pwc-archive/papers-with-abstracts huggingface dataset:
Quantifying Phase Unbalance and Coordination Impacts on Distribution Network Flexibility
Authors: Andrey Churkin, Wangwei Kong, Pierluigi Mancarella, Eduardo A. Martínez Ceseña
Date: 2024-08-12 00:00:00

The increasing integration of distributed energy resources (DER) provides distribution system operators (DSO) with new flexible resources to support more efficient operation and planning of distribution networks. To utilise these resources, various DER flexibility aggregation methods have been proposed in the literature, such as aggregated P-Q flexibility areas at the interface with other networks. However, whereas focusing on estimating the limits of flexibility services, existing studies make the critical assumption that all available flexible units are perfectly coordinated to jointly provide flexibility and manage network constraints. Moreover, due to the extensive use of single-phase power flow analysis, the impacts of phase unbalance on DER flexibility aggregation remain largely unexplored. To address these gaps in knowledge, this work proposes a framework for modelling flexibility services in low voltage (LV) distribution networks which enables explicitly imposing voltage unbalance and phase coordination constraints. The simulations, performed for an illustrative 5-bus system and a real 221-bus LV network in the UK, demonstrate that a significant share (over 30%) of total aggregated DER flexibility potential may be unavailable due to voltage unbalances and lack of coordination between DER connected to different phases.
Dynamic Equivalent Identification of Interconnected Areas Using Disturbance records from Synchro phasor Measurement Units
Authors: W. Brito, M. Chamba, W. Vargas, D. Echeverria
Date: 2024-08-12 00:00:00

In this paper, a methodology for modelling a dynamic equivalent of an external area is presented. The equivalent consists of a generator with series impedance and a parallel load (generalized Ward equivalent), integrating control systems such as the Automatic Voltage Regulator (AVR) and the Speed Regulator (GOV) in a test system known as PST-16. The main objective is to identify the parameters of the control systems and other parameters inherent to the generator so that the response of the equivalent system is similar to the response of the complete system.
Passivity-Based Gain-Scheduled Control with Scheduling Matrices
Authors: Sepehr Moalemi, James Richard Forbes
Date: 2024-08-12 00:00:00
Tasks: Scheduling

This paper considers gain-scheduling of very strictly passive (VSP) subcontrollers using scheduling matrices. The use of scheduling matrices, over scalar scheduling signals, realizes greater design freedom, which in turn can improve closed-loop performance. The form and properties of the scheduling matrices such that the overall gain-scheduled controller is VSP are explicitly discussed. The proposed gain-scheduled VSP controller is used to control a rigid two-link robot subject to model uncertainty where robust input-output stability is assured via the passivity theorem. Numerical simulation results highlight the greater design freedom, resulting in improved performance, when scheduling matrices are used over scalar scheduled signals.
Wireless Channel Aware Data Augmentation Methods for Deep Learning-Based Indoor Localization
Authors: Omer Gokalp Serbetci, Daoud Burghal, Andreas F. Molisch
Date: 2024-08-12 00:00:00
Tasks: Data Augmentation, Indoor Localization, Outdoor Localization, Transfer Learning

Indoor localization is a challenging problem that - unlike outdoor localization - lacks a universal and robust solution. Machine Learning (ML), particularly Deep Learning (DL), methods have been investigated as a promising approach. Although such methods bring remarkable localization accuracy, they heavily depend on the training data collected from the environment. The data collection is usually a laborious and time-consuming task, but Data Augmentation (DA) can be used to alleviate this issue. In this paper, different from previously used DA, we propose methods that utilize the domain knowledge about wireless propagation channels and devices. The methods exploit the typical hardware component drift in the transceivers and/or the statistical behavior of the channel, in combination with the measured Power Delay Profile (PDP). We comprehensively evaluate the proposed methods to demonstrate their effectiveness. This investigation mainly focuses on the impact of factors such as the number of measurements, augmentation proportion, and the environment of interest impact the effectiveness of the different DA methods. We show that in the low-data regime (few actual measurements available), localization accuracy increases up to 50%, matching non-augmented results in the high-data regime. In addition, the proposed methods may outperform the measurement-only high-data performance by up to 33% using only 1/4 of the amount of measured data. We also exhibit the effect of different training data distribution and quality on the effectiveness of DA. Finally, we demonstrate the power of the proposed methods when employed along with Transfer Learning (TL) to address the data scarcity in target and/or source environments.
Data-Efficient Prediction of Minimum Operating Voltage via Inter- and Intra-Wafer Variation Alignment
Authors: Yuxuan Yin, Rebecca Chen, Chen He, Peng Li
Date: 2024-08-12 00:00:00

Predicting the minimum operating voltage ($V_{min}$) of chips stands as a crucial technique in enhancing the speed and reliability of manufacturing testing flow. However, existing $V_{min}$ prediction methods often overlook various sources of variations in both training and deployment phases. Notably, the neglect of wafer zone-to-zone (intra-wafer) variations and wafer-to-wafer (inter-wafer) variations, compounded by process variations, diminishes the accuracy, data efficiency, and reliability of $V_{min}$ predictors. To address this gap, we introduce a novel data-efficient $V_{min}$ prediction flow, termed restricted bias alignment (RBA), which incorporates a novel variation alignment technique. Our approach concurrently estimates inter- and intra-wafer variations. Furthermore, we propose utilizing class probe data to model inter-wafer variations for the first time. We empirically demonstrate RBA's effectiveness and data efficiency on an industrial 16nm automotive chip dataset.
Event-triggered moving horizon estimation for nonlinear systems
Authors: Isabelle Krauss, Julian D. Schiller, Victor G. Lopez, Matthias A. Müller
Date: 2024-08-12 00:00:00

This work proposes an event-triggered moving horizon estimation (ET-MHE) scheme for general nonlinear systems. The key components of the proposed scheme are a novel event-triggering mechanism (ETM) and the suitable design of the MHE cost function. The main characteristic of our method is that the MHE's nonlinear optimization problem is only solved when the ETM triggers the transmission of measured data to the remote state estimator. If no event occurs, then the current state estimate results from an open-loop prediction using the system dynamics. Furthermore, we show robust global exponential stability of the ET-MHE under a suitable detectability condition. Finally, we illustrate the applicability of the proposed method in terms of a nonlinear benchmark example, where we achieved similar estimation performance compared to standard MHE using 86% less computational resources.
Modelling of measuring systems -- From white box models to cognitive approaches
Authors: Nadine Schiering, Sascha Eichstaedt, Michael Heizmann, Wolfgang Koch, Linda-Sophie Schneider, Stephan Scheele, Klaus-Dieter Sommer
Date: 2024-08-12 00:00:00

Mathematical models of measuring systems and processes play an essential role in metrology and practical measurements. They form the basis for understanding and evaluating measurements, their results and their trustworthiness. Classic analytical parametric modelling is based on largely complete knowledge of measurement technology and the measurement process. But due to digital transformation towards the Internet of Things (IIoT) with an increasing number of intensively and flexibly networked measurement systems and consequently ever larger amounts of data to be processed, data-based modelling approaches have gained enormous importance. This has led to new approaches in measurement technology and industry like Digital Twins, Self-X Approaches, Soft Sensor Technology and Data and Information Fusion. In the future, data-based modelling will be increasingly dominated by intelligent, cognitive systems. Evaluating of the accuracy, trustworthiness and the functional uncertainty of the corresponding models is required. This paper provides a concise overview of modelling in metrology from classical white box models to intelligent, cognitive data-driven solutions identifying advantages and limitations. Additionally, the approaches to merge trustworthiness and metrological uncertainty will be discussed.
DC-DC Converters Optimization in Case of Large Variation in the Load
Authors: Alexander Domyshev, Elena Chistyakova, Aliona Dreglea, Denis Sidorov, Fang Liu
Date: 2024-08-12 00:00:00

The method for controlling a DC-DC converter is proposed to ensures the high quality control at large fluctuations in load currents by using differential gain control coefficients and second derivative control. Various implementations of balancing the currents of a multiphase DC-DC converter are discussed, with a focus on achieving accurate current regulation without introducing additional delay in the control system. Stochastic particle swarm optimization method is used to find optimal values of the PID controller parameters. An automatic constraint-handling in optimization are also discussed as relevant techniques in the field.
Harmonic Stability Analysis of Microgrids with Converter-Interfaced Distributed Energy Resources, Part II: Case Studies
Authors: Johanna Kristin Maria Becker, Andreas Martin Kettner, Mario Paolone
Date: 2024-08-12 00:00:00

In Part I of this paper a method for the Harmonic Stability Assessment (HSA) of power systems with a high share of Converter-Interfaced Distributed Energy Resources (CIDERs) was proposed. Specifically, the Harmonic State-Space (HSS) model of a generic power system is derived through combination of the components HSS models. The HSS models of CIDERs and grid are based on Linear Time-Periodic (LTP) models, capable of representing the coupling between different harmonics. In Part II, the HSA of a grid-forming, and two grid-following CIDERs (i.e., ex- and including the DC-side modelling) is performed. More precisely, the classification of the eigenvalues, the impact of the maximum harmonic order on the locations of the eigenvalues, and the sensitivity curves of the eigenvalues w.r.t. to control parameters are provided. These analyses allow to study the physical meaning and origin of the CIDERs eigenvalues. Additionally, the HSA is performed for a representative example system derived from the CIGRE low-voltage benchmark system. A case of harmonic instability is identified through the system eigenvalues, and validated with Time-Domain Simulations (TDS) in Simulink. It is demonstrated that, as opposed to stability analyses based on Linear Time-Invariant (LTI) models, the HSA is suitable for the detection of harmonic instability.
Harmonic Stability Analysis of Microgrids with Converter-Interfaced Distributed Energy Resources, Part I: Modelling and Theoretical Foundations
Authors: Johanna Kristin Maria Becker, Andreas Martin Kettner, Mario Paolone
Date: 2024-08-12 00:00:00

This paper proposes a method for the Harmonic Stability Assessment (HSA) of power systems with a high share of Converter-Interfaced Distributed Energy Resources (CIDERs). To this end, the Harmonic State-Space (HSS) model of a generic power system is formulated by combining the HSS models of the resources and the grid in closed-loop configuration. The HSS model of the resources is obtained from the Linear Time Periodic (LTP) models of the CIDER components transformed to frequency domain using Fourier theory and Toeplitz matrices. Notably, the HSS of a CIDER is capable of representing the coupling between harmonic frequencies in detail. The HSS model of the grid is derived from the dynamic equations of the individual branch and shunt elements. The system matrix of the HSS models on power-system or resource level is employed for eigenvalue analysis in the context of HSA. A sensitivity analysis of the eigenvalue loci w.r.t. changes in model parameters, and a classification of eigenvalues into control-design variant, control-design invariant, and design invariant eigenvalues is proposed. A case of harmonic instability is identified by the HSA and validated via Time-Domain Simulations (TDS) in Simulink.
Time-limited H2-optimal Model Order Reduction of Linear Systems with Quadratic Outputs
Authors: Umair Zulfiqar, Zhi-Hua Xiao, Qiu-Yan Song, Mohammad Monir Uddin, Victor Sreeram
Date: 2024-08-12 00:00:00

An important class of dynamical systems with several practical applications is linear systems with quadratic outputs. These models have the same state equation as standard linear time-invariant systems but differ in their output equations, which are nonlinear quadratic functions of the system states. When dealing with models of exceptionally high order, the computational demands for simulation and analysis can become overwhelming. In such cases, model order reduction proves to be a useful technique, as it allows for constructing a reduced-order model that accurately represents the essential characteristics of the original high-order system while significantly simplifying its complexity. In time-limited model order reduction, the main goal is to maintain the output response of the original system within a specific time range in the reduced-order model. To assess the error within this time interval, a mathematical expression for the time-limited $\mathcal{H}_2$-norm is derived in this paper. This norm acts as a measure of the accuracy of the reduced-order model within the specified time range. Subsequently, the necessary conditions for achieving a local optimum of the time-limited $\mathcal{H}_2$ norm error are derived. The inherent inability to satisfy these optimality conditions within the Petrov-Galerkin projection framework is also discussed. After that, a stationary point iteration algorithm based on the optimality conditions and Petrov-Galerkin projection is proposed. Upon convergence, this algorithm fulfills three of the four optimality conditions. To demonstrate the effectiveness of the proposed algorithm, a numerical example is provided that showcases its ability to effectively approximate the original high-order model within the desired time interval.
Wireless MAC Protocol Synthesis and Optimization with Multi-Agent Distributed Reinforcement Learning
Authors: Navid Keshtiarast, Oliver Renaldi, Marina Petrova
Date: 2024-08-12 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, rllib

In this letter, we propose a novel Multi-Agent Deep Reinforcement Learning (MADRL) framework for Medium Access Control (MAC) protocol design. Unlike centralized approaches, which rely on a single entity for decision-making, MADRL empowers individual network nodes to autonomously learn and optimize their MAC based on local observations. Leveraging ns3-ai and RLlib, as far as we are aware of, our framework is the first of a kind that enables distributed multi-agent learning within the ns-3 environment, facilitating the design and synthesis of adaptive MAC protocols tailored to specific environmental conditions. We demonstrate the effectiveness of the MADRL MAC framework through extensive simulations, showcasing superior performance compared to legacy protocols across diverse scenarios. Our findings highlight the potential of MADRL-based MAC protocols to significantly enhance Quality of Service (QoS) requirements for future wireless applications.
Identifying Feedforward and Feedback Controllable Subspaces of Neural Population Dynamics
Authors: Ankit Kumar, Loren M. Frank, Kristofer E. Bouchard
Date: 2024-08-11 00:00:00
Tasks: Dimensionality Reduction

There is overwhelming evidence that cognition, perception, and action rely on feedback control. However, if and how neural population dynamics are amenable to different control strategies is poorly understood, in large part because machine learning methods to directly assess controllability in neural population dynamics are lacking. To address this gap, we developed a novel dimensionality reduction method, Feedback Controllability Components Analysis (FCCA), that identifies subspaces of linear dynamical systems that are most feedback controllable based on a new measure of feedback controllability. We further show that PCA identifies subspaces of linear dynamical systems that maximize a measure of feedforward controllability. As such, FCCA and PCA are data-driven methods to identify subspaces of neural population data (approximated as linear dynamical systems) that are most feedback and feedforward controllable respectively, and are thus natural contrasts for hypothesis testing. We developed new theory that proves that non-normality of underlying dynamics determines the divergence between FCCA and PCA solutions, and confirmed this in numerical simulations. Applying FCCA to diverse neural population recordings, we find that feedback controllable dynamics are geometrically distinct from PCA subspaces and are better predictors of animal behavior. Our methods provide a novel approach towards analyzing neural population dynamics from a control theoretic perspective, and indicate that feedback controllable subspaces are important for behavior.
Learning-based Parameterized Barrier Function for Safety-Critical Control of Unknown Systems
Authors: Sihua Zhang, Di-Hua Zhai, Xiaobing Dai, Tzu-Yuan Huang, Yuanqing Xia, Sandra Hirche
Date: 2024-08-09 00:00:00

With the increasing complexity of real-world systems and varying environmental uncertainties, it is difficult to build an accurate dynamic model, which poses challenges especially for safety-critical control. In this paper, a learning-based control policy is proposed to ensure the safety of systems with unknown disturbances through control barrier functions (CBFs). First, the disturbance is predicted by Gaussian process (GP) regression, whose prediction performance is guaranteed by a deterministic error bound. Then, a novel control strategy using GP-based parameterized high-order control barrier functions (GP-P-HOCBFs) is proposed via a shrunk original safe set based on the prediction error bound. In comparison to existing methods that involve adding strict robust safety terms to the HOCBF condition, the proposed method offers more flexibility to deal with the conservatism and the feasibility of solving quadratic problems within the CBF framework. Finally, the effectiveness of the proposed method is demonstrated by simulations on Franka Emika manipulator.
Efficient Unit Commitment Constraint Screening under Uncertainty
Authors: Xuan He, Honglin Wen, Yufan Zhang, Yize Chen, Danny H. K. Tsang
Date: 2024-08-09 00:00:00
Tasks: Load Forecasting

Day-ahead unit commitment (UC) is a fundamental task for power system operators, where generator statuses and power dispatch are determined based on the forecasted nodal net demands. The uncertainty inherent in renewables and load forecasting requires the use of techniques in optimization under uncertainty to find more resilient and reliable UC solutions. However, the solution procedure of such specialized optimization may differ from the deterministic UC. The original constraint screening approach can be unreliable and inefficient for them. Thus, in this work we design a novel screening approach under the forecasting uncertainty. Our approach accommodates such uncertainties in both chance-constrained and robust forms, and can greatly reduce the UC instance size by screening out non-binding constraints. To further improve the screening efficiency, we utilize the multi-parametric programming theory to convert the underlying optimization problem of the screening model to a piecewise affine function. A multi-area screening approach is further developed to handle the computational intractability issues for large-scale problems. We verify the proposed method's performance on a variety of UC setups and uncertainty situations. Experimental results show that our robust screening procedure can guarantee better feasibility, while the CC screening can produce more efficient reduced models. The average screening time for a single line flow constraint can be accelerated by 71.2X to 131.3X using our proposed method.
Modeling Transit in a Fully Integrated Agent-Based Framework: Methodology and Large-Scale Application
Authors: Omer Verbas, Taner Cokyasar, Pedro Veiga de Camargo, Krishna Murthy Gurumurthy, Natalia Zuniga-Garcia, Joshua Auld
Date: 2024-08-09 00:00:00
Tasks: Decision Making

This study presents a transit routing, assignment, and simulation framework which is fully embedded in a multimodal, multi-agent transportation demand and supply modeling platform. POLARIS, a high-performance agent-based simulation platform, efficiently integrates advanced travel and freight demand modeling, dynamic traffic and transit assignment, and multimodal transportation simulation within a unified framework. We focus on POLARIS's transit routing, assignment, and simulation components, detailing its structural design and essential terminologies. We demonstrate how the model integrates upstream decision-making processes - activity generation, location and timing choices, and mode selection, particularly for transit-inclusive trips - followed by routing, assignment decisions, and the movement of travelers and vehicles within a multimodal network. This integration enables modeling of interactions among all agents, including travelers, vehicles, and transportation service providers. The study reviews literature on transportation system modeling tools, describes the transit modeling framework within POLARIS, and presents findings from large-scale analyses of various policy interventions. Results from numerical experiments reveal that measures such as congestion pricing, transit service improvements, first-mile-last-mile subsidies, increased e-commerce deliveries, and vehicle electrification significantly impact transit ridership, with some interactions between these levers exhibiting synergistic or canceling effects. The case study underscores the necessity of integrating transit modeling within a broader multimodal network simulation and decision-making context.