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Results for “Markov transition field”

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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11 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Learning-Based Real-Time Event Identification Using Rich Real PMU Data

A large-scale deployment of phasor measurement units (PMUs) that reveal the inherent physical laws of power systems from a data perspective enables an enhanced awareness of power system operation. However, the high-granularity and non-stationary nature of PMU data and imperfect data quality could bring great technical challenges for real-time system event identification. To address these challenges, this paper proposes a two-stage learning-based framework. In the first stage, a Markov transition field (MTF) algorithm is exploited to extract the latent data features by encoding temporal dependency and transition statistics of PMU data in graphs. Then, a spatial pyramid pooling (SPP)-aided convolutional neural network (CNN) is established to efficiently and accurately identify power events. The proposed method fully builds on and is also tested on a large real-world dataset from several tens of PMU sources (and the corresponding event logs), located across the U.S., with a time span of two consecutive years. We report the numerical results validate that our method has high identification accuracy while showing good robustness against poor data quality.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

K-Spin Hamiltonian for Quantum-Resolvable Markov Decision Processes

The Markov decision process is the mathematical formalization underlying the modern field of reinforcement learning when transition and reward functions are unknown. We derive a pseudo-Boolean cost function that is equivalent to a K-spin Hamiltonian representation of the discrete, finite, discounted Markov decision process with infinite horizon. This K-spin Hamiltonian furnishes a starting point from which to solve for an optimal policy using heuristic quantum algorithms such as adiabatic quantum annealing and the quantum approximate optimization algorithm on near-term quantum hardware. In arguing that the variational minimization of our Hamiltonian is approximately equivalent to the Bellman optimality condition for a prevalent class of environments we establish an interesting analogy with classical field theory. Along with proof-of-concept calculations to corroborate our formulation by simulated and quantum annealing against classical Q-Learning, we analyze the scaling of physical resources required to solve our Hamiltonian on quantum hardware.

Hamiltonian↗
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↗
DOE OSTI2022

A Note on the Effects of Linear Topology Preservation in Monte Carlo Simulations of Knotted Proteins

Monte Carlo simulations are a powerful technique and are widely used in different fields. When applied to complex molecular systems with long chains, such as those in synthetic polymers and proteins, they have the advantage of providing a fast and computationally efficient way to sample equilibrium ensembles and calculate thermodynamic and structural properties under desired conditions. Conformational Monte Carlo techniques employ a move set to perform the transitions in the simulation Markov chain. While accepted conformations must preserve the sequential bonding of the protein chain model and excluded volume among its units, the moves themselves may take the chain across itself. We call this a break in linear topology preservation. In this manuscript, we show, using simple protein models, that there is no difference in equilibrium properties calculated with a move set that preserves linear topology and one that does not. However, for complex structures, such as those of deeply knotted proteins, the preservation of linear topology provides correct equilibrium results but only after long relaxation. In any case, to analyze folding pathways, knotting mechanisms and folding kinetics, the preservation of linear topology may be an unavoidable requirement.

Especial, João N. C. (ORCID:0000000254765370)↗
DOE OSTI2022

Classification of Photovoltaic Failures with Hidden Markov Modeling, an Unsupervised Statistical Approach

Failure detection methods are of significant interest for photovoltaic (PV) site operators to help reduce gaps between expected and observed energy generation. Current approaches for field-based fault detection, however, rely on multiple data inputs and can suffer from interpretability issues. In contrast, this work offers an unsupervised statistical approach that leverages hidden Markov models (HMM) to identify failures occurring at PV sites. Using performance index data from 104 sites across the United States, individual PV-HMM models are trained and evaluated for failure detection and transition probabilities. This analysis indicates that the trained PV-HMM models have the highest probability of remaining in their current state (87.1% to 93.5%), whereas the transition probability from normal to failure (6.5%) is lower than the transition from failure to normal (12.9%) states. A comparison of these patterns using both threshold levels and operations and maintenance (O&M) tickets indicate high precision rates of PV-HMMs (median = 82.4%) across all of the sites. Although additional work is needed to assess sensitivities, the PV-HMM methodology demonstrates significant potential for real-time failure detection as well as extensions into predictive maintenance capabilities for PV.

classification↗
DOE OSTI2024

Quantification of locked mode instability triggered by a change in confinement

Abstract This work presents the first analysis of the disruptive locked mode (LM) triggered by the dynamics of a confinement change. It shows that, under certain conditions, the LM threshold during the transient is significantly lower than expected from steady states. We investigate the sensitivity to a controlled n = 1 error field (EF) activated prior to the L-H transition in the COMPASS tokamak, at q 95 ∼ 3, β N ∼ 1, and using EF coils on the high-field side of the vessel. A threshold for EF penetration subsequent to the L-H transition is identified, which shows no significant trend with density or applied torque, and is an apparent consequence of the reduced intrinsic rotation of the 2/1 mode during this transient phase. This finding challenges the assumption made in theoretical and empirical works that natural mode rotation can be predicted by global plasma parameters and urges against using any parametric EF penetration scaling derived from steady-state experiments to define the EF correction strategy in the entire discharge. Furthermore, even at EFs below the identified penetration threshold, disruptive locking of sawtooth-seeded 2/1 tearing modes is observed after about 30% of L-H transitions without external torque.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

First observation of RMP ELM mitigation on MAST Upgrade

Abstract The first experimental attempts at controlling edge localised modes (ELMs) via the application of resonant magnetic perturbations on the MAST Upgrade tokamak are reported. Using the linear MHD model MARS-F, the phase shift between the upper and lower coil rows ΔΦ was optimised for toroidal mode number n = 1 and n = 2 fields, to provide forward guidance to experiments. In low β N discharges, the application of n = 1 3D fields caused the ELM frequency f E L M to increase by over a factor 20 relative to the reference, and also induced a locked mode, which did not cause a plasma termination nor an H-L back transition. However when β N was raised, this induced locked mode caused plasma termination which precluded mitigation access. Initially, applying a numerically optimised n = 2 field had no effect. However applying a rigid toroidal shift to this field caused a locked mode disruption, demonstrating the presence of a substantial n = 2 error field. Coil current ramps were conducted with ΔΦ set at 6 different values, resulting in either locked mode disruptions or no effect, but mitigation with n = 2 fields was not established.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Machine learning for modern power distribution systems: Progress and perspectives

The application of machine learning (ML) to power and energy systems (PES) is being researched at an astounding rate, resulting in a significant number of recent additions to the literature. As the infrastructure of electric power systems evolves, so does interest in deploying ML techniques to PES. However, despite growing interest, the limited number of reported real-world applications suggests that the gap between research and practice is yet to be fully bridged. To help highlight areas where this gap could be narrowed, this article discusses the challenges and opportunities in developing and adapting ML techniques for modern electric power systems, with a particular focus on power distribution systems. These systems play a crucial role in transforming the electric power sector and accommodating emerging distributed technologies to mitigate the impacts of climate change and accelerate the transition to a sustainable energy future. The objective of this article is not to provide an exhaustive overview of the state-of-the-art in the literature, but rather to make the topic accessible to readers with an engineering or computer science background and an interest in the field of ML for PES, thereby encouraging cross-disciplinary research in this rapidly developing field. To this end, the article discusses the ways in which ML can contribute to addressing the evolving operational challenges facing power distribution systems and identifies relevant application areas that exemplify the potential for ML to make near-term contributions. At the same time, key considerations for the practical implementation of ML in power distribution systems are discussed, along with suggestions for several potential future directions.

Marković, Marija (ORCID:0000000247839837)↗
DOE OSTI2022

Universal tripartite entanglement signature of ungappable edge states

Gapped two-dimensional topological phases can feature ungappable edge states which are robust even in the absence of protecting symmetries. In this Letter we argue that a multipartite entanglement measure recently proposed in the context of holography, the Markov gap, provides a universal diagnostic of ungappable edge states. Defined as a difference of the reflected entropy and mutual information h(A:B)=S R (A:B)-I(A:B) between two parties, we argue that for A,B being adjacent subregions in the bulk h=$\frac{c+}{3}$ ln 2, where c + is the minimal total central charge of the boundary theory. As evidence, we prove that h=0 for string-net models and numerically verify that h=$\frac{|C|}{3}$ ln 2 for a Chern-C insulator. Our Letter establishes a unique bulk entanglement criteria for the presence of a conformal field theory on the boundary.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

12 C(e,e'pN) measurements of short range correlations in the tensor-to-scalar interaction transition region

High-momentum configurations of nucleon pairs at short-distance are probed using measurements of the $^{12}$C$(e,e'p)$ and $^{12}$C$(e,e'pN)$ reactions (where $N$ is either $n$ or $p$), at high-$Q^2$ and $x_B>1.1$. The data span a missing-momentum range of 300--1000 MeV/c and are predominantly sensitive to the transition region of the strong nuclear interaction from a Tensor to Scalar interaction. The data are well reproduced by theoretical calculations using the Generalized Contact Formalism with both chiral and phenomenological nucleon-nucleon ($NN$) interaction models. This agreement suggests that the measured high missing-momentum protons up to $1000$ MeV/c predominantly belong to short-ranged correlated (SRC) pairs. The measured $^{12}$C$(e,e'pN)$ / $^{12}$C$(e,e'p)$ and $^{12}$C$(e,e'pp)$ / $^{12}$C$(e,e'pn)$ cross-section ratios are consistent with a decrease in the fraction of proton-neutron SRC pairs and increase in the fraction of proton-proton SRC pairs with increasing missing momentum. This confirms the transition from an isospin-dependent tensor $NN$ interaction at $\sim 400$ MeV/c to an isospin-independent scalar interaction at high-momentum around $\sim 800$ MeV/c as predicted by theoretical calculation.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Extraction of Beam-Spin Asymmetries from the Hard Exclusive π + Channel off Protons in a Wide Range of Kinematics

We have measured beam-spin asymmetries to extract the $\sin\phi$ moment $A_{LU}^{\sin\phi}$ from the hard exclusive $\vec{e} p \to e^\prime n \pi^+$ reaction above the resonance region, for the first time with nearly full coverage from forward to backward angles in the center-of-mass. The $A_{LU}^{\sin\phi}$ moment has been measured up to 6.6 GeV$^{2}$ in $-t$, covering the kinematic regimes of Generalized Parton Distributions (GPD) and baryon-to-meson Transition Distribution Amplitudes (TDA) at the same time. The experimental results in very forward kinematics demonstrate the sensitivity to chiral-odd and chiral-even GPDs. In very backward kinematics where the TDA framework is applicable, we found $A_{LU}^{\sin\phi}$ to be negative, while a sign change was observed near 90$^\circ$ in the center-of-mass. The unique results presented in this paper will provide critical constraints to establish reaction mechanisms that can help to further develop the GPD and TDA frameworks.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗