Search NASA⌕ Search

SEARCH · Search NASA

Results for “voltage magnitude estimation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Net Load Redistribution Attacks on Nodal Voltage Magnitude Estimation in AC Distribution Networks

A high penetration level of smart devices and communication networks increases the threat of cyber-attacks in the distribution system. In this paper, we model a hidden, coordinated, net load redistribution attack (NLRA) in an AC distribution system. Based on local information of an attack region, the attacker’s goal is to create violations in nodal voltage magnitude estimation. Acting as a system operator equipped with global AC state estimation and bad data detection, we validate the stealthiness of the hidden NLRA in multiple attack cases. Simulation results on a modified PG&E 69-node distribution system show the validity of the proposed NLRA. The influence of NLRA on the distribution system is assessed and the impact of attack regions, attack timing, and system observability is also revealed.

Zhang, Hang↗
DOE OSTI2021

On the Use of Smart Meter Data to Estimate the Voltage Magnitude on the Primary Side of Distribution Service Transformers

This paper develops a novel method to estimate the voltage magnitude on the primary side of distribution service transformers. The proposed method relies exclusively on smart meters, and therefore it is fully data-driven. This is an important feature because electric utilities have detailed models of only the primary network - that is, the network between the distribution substation and the primary side of service transformers that are installed closer to end-customer sites. The network that connects the secondary side of service transformers to end-customer sites, referred to as the secondary network, is simply represented by a lumped load. For each secondary network, the proposed method uses data acquired from only 2 smart meters: the closest and the farthest-in the sense of electrical distance - from the service transformer. As a reference to this feature, the proposed method is named SM2Vp. To our knowledge, this is the first time a method is shown to provide actionable information for realtime operation and control of power distribution grids using only two smart meters per secondary network. This is important because utilities have experienced barriers in managing and using large data sets for real-time operation and control. SM2Vp is primarily intended to provide pseudo-measurements for distribution system state estimation, but it can also be used directly for voltage control schemes. The performance of SM2Vp is demonstrated by numerical simulations carried out on three secondary network synthetic models and by using field data provided by a utility partner serving customers in southwestern California. A maximum relative error of approximately 3.9% or less is observed for the primary voltage magnitude estimates in all numerical experiments.

distribution service transformer↗
DOE OSTI2021

On the Use of Smart Meter Data to Estimate the Voltage Magnitude on the Primary Side of Distribution Service Transformers: Preprint

This paper develops a novel method to estimate the voltage magnitude on the primary side of distribution service transformers. The proposed method relies exclusively on smart meters, and therefore it is fully data-driven. This is an important feature because electric utilities have detailed models of only the primary network--that is, the network between the distribution substation and the primary side of service transformers that are installed closer to end-customer sites. The network that connects the secondary side of service transformers to end-customer sites, referred to as the secondary network, is simply represented by a lumped load. For each secondary network, the proposed method uses data acquired from only 2 smart meters: the closest and the farthest--in the sense of electrical distance--from the service transformer. As a reference to this feature, the proposed method is named SM2Vp. To our knowledge, this is the first time a method is shown to provide actionable information for real-time operation and control of power distribution grids using only two smart meters per secondary network. This is important because utilities have experienced barriers in managing and using large data sets for real-time operation and control. SM2Vp is primarily intended to provide pseudo-measurements for distribution system state estimation, but it can also be used directly for voltage control schemes. The performance of SM2Vp is demonstrated by numerical simulations carried out on three secondary network synthetic models and by using field data provided by a utility partner serving customers in southwestern California. A maximum relative error of approximately 3.9% or less is observed for the primary voltage magnitude estimates in all numerical experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Matrix Completion for Improved Observability in Low-Voltage Distribution Grids

This paper considers the problem of recovering missing entries in a partially observed matrix from relatively few measurements (i.e., the so-called matrix completion problem) with the aim of increasing the presently limited observability of low-voltage distribution grids. To this end, the partially observed matrix is formed using scarce voltage magnitude measurements while accounting for their spatial information. Voltage readings are assumed to be collected from distribution utility sensors and/or geographically-distributed cable television network sensors located in immediate proximity to distribution grid nodes. A matrix completion approach built on the parameter-less singular value shrinkage technique is used to estimate voltage magnitudes at otherwise non-observable low-voltage nodes using a small number of single- or multiple-snapshot data. The effectiveness of the proposed approach is demonstrated using a U.S.-style distribution test system from the synthetic SMART- DS data set under very low- to moderate-observability conditions.

low-rank matrix completion↗
DOE OSTI2024

A Machine Learning Initializer for Newton-Raphson AC Power Flow Convergence

Power flow computations are fundamental to many power system studies. Obtaining a converged power flow case is not a trivial task especially in large power grids due to the non-linear nature of the power flow equations. One key challenge is that the widely used Newton based power flow methods are sensitive to the initial voltage magnitude and angle estimates, and a bad initial estimate would lead to non-convergence. This paper addresses this challenge by developing a random-forest (RF) machine learning model to provide better initial voltage magnitude and angle estimates towards achieving power flow convergence. This method was implemented on a real ERCOT 6102 bus system under various operating conditions. By providing better Newton-Raphson initialization, the RF model precipitated the solution of 2,106 cases out of 3,899 non-converging dispatches. These cases could not be solved from flat start or by initialization with the voltage solution of a reference case. Finally, results obtained from the RF initializer performed better when compared with DC power flow initialization, Linear regression, and Decision Trees.

random forest↗
DOE OSTI2021

Local Power-Voltage Sensitivity and Thevenin Impedance Estimation from Phasor Measurements

This paper describes how to use voltage phasor measurements to produce a sensitivity matrix that describes how real and reactive power injections at a node on a distribution network affect the local voltage magnitude and angle. Rather than estimating the sensitivity directly, the voltage phasor measurements and power commands/measurements are used to estimate the unbalanced, three-phase Thevenin impedance. The Thevenin impedance estimation is conducted using recursive least squares on temporal difference measurements. The Thevenin impedance and voltage phasor measurement are then used to build the local power-voltage sensitivity matrix with the closed form expression for the Jacobian of the power flow manifold. Hardware-in-the-loop simulations with phasor measurement units providing real phasor measurements are used to evaluate the recursive temporal difference Thevenin impedance estimation and Thevenin-based power-voltage sensitivity methods.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Generalized Analytical Estimation of Sensitivity Matrices in Unbalanced Distribution Networks

Fast and accurate estimation of sensitivity matrices is significant for the enhancement of distribution system modeling and automation. Analytical estimations have mainly focused on voltage magnitude sensitivity to active/reactive power injections for unbalanced networks with Wye-connected loads and neglecting DERs' smart inverter functionality. Hence, this paper enhances the scope of analytical estimation of sensitivity matrices for unbalanced networks with 1- Φ, 2- Φ, and 3- Φ Delta/Wye-connected loads, DERs with smart inverter functionality, and substation/line step-voltage regulators (SVR). A composite bus model comprising of DER, Delta- and Wye-connected load is proposed to represent a generic distribution bus, which can be simplified to load, PV, or voltage-controlled bus as required. Furthermore, the proposed matrix-based analytical method consolidates voltage magnitude and angle sensitivity to active/reactive power injection and tap-position of all SVRs into a single algorithm. Extensive case studies on IEEE and EPRI networks show the accuracy and wide scope of the proposed algorithm compared to the existing benchmark method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Doping- and Strain-Dependent Electrolyte-Gate-Induced Perovskite to Brownmillerite Transformation in Epitaxial La 1– x Sr x CoO 3–δ Films

Much recent attention has focused on the voltage-driven reversible topotactic transformation between the ferromagnetic metallic perovskite (P) SrCoO 3-δ and oxygen-vacancy-ordered antiferromagnetic insulating brownmillerite (BM) SrCoO 2.5 . This is emerging as a paradigmatic example of the power of electrochemical gating (using, e.g., ionic liquids/gels), the wide modulation of electronic, magnetic, and optical properties generating clear application potential. SrCoO 3 films are challenging with respect to stability, however, and there has been little exploration of alternate compositions. Here, we present the first study of ion-gel-gating-induced P. BM transformations across almost the entire La 1-x SrxCoO 3 phase diagram (0 ≤ x ≤ 0.70), under both tensile and compressive epitaxial strain. Electronic transport, magnetometry, and operando synchrotron X-ray diffraction establish that voltage-induced P → BM transformations are possible at essentially all x, including x ≤ 0.50, where both P and BM phases are highly stable. Under small compressive strain, the transformation threshold voltage decreases from approximately +2.7 V at x = 0 to negligible at x = 0.70. Both larger compressive strain and tensile strain induce further threshold voltage lowering, particularly at low x. The P → BM threshold voltage is thus tunable, via both composition and strain. At x = 0.50, voltage-controlled ferromagnetism, transport, and optical transmittance are then demonstrated, achieving Curie temperature and resistivity modulations of similar to 220 K and at least 5 orders of magnitude, respectively, and enabling estimation of the voltage-dependent Co valence. The results are analyzed in the context of doping- and strain-dependent oxygen vacancy formation energies and diffusion coefficients, establishing that it is thermodynamic factors, not kinetics, that underpin the decrease in the threshold voltage with x, that is, with increasing formal Co valence. These findings substantially advance the practical and mechanistic understanding of this voltage-driven transformation, with fundamental and technological implications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Voltage Estimation in Low-Voltage Distribution Grids with Distributed Energy Resources

Present distribution grids generally have limited sensing capabilities and are therefore characterized by low observability. Improved observability is a prerequisite for increasing the hosting capacity of distributed energy resources such as solar photovoltaics (PV) in distribution grids. In this context, this paper presents learning-aided low-voltage estimation using untapped but readily available and widely distributed sensors from cable television (CATV) networks. The cable broadband sensors offer timely local voltage magnitude sensing with 5-minute resolution and can provide an order of magnitude more data on the time-varying state of a secondary distribution system than currently deployed utility sensors. The proposed solution incorporates voltage readings from neighboring CATV sensors, taking into account spatio-temporal aspects of the observations, and estimates single-phase voltage magnitudes at all non-monitored low-voltage buses using random forests. The effectiveness of the proposed approach was demonstrated using a multi-phase 1572-bus feeder from the SMART-DS data set for two case studies passive distribution feeder (without PV) and active distribution feeder (with PV). The analysis was conducted on simulated data, and the results show voltage estimates with a high degree of accuracy, even at extremely low percentages of observable nodes.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Dynamics of the water-plasma interface in various discharge modes of atmospheric-pressure plasmas

This paper presents an experimental study of the dynamics of the water-plasma interface during the interaction high-voltage (HV) atmospheric pressure discharge with the water surface. Under the influence of the voltage applied to the pin type electrode, discharges are formed at the air layer between the sharp-tip HV electrode and deionized water. Three discharge regimes were identified from synchronized electrical waveforms and imaging: (i) a weak linear regime at 3–10.6 kV, (ii) a branching streamer regime at the maximum applied voltage of 12.6 kV, and (iii) a continuous arc-like regime occurring during the transition to lower voltage and higher current (e.g., U ≈ 3.2 kV, I ≈ 4.07 mA). High-speed shadowgraph images showed a symmetric interfacial cavity whose depth increased nonlinearly with voltage from h 0 ≈ 0.1 mm at 3 kV to h 0 ≈ 2.7 mm at 10.6 kV, reaching a maximum depth of h 0 ≈ 5.9 mm at 12.6 kV, while the cavity disappeared in the continuous-channel regime and was replaced by outward-propagating wave-like motion. The deformation of the water surface during the discharge-water interaction is governed by the balance between electric field forces, surface tension, gravitational forces, and electrohydrodynamic forces, whose relative contributions vary with the applied voltage. This effect was explained by quantitatively estimating the magnitudes of the acting forces and establishing the force balance. Optical emission spectroscopy in the continuous-channel regime indicated air plasma signatures and yielded a gas temperature of approximately 400 K, while prolonged operation (≈ 20 min) increased the water temperature to ~ 70 °C, reduced the water-layer thickness from 6 mm to 3 mm, and decreased pH from 7 to 4. These regime-resolved, quantitative results clarify how the dominant interfacial forcing shifts with discharge mode and provide a mechanistic basis for controlling plasma-water interactions in atmospheric-pressure applications.

atmospheric pressure plasma↗
DOE OSTI2024

Impedance-Aware Graph Convolutional Networks for Voltage Estimation in Active Distribution Networks

Voltage estimation plays a key role in ensuring the effective control and reliability of distribution networks. However, traditional machine learning methods often fail to capture the details of the distribution network’s topology. To overcome this challenge, graph convolutional networks (GCN) have emerged as an alternative. Graph convolutional networks inherently capture the topology of the grid, utilizing correlations to achieve precise voltage estimation. Other machine learning models and conventional GCNs fail to account for the distribution line characteristics found in the real world, limiting their effectiveness. This paper proposes an advanced variant of GCN called the Impedance-Aware Graph Convolutional Network (IA-GCN). The IA-GCN layer incorporates the magnitude of the impedance into the graph convolution mechanism, allowing it to capture topological nuances and provide valuable insights into node interrelationships by considering impedance as an intrinsic dimension. The performance of the IA-GCN layer is then compared with that of GCN and GraphSAGE layers through a surrogate model for voltage estimation. The performance analysis demonstrates that IA-GCN outperforms GCN by reducing the MAE by 87.55% and improving the R-squared value by 98%.

Ravi, Abhijith↗
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

Low-temperature spin Seebeck effect in nonmagnetic vanadium dioxide

The spin Seebeck effect (SSE) is sensitive to thermally driven magnetic excitations in magnetic insulators. Vanadium dioxide in its insulating low-temperature phase is expected to lack magnetic degrees of freedom, as vanadium atoms are thought to form singlets upon dimerization of the vanadium chains. Instead, we find a paramagnetic SSE response in V⁢O 2 films that grows as the temperature decreases below 50 K. The field and temperature-dependent SSE voltage is qualitatively consistent with a general model of paramagnetic SSE response and inconsistent with triplet spin transport. Quantitative estimates find a spin Seebeck coefficient comparable in magnitude to that observed in strongly magnetic materials. Finally, the microscopic nature of the magnetic excitations in V⁢O 2 requires further examination.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2024

Field‐representative evaluation of PID‐polarization in TOPCon PV modules by accelerated stress testing

Abstract Potential‐induced degradation‐polarization (PID‐p) can reduce module power, but how to project the extent to which PID‐p may occur in field conditions considering the factors of system voltage, condensed moisture, temperature, and illumination has not been clarified. Using tunnel oxide passivated contact (TOPCon) modules, this work demonstrates a method to test full‐size crystalline silicon PV modules for PID‐p to provide field‐representative results. In initial screening tests with positive or negative 1000 V electrical bias applied at 60°C for 96 h using Al foil electrodes on the glass surfaces, the module type exhibited reversible PID‐p only on the front face when the cell circuit was in negative voltage potential. No PID was detected on the rear after testing in either polarity. We then evaluated the PID‐p sensitivity on the front side under different UV irradiances while maintaining the glass surface wet to estimate real‐world susceptibility to PID‐p. The magnitude of the observed behavior was fit using a previously developed charge transfer and depletion by light model. Whereas power loss with −1000 V applied to the cell circuit at 60°C for 96 h in the dark was about 30%, testing the module front under 0.051 W·m −2 nm −1 at 340 nm UVA irradiation using fluorescent tubes, the mean degradation was only 3%. When the modules were tested in the dark for PID‐p with in situ dark current–voltage (I‐V) characterization, the thermal activation energy for degradation was 0.71 eV; for recovery in the dark, it was 0.58 eV. Whereas recovery from the degraded state at 60°C in the dark without voltage bias was 5% absolute in 38 h, rapid recovery of about 5% absolute was observed with 1000 W·s/m 2 exposure at 25°C using a flash tester.

14 SOLAR ENERGY↗
DOE OSTI2024

Leveraging Structures in Fault Diagnosis for Lithium-Ion Battery Packs

Lithium-ion battery systems consist of a varying number of single cells, designed to meet specific application requirements for output voltage and capacity. Effective fault diagnosis in these battery systems is an essential prerequisite for ensuring their safe and reliable operation. To address this need, we introduce a novel model-based fault diagnosis approach that distinguishes itself by leveraging informative structures inherent in battery systems such as architecture, uniformity among the constituent cells, and sparsity of fault occurrences to enhance its fault diagnosis capabilities. The proposed approach formulates a moving horizon estimation (MHE) problem, incorporating such structural information to estimate different fault signals—specifically, internal short circuits, external short circuits, and voltage and current sensors faults. We conduct various simulations to evaluate the performance of the proposed approach under different fault types and magnitudes. The obtained results validate the proposed approach and promise effective fault diagnosis for battery systems.

Farakhor, Amir↗
DOE OSTI2025

Magnetically tuned metal-insulator transition in LaAlO 3 /SrTiO 3 nanowire arrays

A wide family of two-dimensional (2D) systems, including stripe-phase superconductors, sliding Luttinger liquids, and anisotropic 2D materials, can be modeled by an array of coupled one-dimensional (1D) electron channels or nanowire arrays. Here we report experiments in arrays of conducting nanowires with gate and field tunable interwire coupling, that are programmed at the LaAlO 3 /SrTiO 3 interface. We find a magnetically tuned metal-to-insulator transition in which the transverse resistance of the nanowire array increases by up to four orders of magnitude, which can be further tuned with a gate voltage. To explain this behavior, we develop a minimal model of a coupled two-wire system where a Wenzel-Kramers-Brillouin-based approach is used to estimate the transverse tunneling conductance. We demonstrate the existence of distinct conductance features and highlight the crucial role played by the field dependence of the interwire potential barrier on transport properties. Since our model makes minimal assumptions, we expect our predictions to hold for a wide class of coupled 1D systems. The nanowire arrays can serve as model systems to understand the origin of exotic behavior in correlated materials via analog quantum simulation.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Critical Clearing Time for Droop-Controlled Grid-Forming Inverters With Circular Current Limiting and Virtual Impedance Current Limiting

The critical clearing time is a crucial parameter for assessing the large-signal stability of grid-forming (GFM) inverters. Variations in current limiters can significantly impact an inverter's transient behavior during large disturbances. Here, this letter introduces a method based on the power-angle relationship to evaluate the fault recovery of a droop-controlled GFM inverter under a three-phase bolted fault scenario, considering both the circular current limiter and the virtual impedance method. High-fidelity simulations validate the accuracy of the proposed formulas in estimating the critical clearing time of the GFM inverter.

42 ENGINEERING↗
DOE OSTI2020

Perturbation-Based Diagnosis of False Data Injection Attack Using Distributed Energy Resources

Modern smart grid relies on various sensor measurements for its operational control. In a successful false data injection attack, the attacker manipulates the measurements from the grid sensors such that undetected errors are introduced into the estimates of the system parameters leading to catastrophic situations. This paper proposes a novel perturbation based false data injection attack detection mechanism that utilizes inverter based distributed energy resources (DERs) to create low magnitude perturbation signal in the distribution system voltage that is inconsequential to the normal grid operation. Two voltage sensitivity analysis based algorithms are designed to identify the optimal set of DERs that can create the voltage perturbation signal of desired magnitude. An analytical method of voltage sensitivity analysis is used to compute the magnitude of voltage perturbation signal at each node in a computationally efficient manner. Then, a detection mechanism is developed that checks for the presence of the perturbation sequence in each sensor measurement. A sensor measurement is deemed authentic if the voltage perturbation signal is present in the data. In case of sensor malfunction or cyber-attack, the perturbation signal will not be present in the measurement data. Performance of the proposed attack detection mechanism is validated via simulation of the IEEE 69 bus test system.

24 POWER TRANSMISSION AND DISTRIBUTION↗