pwc-archive/papers-with-abstracts huggingface dataset:
Online Expectation-Maximization Based Frequency and Phase Consensus in Distributed Phased Arrays
Authors: Mohammed Rashid, Jeffrey A. Nanzer
Date: 2022-07-25 00:00:00

Distributed phased arrays are comprised of separate, smaller antenna systems that coordinate with each other to support coherent beamforming towards a destination. However, due to the frequency drift and phase jitter of the oscillators, as well as the frequency and phase estimation errors induced at the nodes, there exists decoherence that degrades the beamforming process. A decentralized frequency and phase consensus (DFPC) algorithm was proposed in prior work for undirected networks in which the nodes locally share their frequencies and phases with their neighbors to reach synchronization. Kalman filtering (KF) was also integrated with DFPC (KF-DFPC) to lower the total residual phase error upon convergence. Since these DFPC-based algorithms rely on the average consensus protocol, they do not converge for directed networks. In this paper, we propose a push-sum based frequency and phase consensus (PsFPC) algorithm for the directed networks. The residual phase error of PsFPC is theoretically derived as well. Kalman filtering is also integrated with PsFPC and the resulting KF-PsFPC algorithm shows a significant reduction in the residual phase error upon convergence. KF assumes that the model parameters, i.e., the measurement noise and innovation noise covariance matrices, are known. Since they may not be known in practice, we develop an online expectation maximization (EM) based algorithm that iteratively computes the maximum likelihood (ML) estimate of the unknown matrices in an online manner. EM is integrated with KF-PsFPC to propose the EM-KF-PsFPC algorithm. Simulation results are included where the performance of the PsFPC-based algorithms is analyzed for different distributed phased arrays and is compared to other algorithms.
Consensus-based Frequency and Voltage Regulation for Fully Inverter-based Islanded Microgrids
Authors: Y. Cheng, Tao Liu, David J. Hill, Xue Lyu
Date: 2022-07-24 00:00:00

This paper proposes a new distributed consensus-based control method for voltage and frequency control of fully inverter-based islanded microgrids (MGs). The proposed method includes the active power sharing in voltage control to improve the reactive power sharing accuracy and thus generalizes some existing secondary frequency and voltage control methods. Firstly, frequency is regulated by distributed secondary frequency control. Secondly, voltage is regulated by distributed average voltage control and decentralized individual voltage control. It offers a tunable trade-off between voltage regulation, active, and reactive power sharing accuracy. Therefore, it avoids the abuse of sacrificing reactive power sharing accuracy for exact voltage regulation which is a common issue of existing methods. The proposed method is implemented in a distributed way that does not require a prior knowledge of the MG network structure and loads and hence can ensure scalability. Simulation results shows that the proposed controller achieves different compromise between the above three targets under different modes of operation.
A Novel Decentralized Inverter Control Algorithm for Loss Minimization and LVRT Improvement
Authors: Ilgiz Murzakhanov, Gururaj Mirle Vishwanath, Vemalaiah Kasi, Garima Prashal, Spyros Chatzivasileiadis, Narayana Prasad Padhy
Date: 2022-07-24 00:00:00

Algorithms that adjust the reactive power injection of converter-connected RES to minimize losses may compromise the converters' fault-ride-through capability. This can become crucial for the reliable operation of the distribution grids, as they could lose valuable resources to support grid voltage at the time they need them the most. This paper explores how two novel loss-minimizing algorithms can both achieve high reduction of the system losses during normal operation and remain connected to support the voltage during faults. The algorithms we propose are decentralized and model-free: they require no communication and no knowledge of the grid topology or the grid location of the converters. Using local information, they control the reactive power injection to minimize the system losses. In this paper, we extend these algorithms to ensure the low voltage ride through (LVRT) capability of the converters, and we integrate them with state-of-the-art Wavelet-CNN-LSTM RES forecasting methods that enhance their performance. We perform extensive simulations on the real-time digital simulation (RTDS) platform, where we demonstrate that the algorithms we propose can achieve a substantial decrease in power losses while remaining compliant with the grid codes for LVRT makes them suitable for the implementation across the distribution system.
Harmonic-Balance Based Power Flow and ZVS Analysis of a Quad-Active Bridge DC-DC Converter
Authors: Ezekiel Olayiwola Arogunjo, Olivia Nnadi, Joseph Olorunfemi Ojo
Date: 2022-07-24 00:00:00

The power flow control of multi-active bridge converters requires a comprehensive steady-state analysis of the converter and the determination of conditions for zero voltage switching of all switching in the converter which result in minimum switching loss. This paper aims to model and carry out the power flow and Zero Voltage Switching (ZVS) analyses of Quad-active-bridge (QAB) dc-dc converter. The dynamic as well as the steady state analyses of the converter were carried out, thereby determining the phase shifts required to meet commanded load powers. The full equivalent circuit model of the converter which include winding resistances and magnetizing inductances is used rather than the popular lossless star-equivalent circuit model that may introduce significant error in the converter's analysis. The conditions which ensure the converter working in ZVS mode are determined and experimentally verified.
An Insight into the Dynamics of a Dual Active Bridge
Authors: Ezekiel Olayiwola Arogunjo, Joseph Olorunfemi Ojo
Date: 2022-07-24 00:00:00

This paper aims at analyzing the effect of the zero dynamics of the Dual Active Bridge Isolated Bidirectional dc-dc converter (DAB) on the dynamics of the complete DAB system. It also explains its influence on controller design for the DAB system. In carrying out these analyses, the state space model of the DAB, as well as the first harmonic approximation (FHA) of the model are derived. The ZVS and the stability analysis of the system are undertaken based on the FHA model of the system. The system is shown to be stable for a constant output voltage operation for the entire power range while it is unstable for the constant power load (CPL) operation for load demands close to the system maximum power. It is also shown that the transformer winding currents are part of the zero dynamic states and are always stable regardless of the operating conditions of the system.
Resource-Efficient HAPS-RIS Enabled Beyond-Cell Communications
Authors: Safwan Alfattani, Animesh Yadav, Halim Yanikomeroglu, Abbas Yongacoglu
Date: 2022-07-23 00:00:00

In the future, urban regions will encounter a massive number of capacity-hungry devices. Relying solely on terrestrial networks for serving all UEs will be a cost-ineffective approach. Consequently, with the anticipated supply and demand mismatch, several UEs will be unsupported. To offer service to the left-out UEs, we employ an energy-efficient and cost-effective beyond-cell communications approach, which uses reconfigurable intelligent surfaces (RIS) on a high-altitude platform station (HAPS). Particularly, unsupported UEs will be connected to a dedicated control station (CS) through RIS-mounted HAPS. A novel resource-efficient optimization problem is formulated that maximizes the number of connected UEs, while minimizing the total power consumed by the CS and RIS. Since the resulting problem is a mixed-integer nonlinear program (MINLP), a low-complexity two-stage algorithm is developed. Numerical results demonstrate that the proposed algorithm outperforms the benchmark approach in terms of the percentage of connected UEs and the resource-efficiency (RE). Also, the results show that the number of connected UEs is more sensitive to transmit power at the CS than the HAPS size.
Modeling and Analysis of a Coupled SIS Bi-Virus Model
Authors: Sebin Gracy, Philip E. Paré, Ji Liu, Henrik Sandberg, Carolyn L. Beck, Karl Henrik Johansson, Tamer Başar
Date: 2022-07-23 00:00:00

The paper deals with the setting where two viruses (say virus 1 and virus 2) coexist in a population, and they are not necessarily mutually exclusive, in the sense that infection due to one virus does not preclude the possibility of simultaneous infection due to the other. We develop a coupled bi-virus susceptible-infected-susceptible (SIS) model from a 4n-state Markov chain model, where n is the number of agents (i.e., individuals or subpopulation) in the population. We identify a sufficient condition for both viruses to eventually die out, and a sufficient condition for the existence, uniqueness and asymptotic stability of the endemic equilibrium of each virus. We establish a sufficient condition and multiple necessary conditions for local exponential convergence to the boundary equilibrium (i.e., one virus persists, the other one dies out) of each virus. Under mild assumptions on the healing rate, we show that there cannot exist a coexisting equilibrium where for each node there is a nonzero fraction infected only by virus 1; a nonzero fraction infected only by virus 2; but no fraction that is infected by both viruses 1 and 2. Likewise, assuming that healing rates are strictly positive, a coexisting equilibrium where for each node there is a nonzero fraction infected by both viruses 1 and 2, but no fraction is infected only by virus 1 (resp. virus 2) does not exist. Further, we provide a necessary condition for the existence of certain other kinds of coexisting equilibria. We show that, unlike the competitive bivirus model, the coupled bivirus model is not monotone. Finally, we illustrate our theoretical findings using an extensive set of in-depth simulations.
A Deployable Online Optimization Framework for EV Smart Charging with Real-World Test Cases
Authors: Nathaniel Tucker, Mahnoosh Alizadeh
Date: 2022-07-23 00:00:00

We present a customizable online optimization framework for real-time EV smart charging to be readily implemented at real large-scale charging facilities. Notably, due to real-world constraints, we designed our framework around 3 main requirements. First, the smart charging strategy is readily deployable and customizable for a wide-array of facilities, infrastructure, objectives, and constraints. Second, the online optimization framework can be easily modified to operate with or without user input for energy request amounts and/or departure time estimates which allows our framework to be implemented on standard chargers with 1-way communication or newer chargers with 2-way communication. Third, our online optimization framework outperforms other real-time strategies (including first-come-first-serve, least-laxity-first, earliest-deadline-first, etc.) in multiple real-world test cases with various objectives. We showcase our framework with two real-world test cases with charging session data sourced from SLAC and Google campuses in the Bay Area.
On Statistical Modeling of Load in Systems with High Capacity Distributed Energy Resources
Authors: Aaqib Peerzada, Miroslav Begovic, Wesam Rohouma, Robert S. Balog
Date: 2022-07-22 00:00:00

The emergence of distributed energy resources has led to new challenges in the operation and planning of power networks. Of particular significance is the introduction of a new layer of complexity that manifests in the form of new uncertainties that could severely limit the resiliency and reliability of a modern power system. For example, the increasing adoption of unconventional loads such as plug-in electric vehicles can result in uncertain consumer demand patterns, which are often characterized by random undesirable peaks in energy consumption. In the first half of 2021, the electric vehicle sales increased by nearly 160%, thus accounting for roughly 26% of new sales in the global automotive market. This paper investigates the applicability of generalized mixture models for the statistical representation of aggregated load in systems enhanced with high capacity distributed energy resources such as plug-in electric vehicles.
Stationary Cost Nodes in Infinite Horizon LQG-GMFGs
Authors: Rinel Foguen Tchuendom, Shuang Gao, Peter E. Caines
Date: 2022-07-22 00:00:00

An analysis of infinite horizon linear quadratic Gaussian (LQG) Mean Field Games is given within the general framework of Graphon Mean Field Games (GMFG) on dense infinite graphs (or networks) introduced in Caines and Huang (2018). For a class of LQG-GMFGs, analytical expressions are derived for the infinite horizon Nash values at the nodes of the infinite graph. Furthermore, under specific conditions on the network and the initial population means, it is shown that the nodes with strict local maximal infinite network degree are also nodes with strict local minimal cost at equilibrium.
Impacts of Dynamic Line Ratings on the ERCOT Transmission System
Authors: Thomas Lee, Vineet Jagadeesan Nair, Andy Sun
Date: 2022-07-22 00:00:00

Grid regulators and participants are paying increasing attention to Dynamic Line Ratings (DLR) as a new approach to address transmission system bottlenecks. In this paper, a thorough comparison of DLR, Ambient Adjusted Ratings (AAR), and the traditional Static Line Ratings (SLR) are conducted on a synthetic ERCOT grid. Estimates of DLR and AAR are calculated using an equation based on heat balance physics, along with high-resolution weather data of temperature and wind velocities. A constraint generation method for contingency screening is developed for solving security-constrained optimal power flow. Numerical results suggest that employing DLR could double the benefits compared to those of AAR relative to SLR, in terms of system costs, renewable curtailment, and emissions.
Machine-learning accelerated identification of exfoliable two-dimensional materials
Authors: Mohammad Tohidi Vahdat, Kumar Agrawal Varoon, Giovanni Pizzi
Date: 2022-07-18 00:00:00
Tasks: BIG-bench Machine Learning, Vocal Bursts Valence Prediction

Two-dimensional (2D) materials have been a central focus of recent research because they host a variety of properties, making them attractive both for fundamental science and for applications. It is thus crucial to be able to identify accurately and efficiently if bulk three-dimensional (3D) materials are formed by layers held together by a weak binding energy that, thus, can be potentially exfoliated into 2D materials. In this work, we develop a machine-learning (ML) approach that, combined with a fast preliminary geometrical screening, is able to efficiently identify potentially exfoliable materials. Starting from a combination of descriptors for crystal structures, we work out a subset of them that are crucial for accurate predictions. Our final ML model, based on a random forest classifier, has a very high recall of 98\%. Using a SHapely Additive exPlanations (SHAP) analysis, we also provide an intuitive explanation of the five most important variables of the model. Finally, we compare the performance of our best ML model with a deep neural network architecture using the same descriptors. To make our algorithms and models easily accessible, we publish an online tool on the Materials Cloud portal that only requires a bulk 3D crystal structure as input. Our tool thus provides a practical yet straightforward approach to assess whether any 3D compound can be exfoliated into 2D layers.
Calcium oscillation on homogeneous and heterogeneous networks of ryanodine receptor
Authors: Zhong-Xue Gao, Tian-Tian Li, Han-Yu Jiang, Jun He
Date: 2022-07-24 00:00:00
Tasks: Super-Resolution

Calcium oscillation is an important calcium homeostasis, imbalance of which is the key mechanism of initiation and progression of many major diseases. The formation and maintenance of calcium homeostasis are closely related to the spatial distribution of calcium channels. In the current paper, a theoretical framework is established by abstracting the spatial distribution of the calcium channels as a nonlinear biological complex network with calcium channels as nodes and Ca$^{2+}$ as edges. A dynamical model for a RyR is adopted to investigate the effect of spatial distribution on calcium oscillation. The mean-field model can be well reproduced from the complete graph and dense Erd\"os-R\'enyi network. The synchronization of RyRs is found important to generate a global calcium oscillation. The clique graph with a cluster structure can not produce a global oscillation due to the failure of synchronization between clusters. A more realistic geometric network is constructed in a two-dimensional plane based on the experimental information about the RyR arrangement of clusters and the frequency distribution of cluster sizes. Different from the clique graph, the global oscillation can be generated with reasonable parameters on the geometric network. The simulation also suggests that existence of small clusters and rogue RyR's plays an important role in the maintenance of global calcium oscillation through keeping synchronization between large clusters. Such results support the heterogeneous distribution of RyR's with different-size clusters, which is helpful to understand recent observations with super resolution nanoscale imaging techniques. The current theoretical framework can also be extent to investigate other phenomena in calcium signal transduction.
Communities in C.elegans connectome through the prism of non-backtracking walks
Authors: Arsenii Onuchin, Alina Chernizova, Mikhail Lebedev, Kirill Polovnikov
Date: 2022-07-22 00:00:00
Tasks: Community Detection

The fundamental relationship between the mesoscopic structure of neuronal circuits and organismic functions they subserve is one of the major challenges in contemporary neuroscience. Formation of structurally connected modules of neurons enacts the conversion from single-cell firing to large-scale behaviour of an organism, highlighting the importance of their accurate profiling in the data. While connectomes are typically characterized by significant sparsity of neuronal connections, recent advances in network theory and machine learning have revealed fundamental limitations of traditionally used community detection approaches in cases where the network is sparse. Here we studied the optimal community structure in the structural connectome of C.elegans, for which we exploited a non-conventional approach that is based on non-backtracking random walks, virtually eliminating the sparsity issue. In full agreement with the previous asymptotic results, we demonstrated that non-backtracking walks resolve the ground truth annotation into clusters on stochastic block models (SBM) with the size and density of the connectome better than the spectral methods related to simple random walks. Based on the cluster detectability threshold, we determined that the optimal number of modules in a recently mapped connectome of C.elegans is 10, which precisely corresponds to the number of isolated eigenvalues in the spectrum of the non-backtracking flow matrix. Broadly, our work provides a robust network-based framework to reveal mesoscopic structures in sparse connectomic datasets, paving way to further investigation of connectome mechanisms for different functions.
Wavelength-Resolution SAR Ground Scene Prediction Based on Image Stack
Authors: B. G. Palm, D. I. Alves, M. I. Pettersson, V. T. Vu, R. Machado, R. J. Cintra, F. M. Bayer, P. Dammert, H. Hellsten
Date: 2022-07-23 00:00:00
Tasks: Change Detection

This paper presents five different statistical methods for ground scene prediction (GSP) in wavelength-resolution synthetic aperture radar (SAR) images. The GSP image can be used as a reference image in a change detection algorithm yielding a high probability of detection and low false alarm rate. The predictions are based on image stacks, which are composed of images from the same scene acquired at different instants with the same flight geometry. The considered methods for obtaining the ground scene prediction include (i) autoregressive models; (ii) trimmed mean; (iii) median; (iv) intensity mean; and (v) mean. It is expected that the predicted image presents the true ground scene without change and preserves the ground backscattering pattern. The study indicate that the the median method provided the most accurate representation of the true ground. To show the applicability of the GSP, a change detection algorithm was considered using the median ground scene as a reference image. As a result, the median method displayed the probability of detection of $97\%$ and a false alarm rate of 0.11/km$^2, when considering military vehicles concealed in a forest.
Decentralized digital twins of complex dynamical systems
Authors: Omer San, Suraj Pawar, Adil Rasheed
Date: 2022-07-07 00:00:00
Tasks: BIG-bench Machine Learning, Federated Learning

In this paper, we introduce a decentralized digital twin (DDT) framework for dynamical systems and discuss the prospects of the DDT modeling paradigm in computational science and engineering applications. The DDT approach is built on a federated learning concept, a branch of machine learning that encourages knowledge sharing without sharing the actual data. This approach enables clients to collaboratively learn an aggregated model while keeping all the training data on each client. We demonstrate the feasibility of the DDT framework with various dynamical systems, which are often considered prototypes for modeling complex transport phenomena in spatiotemporally extended systems. Our results indicate that federated machine learning might be a key enabler for designing highly accurate decentralized digital twins in complex nonlinear spatiotemporal systems.
Forecasting euro area inflation using a huge panel of survey expectations
Authors: Florian Huber, Luca Onorante, Michael Pfarrhofer
Date: 2022-07-25 00:00:00
Tasks: Survey, Time Series, Time Series Analysis

In this paper, we forecast euro area inflation and its main components using an econometric model which exploits a massive number of time series on survey expectations for the European Commission's Business and Consumer Survey. To make estimation of such a huge model tractable, we use recent advances in computational statistics to carry out posterior simulation and inference. Our findings suggest that the inclusion of a wide range of firms and consumers' opinions about future economic developments offers useful information to forecast prices and assess tail risks to inflation. These predictive improvements do not only arise from surveys related to expected inflation but also from other questions related to the general economic environment. Finally, we find that firms' expectations about the future seem to have more predictive content than consumer expectations.
Sparse Bayesian State-Space and Time-Varying Parameter Models
Authors: Sylvia Frühwirth-Schnatter, Peter Knaus
Date: 2022-07-25 00:00:00
Tasks: regression, Time Series, Time Series Analysis, Variable Selection

In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both continuous as well as discrete spike-and-slab shrinkage priors can be transferred from variable selection for regression models to variance selection for TVP models by using a non-centered parametrization. We discuss efficient MCMC estimation and provide an application to US inflation modeling.