts, the proposed approach predicts the distribution of system node voltages which is then used to to identify nodes that may violate the nominal operational limits with high probability. The method is tested on the IEEE 37 node distribution system considering integrated distributed solar energy sources. The method is validated against the classic load flow based method and offers over 95% accuracy in predicting voltage violations.
Computer program for predicting silicon solar cell current-voltage characteristics as function of incident solar intensity and cell temperature over heliocentric distances
Various government agencies have put forth effort to enable the prediction of what lightning current will do to aircraft avionic systems. Ongoing and future efforts of predicting avionic voltages and currents caused by electromagnetic fields external to the aircraft are illustrated. The Intrasystem Analysis Program (IAP) was put to use to predict lightning-induced voltages on avionic systems. Presently funded programs are investigating the modification nonmetallic composite materials will cause to the metallic IAP program predictions. The various factors involved in designing prediction techniques are discussed.
In this paper, we present a physics-informed neural network (PINN) approach for predicting the performance of an all-vanadium redox flow battery, with its physics constraints enforced by a two-dimensional (2D) mathematical model. The 2D model, which includes 6 governing equations and 24 boundary conditions, provides a detailed representation of the electrochemical reactions, mass transport and hydrodynamics occurring inside the redox flow battery. To solve the 2D model with the PINN approach, a composite neural network is employed to approximate species concentration and potentials; the input and output are normalized according to prior knowledge of the battery system; the governing equations and boundary conditions are first scaled to an order of magnitude around 1, and then further balanced with a self-weighting method. Our numerical results show that the PINN is able to predict cell voltage correctly, but the prediction of potentials shows a constant-like shift. To fix the shift, the PINN is enhanced by further constrains derived from the current collector boundary. Finally, we show that the enhanced PINN can be even further improved if a small number of labeled data is available.
Program for analyzing line replacement unit (LRU) voltages in shuttle orbiter predicts minimum voltage level of main power distribution bus needed to meet predetermined LRU voltage. Loads constant-power or resistive. In some cases fed from two or three sources through diodes.
Computer program derived from linear equations used for predicting silicon solar cell current voltage curve characteristics as function of incident solar intensity and cell temperature
Currently, the major reliability characteristic of tantalum capacitors is the failure rate, which is a constant used to characterize random failures of electronic components. However, contemporary MnO2 and polymer tantalum capacitors have both infant mortality (IM) and wear-out (WO) failures. This requires assessments of the useful life of the parts that is limited by the time of inception of WO failures that can be determined using an adequate reliability model and results of highly accelerated life testing (HALT). In this presentation, a modified time dependent dielectric breakdown (TDDB) model is used to describe both IM and WO failures during HALT. Specifics of physical processes resulting in degradation and failures in polymer and MnO2 tantalum capacitors are discussed, and the increase of IM failures with the level of stress is explained. The model justifies exponential dependence of the acceleration factors on voltage and predicts higher values of the voltage acceleration constant for IM compared to WO failures.
High voltage breakdown phenomena in planetary atmospheres - prediction of voltage breakdown of any antenna in any atmosphere
Information in nomograph is derived from data obtained from RF-breakdown tests on components of uniform and nonuniform geometry. Nomograph also can be used in design work to predict breakdown margins; if operational minimum pressure is established giving minimum value on nomograph, minimum breakdown voltage consistent with allowed value can be predicted.
The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.
Because it avoids the high currents in a parallel loaded capacitor, the cascaded Schwarz converter should offer better component utilization than converters with sinusoidal output voltages. The circuit is relatively easy to protect, and it provides a predictable trapezoidal voltage waveform that should be satisfactory for 20-kHz distribution systems. Analysis of the system is enhanced by plotting curves of normalized variables vs. gamma(1), where gamma(1) is proportional to the variable frequency of the first stage. Light-load operation is greatly improved by the addition of a power recycling rectifier bridge that is back biased at medium to heavy loads. Operation has been verified on a 2.5-kW circuit that uses input and output voltages in the same range as those anticipated for certain future spacecraft power systems.