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DOE OSTI2024

Reductive quantum phase estimation

Estimating a quantum phase is a necessary task in a wide range of fields of quantum science. To accomplish this task, two well-known methods have been developed in distinct contexts, namely, Ramsey interferometry (RI) in atomic and molecular physics and quantum phase estimation (QPE) in quantum computing. We demonstrate that these canonical examples are instances of a larger class of phase estimation protocols, which we call reductive quantum phase estimation (RQPE) circuits. Here, we present an explicit algorithm that allows one to create an RQPE circuit. This circuit distinguishes an arbitrary set of phases with a smaller number of qubits and unitary applications, thereby solving a general class of quantum hypothesis testing to which RI and QPE belong. We further demonstrate a tradeoff between measurement precision and phase distinguishability, which allows one to tune the circuit to be optimal for a specific application. Published by the American Physical Society 2024

Papadopoulos, Nicholas J. C. (ORCID:00000002635700↗
DOE OSTI2018

Two-stage State Estimation Algorithm for Distribution Network Analysis

Practical distribution system contains many nodes and short branches. As a result, a more efficient and stable distribution state estimation (DSE) algorithm is needed to properly handle the inverse operation of its large-scale ill-conditioned information matrix. This paper proposed a two-stage DSE algorithm to transform the inverse operation into addition and multiplication operations in a recursive manner. It includes pseudo measurement preprocessing in the offline stage where an approximate linear estimator is adopted to relieve most of computation burdens, and real-time correction in the online stage where a nonlinear estimator is used to update the estimation. Numerical results of the method applied to both IEEE standard test systems and an actual distribution system show the accuracy and effectiveness of the proposed algorithm.

Dong, Jiaojiao↗
DOE OSTI2020

An Inductor Current Estimation Approach for DC/DC Converters Based on Bisection Method

The trends toward higher power density and efficiency for the DC/DC converters has posed more strict requirements for inductors. The inductor current in turn affect the operating characteristics of the converters significantly and thus needs to be accurately estimated. In this paper, a new bisection-method-based inductor current estimation approach considering the variation of inductance is proposed. The proposed approach can be adapted and applied for various DC/DC converters, including the widely used buck and boost converters and etc. Comparing to the conventional estimation method using constant inductance, the proposed method can obtain more precise estimations of both the inductor current waveform and the power loss. Experimental results on a buck converter with a ferrite-core inductor are presented to validate the effectiveness of the proposed approach.

Zhao, Zhe↗
DOE OSTI2017

Channel Estimation for Filter Bank Multicarrier Systems in Low SNR Environments

Channel estimation techniques are crucial for reliable communications. This paper is concerned with channel estimation in a filter bank multicarrier spread spectrum (FBMCSS) system. We explore two channel estimator options: (i) a method that makes use of a periodic preamble and mimics the channel estimation techniques that are widely used in OFDM-based systems; and (ii) a method that stays within the traditional realm of filter bank signal processing. For the case where the channel noise is white, both methods are analyzed in detail and their performance is compared against their respective Cramer-Rao Lower Bounds (CRLB). Advantages and disadvantages of the two methods under different channel conditions are given to provide insight to the reader as to when one will outperform the other.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Grid Parameters and Voltage Estimation Approach Integrating Data-Driven Converter Model

With Measurements of grid voltage and current are essential for the optimal operation of the grid protection and control (P&C) systems. Grid parameters vary through time during the faults and especially in the converter interfaced resources (CIRs) rich power grid, and thus accurate estimation is critical to avoid the mis-operation of the P&C systems. In this paper, a moving horizon estimation (MHE) as an observer is devised and applied to estimate the grid line parameters and grid voltages for protection enhancement. Due to the proprietary and confidentiality of CIRs, the proposed approach uses the black-box model to represent their dynamics. Leveraging the easily accessible measurements of output current from the black-box model of CIR and voltage at the point of common coupling, the proposed method estimates the grid impedance and grid voltage during normal and faulty operating conditions. The performance shows that the optimization-based observer was able to closely observe the accurate states and parameters, which can be utilized by the P&C systems.

Subedi, Sunil↗
DOE OSTI2023

Throughput Measurements and Capacity Estimates for Quantum Connections

The throughput of conventional and quantum network connections is an important performance metric, which is typically specified by bits per second (bps) and entangled quntum bits per second (ebps), respectively. It is measured over practical quantum network connections using specialized methods, and estimated using analytical bounds for which extensive theory has been developed. For practical connections, however, these two quantities have often been hard to correlate due to the lack of measurements and estimates derived under well-characterized common conditions. They both differ significantly from the conventional network throughput of TCP which employs buffers and loss recovery mechanisms. We describe a conventional-quantum testbed that enables the comparison of these two quantities both qualitatively and quantitatively. The bps and ebps throughput is measured over fiber connections of lengths over 75 kilometers, which show that the former decreases significantly slower with distance than the latter and in a qualitatively different way. The analytic capacity estimates of ebps are derived using approximations based on light intensity measurements, and they decrease more rapidly with distance than the measured ebps throughput. These results provide qualitative insights into the conventional transport mechanisms based on buffers, and the conditions used in deriving the analytical ebps capacity estimates.

Rao, Nageswara↗
DOE OSTI2026

Supervised Learning-Based Spatial Position Estimation with Vertical Displacement for Hovering UAV Wireless Power Transfer

This study presents a supervised learning-based spatial position estimation approach for wireless power transfer (WPT) systems supporting hovering unmanned aerial vehicle (UAV) charging. Unlike stationary charging scenarios, hovering UAVs introduce continuous lateral misalignment and vertical displacement, leading to variations in magnetic coupling and reduced power transfer efficiency. To address this challenge, the proposed method estimates the relative spatial position of the receiver coil using only electrical measurements obtained at the secondary side. A supervised learning model is trained to map output voltage and current features to spatial coordinates, enabling position awareness without requiring external sensors, vision systems, or communication links. The sensing functionality is inherently integrated into the WPT system, allowing simultaneous power transfer and localization through the same magnetic interface. Experimental validation is conducted on a laboratory-scale prototype under varying lateral offsets and air-gap conditions. In addition, spline-based interpolation is employed to increase spatial data density for training. The results demonstrate that the proposed framework can capture spatial variations associated with both lateral and vertical displacement, providing reliable position estimation under hovering conditions. This work establishes a hardware-efficient, sensorless solution for UAV wireless charging and serves as a baseline for advanced data-driven position estimation methods in dynamic WPT systems.

Asa, Erdem [ORNL] (ORCID:0000000190884812)↗
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

Entanglement Capacity Estimates and Throughput Measurements of Quantum Channels

The throughput is an important performance metric of entangled qubit distribution quantum networks, and may be characterized by the number of distributed entangled qubit pairs per second (ebps). It is measured over physical quantum network connections using specialized instruments, including photonic entanglement sources and single photon detectors. Extensive theory has been developed to estimate the entangled qubit capacity of quantum channels using abstractions of physical connections. These two quantities both characterize the throughput performance but in different ways, and typically have been hard to relate to each other in concrete terms, in part due to the lack of precise measurements with matching analytical models and derivations. We describe measurements on a physical testbed with fiber connections of lengths 0-75 kilometers. We obtain the normalized analytic capacity estimates using the transmissivity approximations derived using single photon coincidence measurements, and convert them to bounds on throughput (measured in ebps) using a multiplier derived from co-located detector measurements. The results indicate consistent throughput measurements upper-bounded by their analytical capacity estimates across all connections. We show that previous capacity estimates using light measurements are below ebps measurements for some connections, due to the inclusion of non-representative decrease of light levels outside C-band with distance.

Rao, Nageswara↗
DOE OSTI2012

Enhancement of satellite precipitation estimation via unsupervised dimensionality reduction

A methodology to enhance Satellite Precipitation Estimation (SPE) using unsupervised dimensionality reduction (UDR) techniques is developed. This enhanced technique is an extension to the Precipitation Estimation from Remotely Sensed Imagery using an Artificial Neural Network (PERSIANN) and Cloud Classification System (CCS) method (PERSIANN-CCS) enriched using wavelet features combined with dimensionality reduction. Cloud-top brightness temperature measurements from Geostationary Operational Environmental Satellite (GOES-12) are used for precipitation estimation at 4 km 4 km spatial resolutions every 30 min. The study area in the continental United States covers parts of Louisiana, Arkansas, Kansas, Tennessee, Mississippi, and Alabama. Based on quantitative measures, root mean square error (RMSE) and Heidke skill score (HSS), the results show that the UDR techniques can improve the precipitation estimation accuracy. In addition, ICA is shown to have better performance than other UDR techniques; and in some cases, it achieves 10% improvement in the HSS.

Mahrooghy, Majid↗
DOE OSTI2016

Estimating the Propagation of Interdependent Cascading Outages with Multi-Type Branching Processes

In this paper, the multi-type branching process is applied to describe the statistics and interdependencies of line outages, the load shed, and isolated buses. The offspring mean matrix of the multi-type branching process is estimated by the Expectation Maximization (EM) algorithm and can quantify the extent of outage propagation. The joint distribution of two types of outages is estimated by the multi-type branching process via the Lagrange-Good inversion. The proposed model is tested with data generated by the AC OPA cascading simulations on the IEEE 118-bus system. The largest eigenvalues of the offspring mean matrix indicate that the system is closer to criticality when considering the interdependence of different types of outages. Compared with empirically estimating the joint distribution of the total outages, good estimate is obtained by using the multitype branching process with a much smaller number of cascades, thus greatly improving the efficiency. It is shown that the multitype branching process can effectively predict the distribution of the load shed and isolated buses and their conditional largest possible total outages even when there are no data of them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Electromechanical Mode Estimation in the Presence of Periodic Forced Oscillations

Electromechanical modes are inherent to any inter-connected power systems which provide a measure of the small-signal stability margin of the system. A number of algorithms have been developed for the estimation of these modes using synchrophasor measurements. However, most of these algorithms are not designed to operate in the presence of forced oscillations (FO). These FOs are results of periodic rogue input driving the system. When FOs are present, estimates of system modes can be biased depending on the frequency and the amplitude of the FOs. To tackle this problem, a new algorithm is proposed in this paper to estimate system modes in the presence of FOs. In the proposed method, the over-determined modified Yule-Walker method which is used to estimate AR coefficients of an ARMA (Auto-Regressive Moving-Average) signal model is extended to an ARMA with exogenous input (ARMAX) model that incorporates the presence of FOs. Two versions of the proposed method are included in this paper based on the requirement of the information of the duration of FOs in the signal. Furthermore, the results obtained by implementing the proposed algorithm on simulated data and real-world data validate the effectiveness of both versions of the proposed method.

42 ENGINEERING↗
DOE OSTI2022

Bayesian Framework for Multi-Timescale State Estimation in Low-Observable Distribution Systems

To support the smart grid paradigm, there has been a significant increase in sensor deployments and metering infrastructure in distribution systems. However, the measurements provided by these sensors and metering devices are typically sampled at different rates and could suffer from losses during the aggregation process. It is crucial to effectively reconcile the time-series measurements for a reliable state estimation. While weighted least squares has been the traditional approach for state estimation, sparsity-based approaches like matrix completion have become popular due to their superior performance in low-observability conditions. This paper proposes a Bayesian framework for both multi-timescale data aggregation and matrix completion based state estimation. Specifically, the multiscale time-series data aggregated from heterogenous sources are reconciled using a multitask Gaussian process that exploits the spatio-temporal correlations. Here, the resulting consistent timeseries alongwith the confidence bound on the imputations are fed into a Bayesian matrix completion method augmented with linearized power-flow constraints to accurately estimate the states in low-observability conditions. Results on three phase unbalanced IEEE 37 and IEEE 123 bus test systems reveal the superior performance of the proposed Bayesian framework. The computational complexity for the proposed Bayesian framework is also quantified.

42 ENGINEERING↗
DOE OSTI2018

Reanalysis of the Trotter Tibia Quandary and its Continued Effect on Stature Estimation of Past‐Conflict Service Members

Abstract Forensic casework from past‐conflicts relies on the corrected historical Trotter data for stature estimation in Fordisc. For roughly 10 years’, stature estimation using this data has produced point estimates for the tibia that are on average 1.25 inches less than the other long bones. This issue was identified after applying the equations derived from Fordisc to the USS Oklahoma commingled assemblage. Reevaluation of Fordisc revealed that a correction factor of 20 mm, instead of 10 mm, was mistakenly applied to the Trotter tibia data. Historical forensic anthropology reports written at the Defense POW / MIA Accounting Agency were utilized to identify that the overcorrection is isolated to Fordisc 3 with an error rate of 5% of known antemortem statures falling outside of the prediction intervals that relied on the tibia. Further evaluation of the Oklahoma sample indicates the 10 mm correction is still producing point estimates less than the other long bones.

Lynch, Jeffrey James↗
DOE OSTI2022

Single–Well Injection–Drift Test to Estimate Groundwater Velocity

A simple algebraic equation is presented here to estimate the magnitude of groundwater velocity based on data from a single-well injection-drift test thereby eliminating the time-consuming and costly extraction phase. A volume of tracer-amended water was injected by forced-gradient into a single well followed by monitoring of the conservative solute tracers under natural-gradient conditions as their upgradient portions drifted back through the well. The breakthrough curve data from the single well during the drift phase was analyzed to determine the mean travel times of the tracers. The estimated mean upgradient travel distance back through the single well and the mean travel times of the tracers were used in a simple algebraic equation to estimate groundwater velocity. The groundwater velocity based on the single-well injection-drift test was estimated to be approximately 0.64 ft per day. Two transects of observation wells were used to monitor the natural-gradient tracer transport downgradient of the injection well. The one-dimensional, or dual-well, transport of the tracer from the injection well to the nearest downgradient observation well indicated that the groundwater velocity was 0.55 ft per day. The two-dimensional, or multi-well, transport of the center of mass of the tracers indicated that the groundwater velocity was 0.60 ft per day; the dual- and multi-well results were in excellent agreement with those from the single-well and validated the simple algebraic equation. In conclusion, the new single-well method presented here is relatively simple, rapid, and does not require an extraction phase.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Estimation of the Water Table Position in Unconfined Aquifers with MODFLOW 6

Abstract The numerical estimation of the position of the water table in unconfined aquifers is important for many practical applications. Its determination through observations or analytical methods is restricted to a few cases. Therefore, it is often estimated through numerical simulations, which may be affected by numerical artifacts and/or poor stability. We use MODFLOW to estimate the position of the water table for a seemingly simple example problem and demonstrate difficulties that can be faced when performing this kind of numerical simulation. We explain the causes for the numerical challenges that originate from the properties of the mathematical equations that must be solved. Based on the results of more than 600 steady‐state simulations, we show how the stability of the numerical solution can be affected by the values of physical parameters that define the problem (e.g., recharge rate, anisotropy ratio, and other parameters that control the numerical algorithm such as settings of the linear and nonlinear solution methods). Finally, we comment on some best practices to apply numerical simulations to estimate the water table position.

Geology↗
DOE OSTI2023

Methods for Estimating Hydrogen Fuel Tank Characteristics

The pressure vessels needed to store hydrogen for next-generation hydrogen fuel cell vehicles are expected to be a substantial portion of the total system mass, volume, and cost. Gravimetric capacity, volumetric capacity, and cost per kilogram of usable hydrogen are key performance metrics that the U.S. Department of Energy (DOE) uses to determine the viability of hydrogen fuel cell systems. Research and development related to hydrogen storage systems covers a wide range of potential operating conditions, from cryogenic temperatures to high temperatures (above ambient) and low pressure to high pressure. Researchers at PNNL have developed methods for estimating these key pressure vessel characteristics to support on-board hydrogen storage system design and performance evaluation and to support decision-making about DOE hydrogen storage system research investments. This article describes the pressure tank estimation methodology that has been used as a stand-alone calculation and has been incorporated into larger system evaluation tools. The methodology estimates the geometry, mass, and material cost of type I, type III, and type IV pressure vessels based on operating pressure and material strength at the system's operating temperature, using classical thin-wall and thick-wall pressure vessel stress calculations. The geometry, mass, and material cost requirements of the pressure vessel have significant impacts on the total system performance. For example, hydrogen storage materials that can separately achieve a very high hydrogen density can be deemed impractical for use in fuel cell vehicle hydrogen storage systems because the pressure tank containing them is too large, heavy, or expensive. This article describes the design philosophy and analytical process of the tank characteristic estimation methodology, which has been implemented in spreadsheet calculation tools and system-level analysis tools used by DOE researchers. Each of the three tank types (type I, type III, and type IV) uses a different analysis methodology with some common elements. This article also provides examples of implementing the methodology to perform parametric studies of all three pressure vessel types. The goal of this article is to present the methodology in sufficient detail so it can be implemented in other hydrogen fuel cell vehicle design and analysis tools.

42 ENGINEERING↗
DOE OSTI2022

RESULTS OF A VIRTUAL ROUND ROBIN STUDY TO ESTIMATE PROBABILITY OF DETECTION FOR DISSIMILAR METAL WELDS

This paper presents efforts to overcome challenges with empirical probability of detection (POD) estimations in the nuclear power industry through the utilization of a novel virtual flaw method. A virtual round robin (VRR) study was conducted under the Program for Investigation Of NDE by International Collaboration (PIONIC), organized by the United States Nuclear Regulatory Commission (NRC) utilizing data generated by the virtual flaw method. Analysis of results from the VRR was performed by teams from Pacific Northwest National Laboratory (PNNL), Electric Power Research Institute (EPRI), and Aalto University. Empirically derived POD estimations are presented, and challenges associated with obtaining these estimations are discussed. The virtual flaw method is introduced and some details of its implementation for the VRR activity are described. Results from POD analysis of the VRR data by PNNL, EPRI, and Aalto University are presented and a discussion regarding differences in analysis results is provided. Finally, potential future efforts to improve the application of the virtual flaw method and its estimation of POD are discussed.

Probability of detection, Dissimilar Metal Weld, P↗