pwc-archive/papers-with-abstracts huggingface dataset:
Low-frequency Resonances in Grid-Forming Converters: Causes and Damping Control
Authors: Fangzhou Zhao, Tianhua Zhu, Zejie Li, Xiongfei Wang
Date: 2024-02-22 00:00:00

Grid-forming voltage-source converter (GFM-VSC) may experience low-frequency resonances, such as synchronous resonance (SR) and sub-synchronous resonance (SSR), in the output power. This paper offers a comprehensive study on the root causes of low-frequency resonances with GFM-VSC systems and the damping control methods. The typical GFM control structures are introduced first, along with a mapping between the resonances and control loops. Then, the causes of SR and SSR are discussed, highlighting the impacts of control interactions on the resonances. Further, the recent advancements in stabilizing control methods for SR and SSR are critically reviewed with experimental tests of a GFM-VSC under different grid conditions.
Strong-ARM Dynamic Latch Comparators: Design and Analyses on CAD Platform
Authors: Kasi Bandla, Dipankar Pal
Date: 2024-02-22 00:00:00

Strong-ARM Dynamic Latch Comparators are widely used in high-speed analog-to-digital converters (ADCs), sense amplifiers in memory, RFID applications, and data receivers. This paper presents different methods to improve the performance of Strong-Arm latch-based comparators. The comparator's significant features such as power dissipation, propagation delay, offset voltage, clock feedthrough, area, and kickback noises are discussed and compared with state-of-the-art candidate topologies. Simulation results show that the new comparator topologies of Strong-ARM Dynamic Latch proposed by these authors gave the best results. The proposed designs are tested. The simulations are carried out using UMC 180nm double metal, double poly standard CMOS process technology, for a 100 MHz clock, at 1.8V supply-rail on the Cadence Virtuoso EDA platform.
MR-ARL: Model Reference Adaptive Reinforcement Learning for Robustly Stable On-Policy Data-Driven LQR
Authors: Marco Borghesi, Alessandro Bosso, Giuseppe Notarstefano
Date: 2024-02-22 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This article introduces a novel framework for data-driven linear quadratic regulator (LQR) design. First, we introduce a reinforcement learning paradigm for on-policy data-driven LQR, where exploration and exploitation are simultaneously performed while guaranteeing robust stability of the whole closed-loop system encompassing the plant and the control/learning dynamics. Then, we propose Model Reference Adaptive Reinforcement Learning (MR-ARL), a control architecture integrating tools from reinforcement learning and model reference adaptive control. The approach stands on a variable reference model containing the currently identified value function. Then, an adaptive stabilizer is used to ensure convergence of the applied policy to the optimal one, convergence of the plant to the optimal reference model, and overall robust closed-loop stability. The proposed framework provides theoretical robustness certificates against real-world perturbations such as measurement noise, plant nonlinearities, or slowly varying parameters. The effectiveness of the proposed architecture is validated via realistic numerical simulations.
On decentralized computation of the leader's strategy in bi-level games
Authors: Marko Maljkovic, Gustav Nilsson, Nikolas Geroliminis
Date: 2024-02-22 00:00:00

Motivated by the omnipresence of hierarchical structures in many real-world applications, this study delves into the intricate realm of bi-level games, with a specific focus on exploring local Stackelberg equilibria as a solution concept. While existing literature offers various methods tailored to specific game structures featuring one leader and multiple followers, a comprehensive framework providing formal convergence guarantees to a local Stackelberg equilibrium appears to be lacking. Drawing inspiration from sensitivity results for nonlinear programs and guided by the imperative to maintain scalability and preserve agent privacy, we propose a decentralized approach based on the projected gradient descent with the Armijo stepsize rule. The main challenge here lies in assuring the existence and well-posedness of Jacobians that describe the leader's decision's influence on the achieved equilibrium of the followers. By meticulous tracking of the Implicit Function Theorem requirements at each iteration, we establish formal convergence guarantees to a local Stackelberg equilibrium for a broad class of bi-level games. Building on our prior work on quadratic aggregative Stackelberg games, we also introduce a decentralized warm-start procedure based on the consensus alternating direction method of multipliers addressing the previously reported initialization issues. Finally, we provide empirical validation through two case studies in smart mobility, showcasing the effectiveness of our general method in handling general convex constraints, and the effectiveness of its extension in tackling initialization issues.
Closed-loop Data-Enabled Predictive Control and its equivalence with Closed-loop Subspace Predictive Control
Authors: Rogier Dinkla, Sebastiaan Mulders, Tom Oomen, Jan-Willem van Wingerden
Date: 2024-02-22 00:00:00

Factors like improved data availability and increasing system complexity have sparked interest in data-driven predictive control (DDPC) methods like Data-enabled Predictive Control (DeePC). However, closed-loop identification bias arises in the presence of noise, which reduces the effectiveness of obtained control policies. In this paper we propose Closed-loop Data-enabled Predictive Control (CL-DeePC), a framework that unifies different approaches to address this challenge. To this end, CL-DeePC incorporates instrumental variables (IVs) to synthesize and sequentially apply consistent single or multi-step-ahead predictors. Furthermore, a computationally efficient CL-DeePC implementation is developed that reveals an equivalence with Closed-loop Subspace Predictive Control (CL-SPC). Compared to DeePC, CL-DeePC simulations demonstrate superior reference tracking, with a sensitivity study finding a 48% lower susceptibility to noise-induced reference tracking performance degradation.
Oscillations between Grid-Forming Converters in Weakly Connected Offshore WPPs
Authors: Sulav Ghimire, Kanakesh V. Kkuni, Gabriel M. G. Guerreiro, Emerson D. Guest, Kim H. Jensen, Guangya Yang
Date: 2024-02-22 00:00:00

This paper studies control interactions between grid-forming (GFM) converters exhibited by power and frequency oscillations in a weakly connected offshore wind power plant (WPP). Two GFM controls are considered, namely virtual synchronous machine (VSM) and virtual admittance (VAdm) based GFM. The GFM control methods are implemented in wind turbine generators (WTGs) of a verified aggregated model of a WPP and the control interaction between these GFM WTGs is studied for several cases: cases with the same GFM control methods, and cases with different GFM control methods. A sensitivity analysis is performed for the observed oscillations to understand which system parameter affects the oscillations the most. Several solution methods are proposed and the inapplicability of some of the conventional solution methods are elaborated in this paper.
Optimal transmission expansion modestly reduces decarbonization costs of U.S. electricity
Authors: Rangrang Zheng, Greg Schivley, Patricia Hidalgo-Gonzalez, Matthias Fripp, Michael J. Roberts
Date: 2024-02-22 00:00:00

Solar and wind power are cost-competitive with fossil fuels, yet their intermittent nature presents challenges. Significant temporal and geographic differences in land, wind, and solar resources suggest that long-distance transmission could be particularly beneficial. Using a detailed, open-source model, we analyze optimal transmission expansion jointly with storage, generation, and hourly operations across the three primary interconnects in the United States. Transmission expansion offers far more benefits in a high-renewable system than in a system with mostly conventional generation. Yet while an optimal nationwide plan would have more than triple current interregional transmission, transmission decreases the cost of a 100% clean system by only 7% compared to a plan that relies solely on current transmission. Expanding capacity only within existing interconnects can achieve most of these savings. Adjustments to energy storage and generation mix can leverage the current interregional transmission infrastructure to build a clean power system at a reasonable cost.
Parking of Connected Automated Vehicles: Vehicle Control, Parking Assignment, and Multi-agent Simulation
Authors: Xu Shen, Yongkeun Choi, Alex Wong, Francesco Borrelli, Scott Moura, Soomin Woo
Date: 2024-02-22 00:00:00
Tasks: Collision Avoidance, Decision Making

This paper introduces a novel approach to optimize the parking efficiency for fleets of Connected and Automated Vehicles (CAVs). We present a novel multi-vehicle parking simulator, equipped with hierarchical path planning and collision avoidance capabilities for individual CAVs. The simulator is designed to capture the key decision-making processes in parking, from low-level vehicle control to high-level parking assignment, and it enables the effective assessment of parking strategies for large fleets of ground vehicles. We formulate and compare different strategic parking spot assignments to minimize a collective cost. While the proposed framework is designed to optimize various objective functions, we choose the total parking time for the experiment, as it is closely related to the reduction of vehicles' energy consumption and greenhouse gas emissions. We validate the effectiveness of the proposed strategies through empirical evaluation against a dataset of real-world parking lot dynamics, realizing a substantial reduction in parking time by up to 43.8%. This improvement is attributed to the synergistic benefits of driving automation, the utilization of shared infrastructure state data, the exclusion of pedestrian traffic, and the real-time computation of optimal parking spot allocation.
Extending identifiability results from isolated networks to embedded networks
Authors: Eduardo Mapurunga, Michel Gevers, Alexandre S. Bazanella
Date: 2024-02-21 00:00:00
Tasks: valid

This paper deals with the design of Excitation and Measurement Patterns (EMPs) for the identification of dynamical networks, when the objective is to identify only a subnetwork embedded in a larger network. Recent results have shown how to construct EMPs that guarantee identifiability for a range of networks with specific graph topologies, such as trees, loops, or Directed Acyclic Graphs (DAGs). However, an EMP that is valid for the identification of a subnetwork taken in isolation may no longer be valid when that subnetwork is embedded in a larger network. Our main contribution is to exhibit conditions under which it does remain valid, and to propose ways to enhance such EMP when these conditions are not satisfied.
Driving Towards Stability and Efficiency: A Variable Time Gap Strategy for Adaptive Cruise Control
Authors: Shaimaa K. El-Baklish, Anastasios Kouvelas, Michail A. Makridis
Date: 2024-02-21 00:00:00

Automated vehicle technologies offer a promising avenue for enhancing traffic efficiency, safety, and energy consumption. Among these, Adaptive Cruise Control (ACC) systems stand out as a prevalent form of automation on today's roads, with their time gap settings holding paramount importance. While decreasing the average time headway tends to enhance traffic capacity, it simultaneously raises concerns regarding safety and string stability. This study introduces a novel variable time gap feedback control policy aimed at striking a balance between maintaining a minimum time gap setting under equilibrium car-following conditions, thereby improving traffic capacity, while ensuring string stability to mitigate disturbances away from the equilibrium flow. Leveraging nonlinear $H_\infty$ control technique, the strategy employs a variable time gap component as the manipulated control signal, complemented by a constant time gap component that predominates during car-following equilibrium. The effectiveness of the proposed scheme is evaluated against its constant time-gap counterpart calibrated using field platoon data from the OpenACC dataset. Through numerical and traffic simulations, our findings illustrate that the proposed algorithm effectively dampens perturbations within vehicle platoons, leading to a more efficient and safer mixed traffic flow.
Autoencoder with Ordered Variance for Nonlinear Model Identification
Authors: Midhun T. Augustine, Parag Patil, Mani Bhushan, Sharad Bhartiya
Date: 2024-02-20 00:00:00
Tasks: model

This paper presents a novel autoencoder with ordered variance (AEO) in which the loss function is modified with a variance regularization term to enforce order in the latent space. Further, the autoencoder is modified using ResNets, which results in a ResNet AEO (RAEO). The paper also illustrates the effectiveness of AEO and RAEO in extracting nonlinear relationships among input variables in an unsupervised setting.
Functional Spatial Autoregressive Models
Authors: Tadao Hoshino
Date: 2024-02-22 00:00:00

This study introduces a novel spatial autoregressive model in which the dependent variable is a function that may exhibit functional autocorrelation with the outcome functions of nearby units. This model can be characterized as a simultaneous integral equation system, which, in general, does not necessarily have a unique solution. For this issue, we provide a simple condition on the magnitude of the spatial interaction to ensure the uniqueness in data realization. For estimation, to account for the endogeneity caused by the spatial interaction, we propose a regularized two-stage least squares estimator based on a basis approximation for the functional parameter. The asymptotic properties of the estimator including the consistency and asymptotic normality are investigated under certain conditions. Additionally, we propose a simple Wald-type test for detecting the presence of spatial effects. As an empirical illustration, we apply the proposed model and method to analyze age distributions in Japanese cities.
Enhancing Rolling Horizon Production Planning Through Stochastic Optimization Evaluated by Means of Simulation
Authors: Manuel Schlenkrich, Wolfgang Seiringer, Klaus Altendorfer, Sophie N. Parragh
Date: 2024-02-22 00:00:00
Tasks: Stochastic Optimization

Production planning must account for uncertainty in a production system, arising from fluctuating demand forecasts. Therefore, this article focuses on the integration of updated customer demand into the rolling horizon planning cycle. We use scenario-based stochastic programming to solve capacitated lot sizing problems under stochastic demand in a rolling horizon environment. This environment is replicated using a discrete event simulation-optimization framework, where the optimization problem is periodically solved, leveraging the latest demand information to continually adjust the production plan. We evaluate the stochastic optimization approach and compare its performance to solving a deterministic lot sizing model, using expected demand figures as input, as well as to standard Material Requirements Planning (MRP). In the simulation study, we analyze three different customer behaviors related to forecasting, along with four levels of shop load, within a multi-item and multi-stage production system. We test a range of significant parameter values for the three planning methods and compute the overall costs to benchmark them. The results show that the production plans obtained by MRP are outperformed by deterministic and stochastic optimization. Particularly, when facing tight resource restrictions and rising uncertainty in customer demand, the use of stochastic optimization becomes preferable compared to deterministic optimization.
The impact of Facebook-Cambridge Analytica data scandal on the USA tech stock market: An event study based on clustering method
Authors: Vahidin Jeleskovic, Yinan Wan
Date: 2024-02-22 00:00:00
Tasks: Clustering

This study delves into the intra-industry effects following a firm-specific scandal, with a particular focus on the Facebook data leakage scandal and its associated events within the U.S. tech industry and two additional relevant groups. We employ various metrics including daily spread, volatility, volume-weighted return, and CAPM-beta for the pre-analysis clustering, and subsequently utilize CAR (Cumulative Abnormal Return) to evaluate the impact on firms grouped within these clusters. From a broader industry viewpoint, significant positive CAARs are observed across U.S. sample firms over the three days post-scandal announcement, indicating no adverse impact on the tech sector overall. Conversely, after Facebook's initial quarterly earnings report, it showed a notable negative effect despite reported positive performance. The clustering principle should aid in identifying directly related companies and thus reducing the influence of randomness. This was indeed achieved for the effect of the key event, namely "The Effect of Congressional Hearing on Certain Clusters across U.S. Tech Stock Market," which was identified as delayed and significantly negative. Therefore, we recommend applying the clustering method when conducting such or similar event studies.
Deep Learning Models for Conditioning Extremely Noisy Signals
Authors: Andrea Faúndez Quezada, Salvatore La Cavera III, Sidahmed A Abayzeed
Date: 2024-02-22 00:00:00
Tasks: Deep Learning

This paper presents a comparison of several Convolutional Neural Network (CNN) models for extracting target signals in highly noisy measurement conditions. Four CNN architectures were investigated. The first comprises six consecutive convolutional blocks while the second employs a U-Net structure. The third architecture introduces a new model inspired by the principles of wavelet transform. It consists of three CNN blocks with varied kernel sizes branching from the input layer before merging into consecutive concatenation and dense layers. The fourth is a Multilevel Wavelet Convolutional Neural Network (MWCNN), resembling U-net but the upsampling and downsampling are replaced by Discrete Wavelet transform and its inverse respectively. To evaluate these architectures, synthetic data were generated using pulse trains corrupted with various degrees of Gaussian noise to simulate measurement conditions with signal-to-noise ratios (SNR) as low as -20 dB. The methodology encompassed the generation and processing of signals with varied parameters: period (5-25 ms), duty cycle (0.1-0.5), and amplitude (1-150 mV). Subsequently, the machine learning models were trained, validated and tested. Finally, the output is processed to recover the signal amplitude before standardisation. The modified MWCNN model demonstrated superior performance achieving a median of 25.9 dB with a mean Root Mean Square Error (RMSE) that decreases with the amplitude of the signal reaching an average RMSE of 0.000128 mV for 1 mV signals. However, for this architecture, the output SNR drops by a factor of 1.23 when the input SNR decreases by 1 dB. All of the architectures exhibited consistency when testing output SNR for different signal amplitudes, periods and duty cycles. These findings indicate that CNN architectures can be used to denoise signals with SNR as low as -20 dB[...]
Fast and Efficient Sequential Radar Parameter Estimation in MIMO-OTFS Systems
Authors: Kuranage Roche Rayan Ranasinghe, Hyeon Seok Rou, Giuseppe Thadeu Freitas de Abreu
Date: 2024-02-22 00:00:00
Tasks: parameter estimation

We consider the estimation of three-dimensional (3D) radar parameters, namely, bearing or angle-of-arrival (AoA), delay or range, and Doppler shift velocity, under a mono-static multiple-input multiple-output (MIMO) joint communications and radar (JCR) system based on Orthogonal Time Frequency Space (OTFS) signals. In particular, we propose a novel two-step algorithm to estimate the three radar parameters sequentially, where the AoA is obtained first, followed by the estimation of range and velocity via a reduced two-dimensional (2D) grid maximum likelihood (ML) search in the delay-Doppler (DD) domain. Besides the resulting lower complexity, the decoupling of AoA and DD estimation enables the incorporation of an linear minimum mean square error (LMMSE) procedure in the ML estimation of range and velocity, which are found to significantly outperform State-of-the-Art (SotA) alternatives and approach the fundamental limits of the Cram`er-Rao lower bound (CRLB) and search grid resolution.
Cellular Load Dependent Sleep Control for Energy Efficient HetNets with Non-Uniform User Distributions
Authors: Martin Willame, Charles Wiame, Jérôme Louveaux, Claude Oestges, Luc Vandendorpe
Date: 2024-02-22 00:00:00

This study proposes a novel stochastic geometry framework analyzing power control strategies in spatially correlated network topologies. Heterogeneous networks are studied, with users modeled via the superposition of homogeneous and Poisson cluster processes. First, a new expression approaching the distribution of the number of users per base station is provided. This distribution defines the load associated with each Vorono\"i cell, capturing non-uniformities in user locations and correlation to BSs positions. The power allocation is adjusted based on this load, allowing BSs to enter sleep mode when their activity falls below a defined threshold. Furthermore, the propagation model features millimeter wave transmission characteristics and directional beamforming. Considering these aspects, revisited definitions of coverage probability, spectral efficiency, and energy efficiency are proposed. Tractable expressions for these metrics are derived and validated using Monte-Carlo simulations. Asymptotic expressions are also proposed, providing further understanding on the influence of the system parameters. Our numerical results finally analyze the impact of the sleep control on the performance and display the optimal strategies in terms of energy efficiency.
Design and Validation of a Very Low-Power Phasor Measurement Unit
Authors: Zachary J. Lythgoe, Thomas F. Long, Michael J. Buchholz, Anthony R. Livernois, Kebba Kanuteh, David R. Allee, Anamitra Pal, Ian R. Graham, Zachary D. Drummond
Date: 2024-02-22 00:00:00

Phasor measurement units (PMUs) provide a high-resolution view of the power system at the locations where they are placed. As such, it is desirable to place them in bulk in low voltage distribution circuits. However, the power consumption of a PMU/micro-PMU is in the order of Watts (W) that results in them requiring an external power supply, which in turn increases the overall cost. This work details the hardware design of a PMU capable of measuring and reporting voltage and current phasors for a single-phase system at an average power consumption of only 30.8 mW -- one to two orders of magnitude lower than existing academic and commercial PMUs. This enables the proposed PMU to run for two weeks using an 11-Wh battery or indefinitely if paired with an inexpensive solar panel. A test-bench developed in accordance with the 2018 IEC/IEEE 60255-118-1 PMU Standard confirms the accuracy of this PMU. Given its low power consumption, the proposed design is expected to accelerate adoption of PMUs in modern distribution grids.