pwc-archive/papers-with-abstracts huggingface dataset:
AVSim - Realistic Simulation Framework for Airborne and Vector-Borne Disease Dynamics
Authors: Pandula Thennakoon, Mario De Silva, M. Mahesha Viduranga, Sashini Liyanage, Roshan Godaliyadda, Mervyn Parakrama Ekanayake, Vijitha Herath, Anuruddhika Rathnayake, Ganga Thilakarathne, Janaka Ekanayake, Samath Dharmarathne
Date: 2025-02-10 00:00:00

The COVID-19 pandemic underscored the critical need for rapid epidemic trend identification and effective intervention strategies to mitigate disease progression and its socio-economic impact. Concurrent with emerging threats, endemic diseases like dengue continue to strain healthcare systems, particularly in populous, economically challenged nations. This paper introduces AVSim (Airborne and Vectorborne Simulator), an agent-based model designed to provide granular insights for optimizing resource allocation within existing healthcare management frameworks. AVSim leverages realistic human mobility and behavioral patterns to simulate disease propagation within a detailed, scalable environment encompassing homes, schools, hospitals, and commercial venues. Human movement is modeled based on occupational and behavioral patterns, including age-specific activities. The simulator incorporates age- and environment-specific disease outcomes, host-host and host-vector interactions, and multiple disease stages, including mild, severe, and critical phases. Immunity, quarantine, and hospitalization are also modeled. Furthermore, AVSim supports tracing the path of disease spread, providing micro-level insights into transmission dynamics. Implemented in Python, AVSim offers flexibility and extensibility, enabling users to create highly customized scenarios for airborne and vector-borne disease modeling. Case studies demonstrating AVSim's application to COVID-19 and dengue illustrate its potential for generating actionable epidemic insights, thereby enhancing public health planning and response.
Learning the Frequency Dynamics of the Power System Using Higher-order Dynamic Mode Decomposition
Authors: Xiao Li, Xinyi Wen, Benjamin Schäfer
Date: 2025-02-10 00:00:00

The increasing penetration of renewable energy sources, characterised by low inertia and intermittent disturbances, presents substantial challenges to power system stability. As critical indicators of system stability, frequency dynamics and associated oscillatory phenomena have attracted significant research attention. While existing studies predominantly employ linearized models, our findings demonstrate that linear approximations exhibit considerable errors when predicting frequency oscillation dynamics across multiple time scales, thus necessitating the incorporation of nonlinear characteristics. This paper proposes a data-driven approach based on higher-order dynamical mode decomposition (HODMD) for learning frequency dynamics. The proposed method offers distinct advantages over alternative nonlinear methods, including no prior knowledge required, adaptability to high-dimensional systems, and robust performance. Furthermore, HODMD demonstrates superior capability in capturing system-wide spatio-temporal modes, successfully identifying modal behaviour that remains undetectable through standard Dynamic Mode Decomposition techniques. The efficacy of the proposed methodology is validated through comprehensive case studies on both IEEE 14-bus and WECC systems.
Analysis and Optimization of Robustness in Multiplex Flow Networks Against Cascading Failures
Authors: Orkun İrsoy, Osman Yağan
Date: 2025-02-10 00:00:00

Networked systems are susceptible to cascading failures, where the failure of an initial set of nodes propagates through the network, often leading to system-wide failures. In this work, we propose a multiplex flow network model to study robustness against cascading failures triggered by random failures. The model is inspired by systems where nodes carry or support multiple types of flows, and failures result in the redistribution of flows within the same layer rather than between layers. To represent different types of interdependencies between the layers of the multiplex network, we define two cases of failure conditions: layer-independent overload and layer-influenced overload. We provide recursive equations and their solutions to calculate the steady-state fraction of surviving nodes, validate them through a set of simulation experiments, and discuss optimal load-capacity allocation strategies. Our results demonstrate that allocating the total excess capacity to each layer proportional to the mean effective load in the layer and distributing that excess capacity equally among the nodes within the layer ensures maximum robustness. The proposed framework for different failure conditions allows us to analyze the two overload conditions presented and can be extended to explore more complex interdependent relationships.
Intelligent Reconfigurable Optical Wireless Ether
Authors: Hongwei Cui, Soung Chang Liew
Date: 2025-02-10 00:00:00
Tasks: Fairness

Optical wireless communication (OWC) uses light for wireless data transmission, potentially providing faster and more secure communication than traditional radio-frequency-based techniques like Wi-Fi. However, light's high directionality and its limited penetration ability restrict the signal coverage. To address this limitation, we propose an artificial "optical wireless ether" (OWE) fabric. OWE acts as a reconfigurable electromagnetic (EM) wave-propagating medium, intelligently enhancing the strength of light signals and redirecting their propagation to cover a broader area. Our proposed ether fabric comprises simple optical signal amplification units, called ether amplifiers (EAs), strategically placed in the environment, e.g., on ceilings. The EAs amplify and propagate signals at the analog level and are agnostic to the signal format: Signals propagate wirelessly between the EAs, losing strength due to attenuation during transmission but regaining it as they pass through the EAs. The key challenge in OWE design lies in the fact that, while increasing EA gains can extend signal coverage, it can also create positive feedback loops, resulting in self-interference and amplifier saturation, which distort the signals -- the key challenge in OWE design. This paper presents a systematic theoretical analysis to prevent amplifier saturation while optimizing the performance of OWE in both single-basic-service-set (single-BSS) and multiple-BSS scenarios. Optimization objectives could include signal-to-noise ratio, resource allocation fairness, and mutual interference. Furthermore, we conducted simulations and experiments to corroborate our theories. To our knowledge, ours is the first experimental demonstration of the feasibility of an artificial ether fabric for extending and guiding light propagation, laying a solid groundwork for future development and exploration of OWE.
A Comprehensive Energy Management Application Method considering Smart Home Occupant Behavior using IoT and Real Big Data
Authors: S. Saba Rafiei, Mahdi S. Naderi, Mehrdad Abedi
Date: 2025-02-09 00:00:00
Tasks: energy management, Management, Scheduling

One of the most far-reaching use cases of the internet of things is in smart grid and smart home operation. The smart home concept allows residents to control, monitor, and manage their energy consumption with minimum loss and self-involvement. Since each household's lifestyle and energy consumption is unique, the management system needs background knowledge about residents' energy consumption behavioral patterns for more accurate planning. To obtain this information, data related to residents' consumption records must be processed. This research has attempted to provide an optimal decentralized management system consisting of interoperable sections to forecast, optimize, schedule, and implement load management in a smart home. Comparing different prediction models using 4 years of 1-min interval real data of a smart home with photovoltaic generation (PV) and electric vehicle (EV), forecasting non-controllable loads and taking a deterministic approach in different scenarios, the system uses mixed integer linear programming (MILP) to provide load scheduling with the objective of an optimal total energy cost reduction with minimum changes in the household's desired consumption compared to the initial state. The results have shown that the proposed system has reliable performance due to the high precision of the forecast and has led to increased energy efficiency, reduced energy cost (up to 62. 05\%), reduced peak-to-average ratio (PAR) (up to 44. 19\%) and reduced standard deviation (SD) (up to 19. 70\%) in net consumption.
Enhanced Rapid Detection of High-impedance Arc Faults in Medium Voltage Electrical Distribution Networks
Authors: Kriti Thakur, Divyanshi Dwivedi, K. Victor Sam Moses Babu, Alivelu Manga Parimi, Prasanta K. Panigrahi, Pradeep Kumar Yemula, Pratyush Chakraborty, Mayukha Pal
Date: 2025-02-09 00:00:00
Tasks: ARC, Fault Detection

High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Previous research has concentrated predominantly on arc fault detection in low-voltage systems, leaving a significant gap in medium-voltage applications. In this work, a novel approach has been developed that enables rapid arc fault detection for medium-voltage distribution lines. In contrast to existing black-box feature-based approaches, the Hankel alternative view of the Koopman (HAVOK) analysis developed from nonlinear dynamics has been applied, which not only offers interpretable features but also opens up new application options in the area of arc fault detection. The method achieves a much faster detection speed in 0.45 ms, 99.36\% enhanced compared to harmonic randomness and waveform distortion method, thus making it suitable for real-time applications. It demonstrates the ability to detect arc faults across various scenarios, including different grounding surfaces and levels of system noise, boosting its practical importance for stakeholders in safety-critical industries.
Exploiting the Hidden Capacity of MMC Through Accurate Quantification of Modulation Indices
Authors: Qianhao Sun, Jingwei Meng, Ruofan Li, Mingchao Xia, Qifang Chen, Jiejie Zhou, Meiqi Fan, Peiqian Guo
Date: 2025-02-09 00:00:00

The modular multilevel converter (MMC) has become increasingly important in voltage-source converter-based high-voltage direct current (VSC-HVDC) systems. Direct and indirect modulation are widely used as mainstream modulation techniques in MMCs. However, due to the challenge of quantitatively evaluating the operation of different modulation schemes, the academic and industrial communities still hold differing opinions on their performance. To address this controversy, this paper employs the state-of-the-art computational methods and quantitative metrics to compare the performance among different modulation schemes. The findings indicate that direct modulation offers superior modulation potential for MMCs, highlighting its higher ac voltage output capability and broader linear PQ operation region. Conversely, indirect modulation is disadvantaged in linear modulation, which indicates inferior output voltage capability. Furthermore, this paper delves into the conditions whereby direct and indirect modulation techniques become equivalent in steady-state. The study findings suggest that the modulation capability of direct modulation is the same as that of indirect modulation in steady-state when additional controls, including closed-loop capacitor voltage control and circulating current suppression control (CCSC), are simultaneously active. Simulation and experiments verify the correctness and validity.
A Grid-Forming HVDC Series Tapping Converter Using Extended Techniques of Flex-LCC
Authors: Qianhao Sun, Ruofan Li, Jichen Wang, Mingchao Xia, Qifang Chen, Meiqi Fan, Gen Li, Xuebo Qiao
Date: 2025-02-09 00:00:00

This paper discusses an extension technology for the previously proposed Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension involves modifying the arm internal-electromotive-force control, redesigning the main-circuit parameters, and integrating a low-power coordination strategy. As a result, the Flex-LCC transforms from a grid-forming (GFM) voltage source converter (VSC) based on series-connected LCC and FBMMC into a novel GFM HVDC series tapping converter, referred to as the Extended Flex-LCC (EFLCC). The EFLCC provides dc characteristics resembling those of current source converters (CSCs) and ac characteristics resembling those of GFM VSCs. This makes it easier to integrate relatively small renewable energy sources (RESs) that operate in islanded or weak-grid supported conditions with an existing LCC-HVDC. Meanwhile, the EFLCC distinguishes itself by requiring fewer full-controlled switches and less energy storage, resulting in lower losses and costs compared to the FBMMC HVDC series tap solution. In particular, the reduced capacity requirement and the wide allowable range of valve-side ac voltages in the FBMMC part facilitate the matching of current-carrying capacities between full-controlled switches and thyristors. The application scenario, system-level analysis, implementation, converter-level operation, and comparison of the EFLCC are presented in detail in this paper. The theoretical analysis is confirmed by experimental and simulation results.
Machine learning-based hybrid dynamic modeling and economic predictive control of carbon capture process for ship decarbonization
Authors: Xuewen Zhang, Kuniadi Wandy Huang, Dat-Nguyen Vo, Minghao Han, Benjamin Decardi-Nelson, Xunyuan Yin
Date: 2025-02-09 00:00:00
Tasks: Model Predictive Control

Implementing carbon capture technology on-board ships holds promise as a solution to facilitate the reduction of carbon intensity in international shipping, as mandated by the International Maritime Organization. In this work, we address the energy-efficient operation of shipboard carbon capture processes by proposing a hybrid modeling-based economic predictive control scheme. Specifically, we consider a comprehensive shipboard carbon capture process that encompasses the ship engine system and the shipboard post-combustion carbon capture plant. To accurately and robustly characterize the dynamic behaviors of this shipboard plant, we develop a hybrid dynamic process model that integrates available imperfect physical knowledge with neural networks trained using process operation data. An economic model predictive control approach is proposed based on the hybrid model to ensure carbon capture efficiency while minimizing energy consumption required for the carbon capture process operation. The cross-entropy method is employed to efficiently solve the complex non-convex optimization problem associated with the proposed hybrid model-based economic model predictive control method. Extensive simulations, analyses, and comparisons are conducted to verify the effectiveness and illustrate the superiority of the proposed framework.
Cooperative Optimization of Grid-Edge Cyber and Physical Resources for Resilient Power System Operation
Authors: Xiang Huo, Shining Sun, Khandaker Akramul Haque, Leen Al Homoud, Ana E. Goulart, Katherine R. Davis
Date: 2025-02-09 00:00:00

The cooperative operation of grid-edge power and energy resources is crucial to improving the resilience of power systems during contingencies. However, given the complex cyber-physical nature of power grids, it is hard to respond timely with limited costs for deploying additional cyber and/or phyiscal resources, such as during a high-impact low-frequency cyber-physical event. Therefore, the paper examines the design of cooperative cyber-physical resource optimization solutions to control grid-tied cyber and physical resources. First, the operation of a cyber-physical power system is formulated into a constrained optimization problem, including the cyber and physical objectives and constraints. Then, a bi-level solution is provided to obtain optimal cyber and physical actions, including the reconfiguration of cyber topology (e.g., activation of communication links) in the cyber layer and the control of physical resources (e.g., energy storage systems) in the physical layer. The developed method improves grid resilience during cyberattacks and can provide guidance on the control of coupled physical side resources. Numerical simulation on a modified IEEE 14-bus system demonstrates the effectiveness of the proposed approach.
Graph Neural Networks for Efficient AC Power Flow Prediction in Power Grids
Authors: Seyedamirhossein Talebi, Kaixiong Zhou
Date: 2025-02-08 00:00:00
Tasks: Management

This paper proposes a novel approach using Graph Neural Networks (GNNs) to solve the AC Power Flow problem in power grids. AC OPF is essential for minimizing generation costs while meeting the operational constraints of the grid. Traditional solvers struggle with scalability, especially in large systems with renewable energy sources. Our approach models the power grid as a graph, where buses are nodes and transmission lines are edges. We explore different GNN architectures, including GCN, GAT, SAGEConv, and GraphConv to predict AC power flow solutions efficiently. Our experiments on IEEE test systems show that GNNs can accurately predict power flow solutions and scale to larger systems, outperforming traditional solvers in terms of computation time. This work highlights the potential of GNNs for real-time power grid management, with future plans to apply the model to even larger grid systems.
On the Convergence and Stability of Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning, and Online Decision Transformers
Authors: Miroslav Štrupl, Oleg Szehr, Francesco Faccio, Dylan R. Ashley, Rupesh Kumar Srivastava, Jürgen Schmidhuber
Date: 2025-02-08 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning

This article provides a rigorous analysis of convergence and stability of Episodic Upside-Down Reinforcement Learning, Goal-Conditioned Supervised Learning and Online Decision Transformers. These algorithms performed competitively across various benchmarks, from games to robotic tasks, but their theoretical understanding is limited to specific environmental conditions. This work initiates a theoretical foundation for algorithms that build on the broad paradigm of approaching reinforcement learning through supervised learning or sequence modeling. At the core of this investigation lies the analysis of conditions on the underlying environment, under which the algorithms can identify optimal solutions. We also assess whether emerging solutions remain stable in situations where the environment is subject to tiny levels of noise. Specifically, we study the continuity and asymptotic convergence of command-conditioned policies, values and the goal-reaching objective depending on the transition kernel of the underlying Markov Decision Process. We demonstrate that near-optimal behavior is achieved if the transition kernel is located in a sufficiently small neighborhood of a deterministic kernel. The mentioned quantities are continuous (with respect to a specific topology) at deterministic kernels, both asymptotically and after a finite number of learning cycles. The developed methods allow us to present the first explicit estimates on the convergence and stability of policies and values in terms of the underlying transition kernels. On the theoretical side we introduce a number of new concepts to reinforcement learning, like working in segment spaces, studying continuity in quotient topologies and the application of the fixed-point theory of dynamical systems. The theoretical study is accompanied by a detailed investigation of example environments and numerical experiments.
Evaluating the Techno-Economic Viability of a Solar PV-Wind Turbine Hybrid System with Battery Storage for an Electric Vehicle Charging Station in Khobar, Saudi Arabia
Authors: Ahmed S. AbdElrazek, Mohamed Soliman, Muhammad Khalid
Date: 2025-02-08 00:00:00

The main aim of this investigation is to replicate and enhance a sustainable hybrid energy structure that combines solar photovoltaic, wind turbines, battery storage. The study employs the Homer simulation model to evaluate the scaling, cost, and control strategy of this hybrid power system. This work primarily focuses on determining the most efficient design for a renewable energy generation system architecture for a significant electric vehicle charging station. The hybrid power system is designed to meet an AC base load of 2424.25 kWh/day with peak consumption of 444 kW. The simulation results indicate that the optimized components and the cost of energy are at an optimal level and the optimal design in terms of renewable energy penetration.
Design of optimal repetitive control based on EID estimator with adaptive periodic event-triggered mechanism for linear systems subjected to exogenous disturbances
Authors: Mohammed Soliman, Abdul-Wahid A. Saif
Date: 2025-02-08 00:00:00

The periodic signal tracking and the unknown disturbance rejection under limited communication resources are main important issues in many physical systems and practical applications. The control of such systems has some challenges such as time-varying delay, unknown external disturbances, structure uncertainty, and the heavy communication burden on the sensors and controller. These challenges affect the system performance and may destabilize the system. Hence, in this article, an improved scheme has been designed to overcome these challenges to achieve a good control performance based on optimization technique, and to guarantee the closed-loop system stability. The proposed scheme can be described as: modified repetitive control (MRC) with equivalent-input-disturbance (EID) estimator based on adaptive periodic event-triggered mechanism (APETM). The scheme that has been created is intended for linear systems that experience external disturbances which are not known, and must operate within constraints on communication resources. MRC based on EID has been developed with the goal of achieving periodic reference tracking and enhancing the ability to effectively reject both periodic and aperiodic unknown disturbances. In addition, utilizing APETM to reduce data transmission, computational burden and to save communication resources. Additionally, an optimization method is employed to fine-tune the parameters of the controller, enabling adjustments to the control and learning actions. Overall architecture of the system, incorporating the APETM-MRC with the utilization of an EID estimator and optimal techniques, can be described as a time-varying delay system. Proposed schemes were demonstrated to be effective, feasible, and robust through simulated application.