Distributed, Active Grid-interrogation and Signal Analysis for Advanced Protective Relaying
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24 POWER TRANSMISSION AND DISTRIBUTION↗
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Distributed, Active Grid-interrogation and Signal Analysis for Advanced Protective Relaying
Explore the source record for details and available documents.
24 POWER TRANSMISSION AND DISTRIBUTION↗
A Privacy-Aware Federated Learning Framework for Distributed Energy Resource Analytics in Constrained Environments
To be resilient against extreme weather events, the rural communities in Puerto Rico are leveraging distributed energy resources (DER). However, computing frameworks sup-porting the grid in critical decision-making are still largely centralized. Sensitive consumer data are transmitted over the Internet or cellular networks to a secondary or tertiary node. It guarantees better situational awareness at the cost of a wider attack surface, jeopardizing user privacy, as more DER come online. Cloud, Edge, and Fog computing all require data aggregation at some level. This paper introduces a privacy-aware federated learning framework that leverages the Fog model by pushing analytics all the way to the DER and load assets. These local models train on individual asset data and transmit only learned parameters (such as weights) over secure communications to a global decision-maker. By abstracting personally identifiable consumer data without impacting decision optimality, this framework better aligns with distributed power generation paradigm.
Sundararajan, Aditya↗
Operational Analytics Studies for ATLAS Distributed Computing: Data Popularity Forecast and Utilization of the WLCG Centers
Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Detecting Anomalies for Fire Prevention in Distribution Systems: Challenges and Analytical Techniques
Electric utilities in California have historically been linked to up to 10% of wildfires. To mitigate this risk, Southern California Edison has invested significantly in wildfire prevention strategies, including undergrounding cables and enhancing equipment inspections. This article explores a novel approach to fire prevention by detecting anomalies in the distribution system that may indicate potential fire hazards. The focus is on identifying arcing conditions through high-resolution point-on-wave (POW) measurements. Arcing, a precursor to fires, is challenging to detect due to its subtle transients and complex system topology. The article discusses the use of advanced signal processing and machine learning techniques, such as spectral correlation function and discrete wavelet transform, to extract features from POW data and accurately identify arcing events. The study demonstrates a high accuracy rate in detecting arcing, paving the way for improved fire prevention measures in electric distribution systems.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Graph Analytics on Jellyfish topology
Because large unstructured datasets is important for many science domains, distributed graph analytics is critical to many scientists. Unfortunately, obtaining scaling and performance for irregular communication is challenging because contemporary network interconnects are primarily designed to maximize bandwidths of fixed-neighborhoods large-message exchanges (e.g., stencils). Although there is no consensus on the “best” network topologies for irregular communication, unstructured graph-based interconnects can be more suitable. We analyze three popular graph workloads – clustering, pattern enumeration, and traversal — on comparable networks (in terms of resources and costs) constructed from Jellyfish Random Regular, Dragonfly and Fat tree topologies, varying the routing algorithms. Using packet-level simulations, we demonstrate up to 60% improvement in communication time with Jellyfish due to diversity of the short paths between arbitrary endpoints, which can reduce overall network stalls and congestion.
Graph Analytics, network topology, interconnect, H↗
Enabling resiliency using microgrids with dynamic boundaries
Not Available
24 POWER TRANSMISSION AND DISTRIBUTION↗
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↗
Noise driven emittance growth with coherent forces
Noise driven emittance growth is modified by the presence of coherent forces. Closed form asymptotic growth rates are given for two frequency distributions. The analytic results are compared with tracking, showing good agreement. Purely numerical results for a parabolic frequency distribution are also presented. If a small antidamping force is present then additional, real frequency shifts increase growth rates.
43 PARTICLE ACCELERATORS↗
Distribution of centrality measures on undirected random networks via the cavity method
The Katz centrality of a node in a complex network is a measure of the node’s importance as far as the flow of information across the network is concerned. For ensembles of locally tree-like undirected random graphs, this observable is a random variable. Its full probability distribution is of interest but difficult to handle analytically because of its “global” character and its definition in terms of a matrix inverse. Leveraging a fast Gaussian Belief Propagation-Cavity algorithm to solve linear systems on tree-like structures, we show that i) the Katz centrality of a single instance can be computed recursively in a very fast way, and ii) the probability P ( K ) that a random node in the ensemble of undirected random graphs has centrality K satisfies a set of recursive distributional equations, which can be analytically characterized and efficiently solved using a population dynamics algorithm. We test our solution on ensembles of Erdős-Rényi and Scale Free networks in the locally tree-like regime, with excellent agreement. The analytical distribution of centrality for the configuration model conditioned on the degree of each node can be employed as a benchmark to identify nodes of empirical networks with over- and underexpressed centrality relative to a null baseline. We also provide an approximate formula based on a rank- 1 projection that works well if the network is not too sparse, and we argue that an extension of our method could be efficiently extended to tackle analytical distributions of other centrality measures such as PageRank for directed networks in a transparent and user-friendly way.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Cutting Corners: Curvilinear-Surface-Based Gravity Models for Asteroids and Comets
We report that asteroids and comets are often highly irregular in shape and can contain large density inhomogenities. For such bodies, the simple point-mass gravity model does not capture the dynamics of the environment and this has driven a need for higher fidelity alternatives. A number of approaches have been developed with one of the most popular being the analytic polyhedral model. The analytic polyhedral model approximates the irregular shaped body as a polyhedron, typically consisting of triangular facets, with some assumed internal density distribution. Several analytic formulations have been derived for the gravitational fields of a constant-density polyhedron. A thorough history of the model is provided in Ref. [6] as well as a comparison of the implementations of Refs. [2–4]. The three versions provide near identical accuracy with the implementation of Werner requiring the fewest transcendental function evaluations suggesting its superior efficiency. Implementations with linear density contrasts and arbitrary polynomial density contrasts have also been developed.
79 ASTRONOMY AND ASTROPHYSICS↗
Pd/C promotes C–H bond activation and oxidation of p -hydroxybenzoate during hydrogenolysis of poplar
Hydrogenolysis of lignin generates a portfolio of products, the yields of which are generally calculated using a subset of phenolic monomers that are dependent on the lignin composition, product distribution, and analytical technique. Some lignins are naturally γ-acylated; poplar lignins, for example, have p-hydroxybenzoate groups on 1–15% of their syringyl subunits. Upon hydrogenolysis, it is generally assumed that the p-hydroxybenzoate is cleaved before the deacylated lignin is depolymerized. Hydrogenolysis of model γ-p-hydroxybenzoylated β-aryl ethers do not, however, produce the deacylated β-aryl ether intermediates, as was previously conjectured; products instead derive from palladium-assisted reactions on the cinnamyl p-hydroxybenzoates resulting in initial β-ether cleavage. The p-hydroxybenzoate moiety itself also undergoes carboxylate-assisted palladium-catalyzed C–H bond activation to form the 2,4-dihydroxybenzoate, that subsequently converts to the 2,4-dihydroxycyclohex-1-enoate. Furthermore, these details underscore previously unrecognized pathways and products that are key to understanding the different hydrogenolysis product distributions from naturally acylated lignins that are prevalent biomass-conversion feedstocks.
09 BIOMASS FUELS↗
Condensation and evaporation of boson stars
Abstract Axion-like particles, including the QCD axion, are well-motivated dark matter candidates. Numerical simulations have revealed coherent soliton configurations, also known as boson stars, in the centers of axion halos. We study evolution of axion solitons immersed into a gas of axion waves with Maxwellian velocity distribution. Combining analytical approach with controlled numerical simulations we find that heavy solitons grow by condensation of axions from the gas, while light solitons evaporate. We deduce the parametric dependence of the soliton growth/evaporation rate and show that it is proportional to the rate of the kinetic relaxation in the gas. The proportionality coefficient is controlled by the product of the soliton radius and the typical gas momentum or, equivalently, the ratio of the gas and soliton virial temperatures. We discuss the asymptotics of the rate when this parameter is large or small.
Physics↗
Dihadron correlations in small- x DIS at NLO: transverse momentum dependent fragmentation
We compute the inclusive dihadron cross-section in Deep Inelastic Scattering at next-to-leading order (NLO) and small x in the Color Glass Condensate. We focus on the kinematic limit where the hadrons are produced at forward rapidities (in the direction of the virtual photon) and back-to-back in the transverse plane. Our calculation demonstrates that the coefficient of the Sudakov double logarithm for this process is –$\frac{αs}{2π}$ [C F + $\frac{Nc}{2}$] instead of –$\frac{αsNc}{4π}$ when back-to-back jets are measured in the final state. To preserve the universality of the Sudakov soft factor associated with the Weizsäcker-Williams transverse momentum dependent (TMD) gluon distribution, we promote the collinear fragmentation functions into TMD fragmentation functions. We then perform the resummation of the Sudakov logarithms through Collins-Soper-Sterman evolution of the TMD fragmentation functions and the Weizsäcker-Williams TMD gluon distribution. Finally, analytic expressions are obtained for the NLO coefficient functions in the $\overline{MS}$-scheme. These results pave the way towards numerically calculating dihadron correlations at small x at the future Electron-Ion Collider with full NLO accuracy.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
Analytical model for fluid flow distribution in an Enhanced Geothermal Systems (EGS)
Not Available
Asai, Pranay (ORCID:0000000234106554)↗
High-resolution particle-in-cell simulations of two-dimensional Bernstein–Greene–Kruskal modes
We present two-dimensional (2D) particle-in-cell (PIC) simulations of 2D Bernstein–Greene–Kruskal modes, which are exact nonlinear steady-state solutions of the Vlasov–Poisson equations, on a 2D plane perpendicular to a background magnetic field, with a cylindrically symmetric electric potential localized on the plane. PIC simulations are initialized using analytic electron distributions and electric potentials from the theory. We confirm the validity of such solutions using high-resolutions up to a 20482 grid. We show that the solutions are dynamically stable for a stronger background magnetic field, while keeping other parameters of the model fixed, but become unstable when the field strength is weaker than a certain value. When a mode becomes unstable, we observe that the instability begins with the excitation of azimuthal electrostatic waves that ends with a spiral pattern.
Physics↗
The importance of accounting for the Tolman correction to surface tension for nucleation and growth modeling of Fe clusters
Gibbs free energies of clusters are required for predictive modeling of cluster growth during condensation of a cooling vapor. Here, we present a straightforward method of calculating free energies of cluster formation using the data from molecular dynamics (MD) simulations. We apply this method to iron clusters having from 2 to 100 atoms. The energies obtained are verified by comparing to an MD-simulated equilibrium cluster size distribution in a sub-saturated vapor. We show that these free energies differ significantly from those obtained with a commonly used spherical cluster approximation, which relies on a surface tension coefficient of a flat surface, as it is used in the classical nucleation theory (CNT). We show that the spherical cluster approximation in CNT can be improved by using a cluster-size-dependent Tolman correction for the surface tension. The Tolman length and effective surface tension values were derived for iron clusters, and they significantly differ from the commonly used experimentally measured values. This improved approximation does not account for geometric magic number effects responsible for spikes and troughs in densities of neighbor cluster sizes. Nonetheless, it allows to more accurately model cluster formation from a cooling vapor. It better reproduces the condensation timeline, overall shape of the cluster size distribution, average cluster size, and the distribution width. In contrast, using a constant surface tension coefficient (as done in CNT) resulted in incorrect condensation dynamics and cluster size distributions. The analytical expression for cluster nucleation rate from CNT was updated to account for the size-dependence of cluster surface tension.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Flow annealed importance sampling bootstrap meets differentiable particle physics
High-energy physics requires the generation of large numbers of simulated data samples from complex but analytically tractable distributions called matrix elements. Surrogate models, such as normalizing flows, are gaining popularity for this task due to their computational efficiency. We adopt an approach based on flow annealed importance sampling bootstrap (FAB) that evaluates the differentiable target density during training and helps avoid the costly generation of training data in advance. We show that FAB reaches higher sampling efficiency with fewer target evaluations in high dimensions in comparison to other methods.
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗