pwc-archive/papers-with-abstracts huggingface dataset:
Detection of High-Impedance Low-Current Arc Faults at Electrical Substations
Authors: K. Victor Sam Moses Babu, Divyanshi Dwivedi, Marcelo Esteban Valdes, Pratyush Chakraborty, Prasanta Kumar Panigrahi, Mayukha Pal
Date: 2024-10-14 00:00:00
Tasks: ARC, Fault Detection

Arcing faults in low voltage (LV) distribution systems associated with arc-flash risk and potentially significant equipment damage are notoriously difficult to detect under some conditions. Especially so when attempting to detect using sensing at the line, high voltage side of a substation transformer. This paper presents an analytics-based physics-aware approach to detect high-impedance, low-current arcing faults from the primary side of the substation transformer at current thresholds, below normal operating events, along with transformer inrush currents. The proposed methodology leverages the Hankel Alternative View Of Koopman Operator approach to differentiate arcing faults from standard operations, while the Series2Graph method is employed to identify the time of fault occurrence and duration. Unlike prior studies that detect such faults at the device or secondary transformer side, this work demonstrates successful fault detection at the primary side of the distribution substation transformer for faults occurring on the secondary side. The approach addresses the practical challenges of differentiating primary side expected and acceptable transients from similar magnitude LV arcing fault currents that may occur on the secondary side. The results demonstrate the efficacy of the proposed method in accurately identifying fault occurrence and duration, minimizing the risk of false positives during similar characteristic events, thus improving the reliability and operational efficiency of power distribution systems. This approach can benefit both traditional and smart power grids that employ similar transformer configurations.
Efficient ICBased Solutions for Medical Devices and Automotive Radars
Authors: Behnam Moradi Shahrbabak
Date: 2024-10-13 00:00:00

This thesis focuses on developing integrated circuit (IC) solutions for medical devices and automotive radars, and is divided into two main parts. Part One presents the design and evaluation of a miniaturized multi chip module (MCM) solution intended to deliver welldefined, charge balanced current stimuli directly to the inner ear. This section emphasizes the design of the supply chip, which includes a DC DC converter. It involves a comprehensive study aimed at optimizing and enhancing the efficiency of the design. Part Two investigates the fundamental principles of designing millimeter wave (mmWave) voltagecontrolled oscillators (VCOs). This section introduces a VCO with stateoftheart performance, showcasing advancements in mmWave technology. Overall, this thesis contributes to both the medical device field and automotive radar technology through innovative IC solutions.
Optimal Set-Membership Smoothing
Authors: Yudong Li, Yirui Cong, Xiangyun Zhou, Jiuxiang Dong
Date: 2024-10-13 00:00:00

This article studies the Set-Membership Smoothing (SMSing) problem for non-stochastic Hidden Markov Models. By adopting the mathematical concept of uncertain variables, an optimal SMSing framework is established for the first time. This optimal framework reveals the principles of SMSing and the relationship between set-membership filtering and smoothing. Based on the design principles, we put forward two SMSing algorithms: one for linear systems with zonotopic constrained uncertainties, where the solution is given in a closed form, and the other for a class of nonlinear systems. Numerical simulations corroborate the effectiveness of our theoretical results.
Flexible Operation of Electricity-HCNG Networks with Variable Hydrogen Fraction: A Distributionally Robust Joint Chance-Constrained Approach
Authors: Sicheng Liu, Bo Yang, Xu Yang, Xin Li, Zhaojian Wang, Xinping Guan
Date: 2024-10-13 00:00:00

Hydrogen-enriched compressed natural gas (HCNG) is a promising way to utilize surplus renewable energy through hydrogen electrolysis and blending it into natural gas. However, the optimal hydrogen volume fraction (HVF) of HCNG varies following the daily fluctuations of renewable energy. Besides, facing the rapid volatility of renewable energy, ensuring rapid and reliable real-time adjustments is challenging for electricity-HCNG (E-HCNG) coupling networks. To this end, this paper proposes a flexible operation framework for electricity-HCNG (E-HCNG) networks against the fluctuations and volatility of renewable energy. Based on operations with variable HVF, the framework developed an E-HCNG system-level affine policy, which allows real-time re-dispatch of operations according to the volatility. Meanwhile, to guarantee the operational reliability of the affine policy, a distributionally robust joint chance constraint (DRJCC) is introduced, which limits the violation probability of operational constraints under the uncertainties of renewable energy volatility. Furthermore, in the solving process, to mitigate the over-conservation in DRJCC decomposition, an improved risk allocation method is proposed, utilizing the correlations among violations under the affine policy. Moreover, to tackle the non-convexities arising from the variable HVF, customized approximations for HCNG flow formulations are developed. The problem is finally reformulated into a mix-integer second-order cone programming problem. The effectiveness of the proposed method is validated both in small-scale and large-scale experiments.
Integrating Reinforcement Learning and Large Language Models for Crop Production Process Management Optimization and Control through A New Knowledge-Based Deep Learning Paradigm
Authors: Dong Chen, Yanbo Huang
Date: 2024-10-13 00:00:00
Tasks: Management, Offline RL, Reinforcement Learning (RL)

Efficient and sustainable crop production process management is crucial to meet the growing global demand for food, fuel, and feed while minimizing environmental impacts. Traditional crop management practices, often developed through empirical experience, face significant challenges in adapting to the dynamic nature of modern agriculture, which is influenced by factors such as climate change, soil variability, and market conditions. Recently, reinforcement learning (RL) and large language models (LLMs) bring transformative potential, with RL providing adaptive methodologies to learn optimal strategies and LLMs offering vast, superhuman knowledge across agricultural domains, enabling informed, context-specific decision-making. This paper systematically examines how the integration of RL and LLMs into crop management decision support systems (DSSs) can drive advancements in agricultural practice. We explore recent advancements in RL and LLM algorithms, their application within crop management, and the use of crop management simulators to develop these technologies. The convergence of RL and LLMs with crop management DSSs presents new opportunities to optimize agricultural practices through data-driven, adaptive solutions that can address the uncertainties and complexities of crop production. However, this integration also brings challenges, particularly in real-world deployment. We discuss these challenges and propose potential solutions, including the use of offline RL and enhanced LLM integration, to maximize the effectiveness and sustainability of crop management. Our findings emphasize the need for continued research and innovation to unlock the full potential of these advanced tools in transforming agricultural systems into optimal and controllable ones.
Distributed Area Coverage Control with Imprecise Robot Localization
Authors: Sotiris Papatheodorou, Yiannis Stergiopoulos, Anthony Tzes
Date: 2024-10-12 00:00:00
Tasks: Collision Avoidance

This article examines the problem of area coverage for a network of mobile robots with imprecise agent localization. Each robot has uniform radial sensing ability, governed by first order kinodynamics. The convex-space is partitioned based on the Guaranteed Voronoi (GV) principle and each robot's area of responsibility corresponds to its GV-cell, bounded by hyperbolic arcs. The proposed control law is distributed, demands the positioning information about its GV-Delaunay neighbors and has an inherent collision avoidance property.
Quantify Gas-to-Power Fault Propagation Speed:A Semi-Implicit Simulation Approach
Authors: Ruizhi Yu, Suhan Zhang, Wei Gu, Shuai Lu
Date: 2024-10-12 00:00:00

Relying heavily on the secure supply of natural gas, the modern clean electric power systems are prone to the gas disturbances induced by the inherent rupture and leakage faults. For the first time, this paper studies the cross-system propagation speed of these faults using a simulation-based approach. Firstly, we establish the differential algebraic equation models of the rupture and leakage faults respectively. The boundary conditions at the fault locations are derived using the method of characteristics. Secondly, we propose utilizing a semi-implicit approach to perform post-fault simulations. The approach, based on the stiffly-accurate Rosenbrock scheme, possesses the implicit numerical stability and explicit computation burdens. Therefore, the high-dimensional and multi-time-scale stiff models can be solved in an efficient and robust way. Thirdly, to accurately locate the simulation events, which can not be predicted a priori, we propose a critical-time-location strategy based on the continuous Runge-Kutta approach. In case studies, we verified the accuracy and the efficiency superiority of the proposed simulation approach. The impacts of gas faults on gas and power dynamics were investigated by simulation, where the critical events were identified accurately. We found that the fault propagation speed mainly depends on the fault position and is influenced by the pipe frictions. The bi-directional coupling between gas and power may lead to cascading failures.
Procedural Generation of Communication Networks in Power Systems
Authors: Xavier Weiss, Lars Nordström, Patrik Hilber, Emre Süren
Date: 2024-10-12 00:00:00

Power system communication networks enable operators to remotely monitor and control field equipment. The sophistication of these networks is also increasing as operators continue the trend towards digitization, which is beneficial in integrating distributed energy resources. However, as the attack surface increases in size so too does the risk of cyberattacks. The topology, configuration and composition of communication networks is therefore confidential since this can provide information to attackers. As a result, the number of benchmarks available for research purposes is limited. A tool for procedurally generating communication network topologies is therefore proposed. While primarily intended as an enabler for public research into communication networks, this tool also allows general insights to be gained into the effect of communication network design on the vulnerability of networks to cyberattacks. The tool includes the ability to encapsulate network characteristics in JSON specification files, which is demonstrated with example Advanced Metering Infrastructure (AMI), Supervisory Control and Data Acquisition (SCADA) and Wide Area Monitoring (WAM) specification files. The SCADA network generation is then compared to a real-world case. Finally, the effect of network redundancy on the networks cyber resilience is investigated.
A Framework to Estimate Life Cycle Emissions for Vehicle-Integrated Photovoltaic Systems
Authors: Maurizio Clemente, Luuk van Sundert, Mauro Salazar, Theo Hofman
Date: 2024-10-12 00:00:00

This paper presents a framework to estimate the environmental impact of solar electric vehicles, accounting for the emissions caused by photovoltaic system production as well as vehicle use. We leverage a cradle-to-gate life cycle assessment to estimate the greenhouse gas emissions of the vehicle-integrated photovoltaic system, from the raw material extraction to the final panel assembly, including the effect of the electricity mix both at the factory location and in the country of use. %the vehicle's life cycle, considering both Furthermore, we modify an existing optimization framework for battery electric vehicles to optimally design a solar electric vehicle and estimate its energy consumption. We showcase our framework by analyzing a case study where the mono-crystalline silicon extraction and refinement processes occur in China, while the final assembly of the panel is in The Netherlands, generating 118 kg of CO2 equivalents per square meter of solar panel. The results suggest that it is generally beneficial to operate solar electric vehicles in countries with a high irradiation index. However, when the local electricity mix already displays a low carbon intensity, the additional emissions introduced by the panel are unnecessary, requiring a longer vehicle lifetime to reach an advantageous emission balance.
Optimal Interval Observers for Bounded Jacobian Nonlinear Dynamical Systems
Authors: Mohammad Khajenejad, Tarun Pati, Sze Zheng Yong
Date: 2024-10-11 00:00:00

In this chapter, we introduce two interval observer designs for discrete-time (DT) and continuous-time (CT) nonlinear systems with bounded Jacobians that are affected by bounded uncertainties. Our proposed methods utilize the concepts of mixed-monotone decomposition and embedding systems to design correct-by-construction interval framers, i.e., the interval framers inherently bound the true state of the system without needing any additional constraints. Further, our methods leverage techniques for positive/cooperative systems to guarantee global uniform ultimate boundedness of the framer error, i.e., the proposed interval observer is input-to-state stable. Specifically, our two interval observer designs minimize the $\mathcal{H}_{\infty}$ and $L_1$ gains, respectively, of the associated linear comparison system of the framer error dynamics. Moreover, our designs adopt a multiple-gain observer structure, which offers additional degrees of freedom, along with coordinate transformations that may improve the feasibility of the resulting optimization programs. We will also discuss and propose computationally tractable optimization formulations to compute the observer gains. Finally, we compare the efficacy of the proposed designs against existing DT and CT interval observers.
Optimal Feedback Stabilizing Control of Bounded Jacobian Discrete-Time Systems via Interval Observers
Authors: Mohammad Khajenejad
Date: 2024-10-11 00:00:00

This paper addresses optimal feedback stabilizing control for bounded Jacobian nonlinear discrete-time (DT) systems with nonlinear observations, affected by state and process noise. Instead of directly stabilizing the uncertain system, we propose stabilizing a higher-dimensional interval observer whose states enclose the true system states. Our nonlinear control approach introduces additional flexibility compared to linear methods, compensating for system nonlinearities and allowing potentially tighter closed-loop intervals. We also establish a separation principle, enabling independent design of observer and control gains, and derive tractable linear matrix inequalities, resulting in a stable closed-loop system.
Towards Input-Convex Neural Network Modeling for Battery Optimization in Power Systems
Authors: Arash Omidi, Tanmay Mishra, Mads R. Almassalkhi
Date: 2024-10-11 00:00:00

Battery energy storage systems (BESS) play an increasingly vital role in integrating renewable generation into power grids due to their ability to dynamically balance supply. Grid-tied batteries typically employ power converters, where part-load efficiencies vary non-linearly. While this non-linearity can be modeled with high accuracy, it poses challenges for optimization, particularly in ensuring computational tractability. In this paper, we consider a non-linear BESS formulation based on the Energy Reservoir Model (ERM). A data-driven approach is introduced with the input-convex neural network (ICNN) to approximate the nonlinear efficiency with a convex function. The epigraph of the convex function is used to engender a convex program for battery ERM optimization. This relaxed ICNN method is applied to two battery optimization use-cases: PV smoothing and revenue maximization, and it is compared with three other ERM formulations (nonlinear, linear, and mixed-integer). Specifically, ICNN-based methods appear to be promising for future battery optimization with desirable feasibility and optimality outcomes across both use-cases.
Privacy-Preserving Optimal State Estimation with Low Complexity via Cramér-Rao Lower Bound Approach
Authors: Liping Guo, Jimin Wang, Yanlong Zhao, Ji-Feng Zhang
Date: 2024-10-11 00:00:00
Tasks: Privacy Preserving, State Estimation

This paper addresses the optimal state estimation problem for dynamic systems while preserving private information against an adversary. To dominate the adversary's estimation accuracy about private information in the mean square error (MSE) sense, the Cram\'er-Rao lower bound (CRLB) is employed to evaluate privacy level. The problem is formulated as a constrained optimization, which minimizes the MSE of the state estimate with a constraint on privacy level, achieving a trade-off between privacy and utility. To solve the constrained optimization problem, an explicit expression for CRLB is first provided using the information inequality. To overcome the increasing sizes of the involved matrices over time, a low-complexity approach is then proposed to achieve online calculation for CRLB, significantly reducing computational complexity. Next, the optimization problem is relaxed to a semi-definite programming problem, and a relaxed solution is provided. Finally, a privacy-preserving state estimation algorithm with low complexity is developed and proved to achieve differential privacy. Two illustrative examples, including a practical case of building occupancy, demonstrate the effectiveness of the proposed algorithm.
Convolutional Neural Network Design and Evaluation for Real-Time Multivariate Time Series Fault Detection in Spacecraft Attitude Sensors
Authors: Riccardo Gallon, Fabian Schiemenz, Alessandra Menicucci, Eberhard Gill
Date: 2024-10-11 00:00:00
Tasks: Anomaly Detection, Fault Detection, Time Series

Traditional anomaly detection techniques onboard satellites are based on reliable, yet limited, thresholding mechanisms which are designed to monitor univariate signals and trigger recovery actions according to specific European Cooperation for Space Standardization (ECSS) standards. However, Artificial Intelligence-based Fault Detection, Isolation and Recovery (FDIR) solutions have recently raised with the prospect to overcome the limitations of these standard methods, expanding the range of detectable failures and improving response times. This paper presents a novel approach to detecting stuck values within the Accelerometer and Inertial Measurement Unit of a drone-like spacecraft for the exploration of Small Solar System Bodies (SSSB), leveraging a multi-channel Convolutional Neural Network (CNN) to perform multi-target classification and independently detect faults in the sensors. Significant attention has been dedicated to ensuring the compatibility of the algorithm within the onboard FDIR system, representing a step forward to the in-orbit validation of a technology that remains experimental until its robustness is thoroughly proven. An integration methodology is proposed to enable the network to effectively detect anomalies and trigger recovery actions at the system level. The detection performances and the capability of the algorithm in reaction triggering are evaluated employing a set of custom-defined detection and system metrics, showing the outstanding performances of the algorithm in performing its FDIR task.
Achieving multi uav best viewpoint coordination in obstructed environments
Authors: Mirko Baglioni, Apurva Patil, Luis Sentis, Anahita Jamshidnejad
Date: 2024-10-11 00:00:00
Tasks: Unity

Wildfire suppression is a complex task that poses high risks to humans. Using robotic teams for wildfire suppression enhances the safety and efficiency of detecting, monitoring, and extinguishing fires. We propose a control architecture based on task hierarchical control for the autonomous steering of a system of flying robots in wildfire suppression. We incorporate a novel line-of-sight obstacle avoidance method that calculates the best viewpoints and ensures an occlusion-free view for the suppression robot during the mission. Path integral control generates optimal trajectories towards the goals. We conduct an ablation study to assess the effectiveness of our approach by comparing it to scenarios where these key components are excluded, in order to validate the approach in simulations using Matlab and Unity. The results demonstrate significant performance improvements, with 44.0 % increase in effectiveness with the new line-of-sight obstacle avoidance task and up to 39.6 % improvement when using path integral control.
A System of Bidirectional Power Routing Toward Multi-energy Management
Authors: Shiu Mochiyama, Ryo Takahashi, Yoshihiko Susuki, Tsutomu Wakabayashi, Takumi Tanaka, Toshinari Momose, Hideki Yamaguchi
Date: 2024-10-11 00:00:00
Tasks: energy management, Management

In this paper, we propose a system of bidirectional power routing for inter-house multi-energy management systems that utilize electricity and hydrogen as energy carriers. The key is to share private facilities such as photovoltaic panels and batteries among a group of houses along with a common hydrogen system. A power router of line switching type is introduced as a physical interface to realize the sharing economy between households. The proposed system offers a unique measure to address the urgent challenges of today's multi-energy system, namely increasing the renewables' self-consumption, enhancing the energy system's resilience, and providing traceability of hydrogen in terms of renewability certification. We also present an experimental demonstration under a simplified scenario using prototype hardware.
Distributed Adaptive Consensus with Obstacle and Collision Avoidance for Networks of Heterogeneous Multi-Agent Systems
Authors: Armel Koulong, Ali Pakniyat
Date: 2024-10-11 00:00:00
Tasks: Collision Avoidance

This paper presents a distributed adaptive control strategy for multi-agent systems with heterogeneous dynamics and collision avoidance. We propose an adaptive control strategy designed to ensure leader-following formation consensus while effectively managing collision and obstacle avoidance using potential functions. By integrating neural network-based disturbance estimation and adaptive tuning laws, the proposed strategy ensures consensus and stability in leader-following formations under fixed topologies.