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DOE OSTI2022

Security Enhancement of Network Constraint Grid-Edge Energy Management System

Network constrained grid edge energy management system (EMS) provides economic solution for active and reactive power dispatch of distributed energy resources (DERs) at the grid edge level. Grid edge EMS ensures secure interconnection of a circuit segment to the distribution system by maintaining grid code requirements (e.g. IEEE 1547–2018). Grid edge EMS is dependent on communication to receive load measurement, which brings a risk of unobservable false data injection attacks (FDIAs). To mitigate the risk, this paper proposes a framework to enhance resilient operation of grid edge EMS by detecting the unobservable FDIAs on loads and replacing them with forecasted values. In this work, a two-step detection algorithm is proposed. In first step, conventional residual based algorithm is deployed. Autoencoder (AE) based data driven mechanism is included in second step to detect the presence of unobservable FDIAs. After ensuring the presence of FDIA, its specific location is detected by checking the maximum residue values till the predefined threshold value is reached. Detected false data injected loads are then replaced with forecasted load values following long-short term memory (LSTM) based forecast to ensure resilient performance of grid edge EMS in the presence of attacks. This proposed security enhancement framework for grid edge EMS is evaluated in IEEE 13 bus system with three integrated DERs. Numerical simulation shows the validation of the proposed framework by reducing voltage violation in real operation of grid edge EMS.

cyber attack detection↗

NASA NTRS1980

Reduced-order Kalman filtering with incomplete observability

Kalman filtering is considered with reference to linear stochastic dynamic systems without complete observability. It is shown that the canonical decomposition theorem can be extended to the stochastic case and the matrix Riccati equation of the Kalman filter is order-reducible if some states are not observable. The inclusion of unobservable states in Kalman filtering makes the unobservable states 'asymptotically' observable in the filter if these unobservable states are dynamically connected to observable states and asymptotically stable. The reduced-order Kalman filter saves computation time when compared to the conventional Kalman filter.

Yonezawa, K.↗

NASA NTRS1990

A simulation survey of galaxy interactions

Many carefully selected samples of interacting galaxies have been observed extensively in attempts to clarify whether interaction produces activity in galaxies. Because the sample members represent a wide range of encounter parameters and times, one can then study whether there are correlations between observable encounter features and, for example, Seyfert activity. On the other hand, in theoretical studies, simulations typically deal with either time-consuming detailed modelling of single galaxy pairs or tracing a few model encounters over time. The authors extend the observational survey approach by combining it with a simulation survey. The authors are conducting a survey of model encounters, covering the most important encounter parameters over a wide range. Some parameters, such as companion structure and initial velocity, are demonstratably less important and can be ignored in a first pass. The parameter range must be richly enough sampled so that the authors can evaluate the uniqueness of the observable morphology and velocity structure of the resulting simulated pairs to diagnose unobservable companion orbit parameters. They are using a self-gravitating polar n-body code run on the Cray X-MP at the Alabama Supercomputer Network. For each simulation, the authors have stellar and gas distributions predicted over, typically, a billion years, along with information on gas motions within the disk and any material captured by the companion or lost to the system. Features of disturbed spiral galaxies are sensitive enough to time and encounter parameters so that a match of the simulation survey results to observations can be applied as starting points to infer unobservable orbital or system parameters in actual sample members. This should enable them to examine whether interesting observed properties (Seyfert activity, nuclear star-formation rate) are functions of unobservable dynamical properties which characterize each encounter. Any correlations (or lack of some expected ones) will provide strong clues as to how or whether these phenomena are related to interactions. Aside from its use with such observed samples, this survey should greatly speed determination of initial orbital parameters for more detailed subsequent simulations of individual systems.

Byrd, Gene G.↗

DOE OSTI2025

Ensemble‐Based, Large‐Eddy Reconstruction of Wind Turbine Inflow in a Near‐Stationary Atmospheric Boundary Layer Through Generative Artificial Intelligence

ABSTRACT To validate the second‐by‐second dynamics of turbines in field experiments, it is necessary to accurately reconstruct the winds going into the turbine. Current time‐resolved inflow reconstruction techniques estimate wind behavior in unobserved regions using relatively simple spectral‐based models of the atmosphere. Here, we develop a technique for time‐resolved inflow reconstruction that is rooted in a large‐eddy simulation model of the atmosphere. Our “large‐eddy reconstruction” technique blends observations and atmospheric model information through a diffusion model machine learning algorithm, allowing us to generate probabilistic ensembles of reconstructions for a single 10‐min observational period. Our generated inflows can be used directly by aeroelastic codes or as inflow boundary conditions in a large‐eddy simulation. We verify the second‐by‐second reconstruction capability of our technique in three synthetic field campaigns, finding positive Pearson correlation coefficient values () between ground‐truth and reconstructed streamwise velocity, as well as smaller positive correlation coefficient values for unobserved fields (spanwise velocity, vertical velocity, and temperature). We validate our technique in three real‐world case studies by driving large‐eddy simulations with reconstructed inflows and comparing to independent inflow measurements. The reconstructions are visually similar to measurements, follow desired power spectra properties, and track second‐by‐second behavior ().

17 WIND ENERGY↗

DOE OSTI2022

First observation of the decay of the 13/2 + isomer in 183 Hg and B(M 2) systematics of neutron transitions across the nuclear chart

The decay of the 13/2 + isomeric state in 183 Hg was observed for the first time following the a decay of the 13/2 + isomer in 187 Pb produced in the 142 Nd( 50 Cr, 2p3n) reaction. Using $\alpha - \gamma$ delayed coincidence measurements, the half-life of this isomer was measured to be 290(30) μs. This isomer is proposed to deexcite by an unobserved low-energy M2 transition to the known 9/2 - member of a strongly prolate-deformed 7/2 - [514] band, followed by a 105-keV M1 transition to the bandhead. A lower limit of B(M2) >= 0.018 W.u. was deduced for the unobserved transition. The presumed retardation is proposed to be due to the notable shape change between the initial, nearly spherical, and the final, strongly deformed, states. A similar scenario is also considered for the 13/2 + isomer in 181 Hg, suggesting both are cases of shape isomers. The B(M2) systematics of neutron transitions across the nuclear chart is discussed.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Dynamic signal recovery in distribution grids using compressive lossy measurements

Distribution system state estimation requires reliable aggregation of the measured data. However, the large volume of the measured data imposes a significant stress on the underlying communication infrastructure. With the challenges associated with measurement availability, current distribution systems are typically unobservable. To cope with the unobservability issue, compressive sensing theory allows us to recover system state information from a small number of measurements provided the states of the distribution system exhibit sparsity. In this paper, we evaluate the robustness of an updated Kalman filtered modified compressive sensing (KF-ModCS) technique that dynamically estimates the grid states using a small fraction of measured data. In practice, measurements used for sparsity based state estimation may also be intermittent due to communication network induced losses. Further, to understand the effect of packet losses on KF-ModCS, we provide an upper bound for the expected variances of the state estimation error for a given rate of information loss. This upper bound is further improved if the support set of the sparse signal that characterizes the state dynamics does not change over time and/or the reduced model is observable. Simulations based on two practical data sets collected from actual customers in a distribution grid validate the theoretical results.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2022

Photoelectron spectroscopy of cryogenically cooled NiO 2 – via slow photoelectron velocity-map imaging

In this work, high-resolution anion photoelectron spectra of cryogenically cooled NiO 2 – anions, obtained using slow photoelectron velocity-map imaging (cryo-SEVI), are presented in tandem with coupled cluster electronic structure calculations including relativistic effects. The experimental spectra encompass the $\tilde{\text{X}}^1Σ_{\text{g}}^+ ← \tilde{\text{X}}^2Π_{\text{g}}, ã^3Π{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$, and $\tilde{\text{A}}^1Π_{\text{g}} ← \tilde{\text{X}}^2Π_{\text{g}}$ photodetachment transitions of linear ONiO 0/– , revealing previously unobserved vibrational structure in all three electronic bands. The high-resolution afforded by cryo-SEVI allows for the extraction of vibrational frequencies for each state, consistent with those previously measured in the ground state and in good agreement with scalar-relativistic coupled-cluster calculations. Previously unobserved vibrational structure is observed in the $ã^3Π_{\text{g}}$ and $Ã^1Π_{\text{g}}$ states and is tentatively assigned. Further, a refined electron affinity of 3.0464(7) eV for NiO 2 is obtained as well as precise term energies for the ã and Ã states of NiO 2 of 0.3982(7) and 0.7422(10) eV, respectively. Numerous Franck–Condon forbidden transitions involving the doubly degenerate $ν_2$ bending mode are observed and ascribed to Herzberg-Teller coupling to an excited electronic state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

19 Ne level structure for explosive nucleosynthesis

Ne 19 is an important isotope in nuclear astrophysics due to its role in both the F 18 ( p , α ) O 15 and O 15 ( α , γ ) Ne 19 reactions in novae and Type I x-ray bursts, respectively. The energy levels of Ne 19 near the α and proton thresholds ( S α = 3529 keV, S p = 6410 keV) correspond to resonances in both of these reactions. Previous measurements to study the structure of Ne 19 have focused on both regions in an effort to constrain these reaction rates. Discrepancies in the energies, spins, and parities for levels in Ne 19 from previous measurements contribute to the reaction-rate uncertainties. Gamma rays from the depopulation of excited states in Ne 19 were measured to reduce the level-energy uncertainties and inconsistencies in previous spin-parity assignments.The F 19 ( He 3 , t ) Ne 19 reaction was used to elucidate the structure of Ne 19 levels up to E x = 6.9 MeV. The reaction products were measured using Gammasphere ORRUBA: Dual Detectors for Experimental Structure Studies—a coupling of the Oak Ridge Rutgers University Barrel Array and Gammasphere at Argonne National Laboratory. Tritons produced in the reaction were measured in coincidence with γ rays from the deexcitation of Ne 19 energy levels. Previously unobserved transitions allowed for discrepancies in the resonance properties relevant to these two reactions to be resolved. In total, 41 transitions from 21 energy levels were measured in Ne 19 , with 21 of those transitions being previously unobserved. Of particular importance, transitions from two 3 / 2 + states with energies of 6423(3) and 6441(3) keV, crucial for accurate estimations of the F 18 ( p , α ) O 15 reaction rate, were found. Energies and spin-parities of important energy levels near the proton and α thresholds were measured and some of the discrepancies in previous measurements were resolved. Overall, measurement of the two near-threshold 3 / 2 + states reduced the calculated upper limit of the F 18 ( p , α ) O 15 reaction rate by factors of 1.5–17 in the nova temperature range.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Ab initio calculation of the β decay from 11 Be to a 10 Be + $\textit{p}$ resonance

In this work, the exotic β -delayed proton emission is calculated in 11 Be from first principles using chiral two- and three-nucleon forces. To investigate the unexpectedly large branching ratio measured in Ayyad et al. [Phys. Rev. Lett. 123, 082501 (2019)], we calculate the proposed (1/2 + , 1/2) proton resonance in 11 B using the no-core shell model with continuum. This calculation helps to address whether this enhancement is caused by unknown dark decay modes or an unobserved proton resonance. We report a branching ratio of $b_p$ = (1.3 ± 0.5) × 10 –6 , suggesting that its unexpectedly large value is caused by an unobserved proton resonance in 11 B.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Optimal Voltage Control in Low-Observable Unbalanced Distribution Systems

The increased integration of distributed energy resources (DERs) in distribution systems brings both advantages and technical challenges. High levels of DERs can often cause over/under voltage problems. Classical voltage control algorithms are based on full knowledge of voltage states across all nodes in the system. However, this may not be a practical assumption since many locations in distribution systems are unobservable. Therefore, this paper proposes a new model predictive control (MPC) based control algorithm that accounts for system unobservability to efficiently eliminate voltage violations with as low as 50% fraction of observable nodes. Additionally, an analytical voltage sensitivity framework is employed to quickly determine the change in voltage states due to PV injections. The effectiveness of the proposed method is validated via simulations on the unbalanced IEEE 37 node test system.

Abujubbeh, Mohammad↗

DOE OSTI2020

Application of Detectability Analysis for Power System Dynamic State Estimation

To guide the measurement placement and selection for dynamic state estimation in power systems, it is important to determine the existence of an observer that can estimate the dynamic states. Past studies have been focused on observability analysis, which determines whether the initial states can be uniquely determined. In this paper, detectability analysis is proposed to determine whether the current states can be estimated as time involving. Through the proposed detectability analysis, the states of an unobservable system can be categorized into the detectable states and undetectable states. It is shown that if the eigenvalues corresponding to the unobservable states are stable, the states are detectable and their estimates will converge to the true ones asymptotically. The study can be used to guide measurement placement and selection.

Detectability, dynamic state estimation↗

DOE OSTI2021

Detecting False Data Injection Attacks in Smart Grids: A Semi-Supervised Deep Learning Approach

The dependence on advanced information and communication technology increases the vulnerability in smart grids under cyber-attacks. Recent research on unobservable false data injection attacks (FDIAs) reveals the high risk of secure system operation, since these attacks can bypass current bad data detection mechanisms. To mitigate this risk, this paper proposes a data-driven learning-based algorithm for detecting unobservable FDIAs in distribution systems. We use autoencoders for efficient dimension reduction and feature extraction of measurement datasets. Further, we integrate the autoencoders into an advanced generative adversarial network (GAN) framework, which successfully detects anomalies under FDIAs by capturing the unconformity between abnormal and secure measurements. Also, considering that the datasets collected from practical power systems are partially labeled due to expensive labeling costs and missing labels, the proposed method only requires a few labeled measurement data in addition to unlabeled data for training. Numerical simulations in three-phase unbalanced IEEE 13-bus and 123-bus distribution systems validate the detection accuracy and efficiency of this method.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Optimizing resource allocation in Miscanthus breeding via sparse testing designs for genomic prediction

Phenotyping high-biomass perennial crops is laborious and the rate of genetic gain in conventional perennial crop breeding programs is typically low. So, it is especially important to identify methods that produce efficiency gains in the breeding process. Miscanthus is a C4 perennial grass with favorable characteristics for producing biomass as a feedstock for biofuels and diverse bio-based products. Increasing biomass yield will increase profitability and environmental benefits, so it is a key target for Miscanthus breeding. In addition, the identification of well-adapted genotypes across a wide range of environmental conditions requires the establishment of multi-environment trials (METs). Sparse testing is a genomic prediction-based strategy that reduces the phenotyping costs in METs by selecting a subset of genotypes to evaluate in a subset of environments and then predicts the performance of the unobserved genotype-environment combinations. A Miscanthus sacchariflorus (MSA) population comprising 336 genotypes observed across three environments was analyzed implementing sparse testing designs. Three prediction models considering main effects (environments, genotypes, genomic) and interaction effects (genotype-by-environment; G×E interaction) were implemented for forecasting dry biomass yield (YDY), total culm (TCM), average internode length (AIL), and culm node number (CNN). Multiple calibration sets based on different compositions and sizes were considered to evaluate performance in terms of the predictive ability (PA) and the mean square error (MSE) for a fixed testing set size. The training set size ranged from 52 to 112 to predict a fixed set of 224 unobserved genotypes across all three environments. The results showed that the model accounting for G×E interaction consistently presented the highest PA and the lowest MSE: for CNN (PA: ~0.77, MSE: ~0.5) and YDY (PA: ~0.70, MSE: ~1.3) while for TCM and AIL these ranged from ~0.28 to 0.41 and ~1.3 to 4.3, respectively. Overall, varying training sets and allocation strategies did not affect PA and MSE, with 52 non-overlapping and 0 overlapping genotypes per environment as the optimal cost-effective allocation framework. This suggests that implementing sparse testing designs could significantly reduce phenotyping costs by fivefold, without compromising PA in breeding programs for perennial crops such as Miscanthus.

Miscanthus sacchariflorus (MSA)↗

DOE OSTI2020

Multi-Trait Regressor Stacking Increased Genomic Prediction Accuracy of Sorghum Grain Composition

Genomic prediction has enabled plant breeders to estimate breeding values of unobserved genotypes and environments. The use of genomic prediction will be extremely valuable for compositional traits for which phenotyping is labor-intensive and destructive for most accurate results. We studied the potential of Bayesian multi-output regressor stacking (BMORS) model in improving prediction performance over single trait single environment (STSE) models using a grain sorghum diversity panel (GSDP) and a biparental recombinant inbred lines (RILs) population. A total of five highly correlated grain composition traits—amylose, fat, gross energy, protein and starch, with genomic heritability ranging from 0.24 to 0.59 in the GSDP and 0.69 to 0.83 in the RILs were studied. Average prediction accuracies from the STSE model were within a range of 0.4 to 0.6 for all traits across both populations except amylose (0.25) in the GSDP. Prediction accuracy for BMORS increased by 41% and 32% on average over STSE in the GSDP and RILs, respectively. Prediction of whole environments by training with remaining environments in BMORS resulted in moderate to high prediction accuracy. Our results show regression stacking methods such as BMORS have potential to accurately predict unobserved individuals and environments, and implementation of such models can accelerate genetic gain.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1973

Reaction Li-6/p, pt/ at 590 MeV.

A lithium target enriched to 95.6 per cent of Li-6 and 0.685 cm thick was bombarded in the 590-MeV proton beam of a synchrocyclotron. Coincident events were detected in a double telescope arrangement. The characteristics of the unobserved three-nucleon residual system calculated for each event include the missing energy, the longitudinal recoil, and the transverse recoil. The cross-section data obtained indicate that zero recoil momentum for the unobserved three-nucleon recoil system is the most likely situation.

Dollhopf, W.↗

NASA NTRS1974

Attitude control concepts for precision-pointing nonrigid spacecraft

Literal criteria are developed for the controllability and observability of general models of flexible spacecraft. Results are interpreted in special cases and in physical terms, permitting in some cases the identification of uncontrollable and unobservable states simply by examination of scalars composed of modal parameters and location matrices for sensors and actuators. A procedure is established for isolation of uncontrollable states, whereby sensor and actuator configurations assure that uncontrollable flexible mode states are also unobservable; in many applications such states can then be removed by coordinate truncation.

Likins, P. W.↗

NASA NTRS1974

The influence of spacecraft flexibility on system controllability and observability

Literal criteria are developed for the controllability and observability of general models of flexible spacecraft. Results are interpreted in special cases and in physical terms, permitting in some cases the identification of uncontrollable and unobservable states simply by examination of scalars composed of modal parameters and location matrices for sensors and actuators. A procedure is established for isolation of uncontrollable states, whereby sensor and actuator configuration assure that uncontrollable flexible mode states are also unobservable; in many applications such states can then be removed by coordinate truncation.

Ohkami, Y.↗

NASA NTRS1984

Yaw Attitude Estimation for the Tracking and Data Relay Satellite System

The Tracking and Data Relay Satellite System (TDRSS) uses a groundbased attitude determination algorithm to open loop point the satellite's high data rate antennas. The spacecraft is able to measure its pitch and roll attitude, but its yaw attitude is periodically unobservable. The ground software uses a state-space estimator, an adaptation of a Luenberger observer, to predict the spacecraft yaw angle during these unobservable periods. It contains states associated with the roll/yaw dynamics and the on-board control law. The accuracy is limited by the modeling fidelity of the disturbance torques acting on the spacecraft. After initial operating problems were cleared up, the operation of the estimator has converged to predicted performance.

Staich, S.↗