pwc-archive/papers-with-abstracts huggingface dataset:
Addressing intra-area oscillations and frequency stability after DC segmentation of a large AC power system
Authors: Mathieu Robin, Javier Renedo, Juan Carlos Gonzalez-Torres, Aurelio Garcia-Cerrada, Luis Rouco, Abdelkrim Benchaib, Pablo Garcia-Gonzalez
Date: 2024-06-26 00:00:00
Tasks: Segmentation

In the last decades, various events have shown that electromechanical oscillations are a major concern for large interconnected Alternating Current (AC) power systems. Segmentation of AC power systems with High Voltage Direct Current (HVDC) systems (DC segmentation, for short) is a method that consists in turning large AC grids into a set of asynchronous AC clusters linked by HVDC links. It is a promising solution to mitigate electromechanical oscillations and other issues. In particular, an appropriately placed DC segmentation can stop a selected inter-area electromechanical oscillation mode. However, without supplementary controllers, DC segmentation will not contribute to the damping of the intra-area oscillation modes in the remaining AC clusters and will deteriorate the frequency stability of the power system. This paper aims at filling this gap and proposes the use of DC segmentation with HVDC systems based on Voltage Source Converters (VSC-HVDC) with supplementary controllers in the converter stations: (a) active-power supplementary controllers for frequency support among the asynchronous AC clusters and (b) a reactive-power supplementary controllers for Power Oscillation Damping (POD-Q), in order to damp the intra-area oscillation modes. The proposed supplementary controllers and their design will be presented, and their efficiency will be demonstrated on the Nordic 44 test system with DC segmentation by means of non-linear time-domain simulation and small-signal stability analysis.
Algebraic Connectivity Control and Maintenance in Multi-Agent Networks under Attack
Authors: Wenjie Zhao, Diego Deplano, Zhiwu Li, Alessandro Giua, Mauro Franceschelli
Date: 2024-06-26 00:00:00

This paper studies the problem of increasing the connectivity of an ad-hoc peer-to-peer network subject to cyber-attacks targeting the agents in the network. The adopted strategy involves the design of local interaction rules for the agents to locally modify the graph topology by adding and removing links with neighbors. Two distributed protocols are presented to boost the algebraic connectivity of the network graph beyond $k-2\sqrt{k-1}$ where $k\in \mathbb{N}$ is a free design parameter; these two protocols are achieved through the distributed construction of random (approximate) regular graphs. One protocol leverages coordinated actions between pairs of neighboring agents and is mathematically proven to converge to the desired graph topology. The other protocol relies solely on the uncoordinated actions of individual agents and it is validated by a spectral analysis through Monte-Carlo simulations. Numerical simulations offer a comparative analysis with other state-of-the-art algorithms, showing the ability of both proposed protocols to maintain high levels of connectivity despite attacks carried out with full knowledge of the network structure, and highlighting their superior performance.
USLC: Universal Self-Learning Control via Physical Performance Policy-Optimization Neural Network
Authors: Yanhui Zhang, Weifang Chen
Date: 2024-06-26 00:00:00
Tasks: Self-Learning

This study addresses the challenge of achieving real-time Universal Self-Learning Control (USLC) in nonlinear dynamic systems with uncertain models. The proposed control method incorporates a Universal Self-Learning module, which introduces a model-free online executor-evaluator framework to enable controller adaptation in the presence of unknown disturbances. By leveraging a neural network model trained on historical system performance data, the controller can autonomously learn to approximate optimal performance during each learning cycle. Consequently, the controller's structural parameters are incrementally adjusted to achieve a performance threshold comparable to human-level performance. Utilizing nonlinear system stability theory, specifically in the context of three-dimensional manifold space, we demonstrate the stability of USLC in Lipschitz continuous systems. We illustrate the USLC framework numerically with two case studies: a low-order circuit system and a high-order morphing fixed-wing attitude control system. The simulation results verify the effectiveness and universality of the proposed method.
Coordination of Transmission and Distribution Systems in Load Restoration
Authors: Santosh Sharma
Date: 2024-06-26 00:00:00

The power distribution system is evolving in the form of an intelligent grid. The proliferation of distributed energy resources (DERs) makes the previously passive system active and more complicated. With the adoption of de-carbonization principles, large-scale coal and nuclear power plants are being gradually replaced by renewables and carbon-free DERs. With this rapid transformation, the power system operates with less inertia and minimal margins. In recent years, power systems have been facing apocalyptic weather events more frequently, and large-scale blackouts have become regular. After the complete or partial blackouts, the power system goes through different stages before it reaches the normal operating condition. The load restoration is the stage where the power system is fully established after the blackouts; however, due to the limiting ramping rates of centralized generation, the energization of large amounts of loads is delayed by some time. To mitigate the negative impact of ramping rates of centralized generation, DERs in distribution systems are proposed to serve the loads in both transmission and distribution systems in coordination with limited centralized generation in the transmission system. The problem is formulated as a centralized or integrated transmission and distribution (T$\&$D) coordination model. The modified IEEE 14 bus test case and IEEE 13 node test feeders are used to validate the proposed strategy; the results indicate the validity of the proposed model.
Tiered Service Architecture for Remote Patient Monitoring
Authors: Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker
Date: 2024-06-26 00:00:00

We develop a remote patient monitoring (RPM) service architecture, which has two tiers of monitoring: ordinary and intensive. The patient's health state improves or worsens in each time period according to certain probabilities, which depend on the monitoring tier. The patient incurs a "loss of quality of life" cost or an "invasiveness" cost, which is higher under intensive monitoring than under ordinary. On the other hand, their health improves faster under intensive monitoring than under ordinary. In each period, the service decides which monitoring tier to use, based on the health of the patient. We investigate the optimal policy for making that choice by formulating the problem using dynamic programming. We first provide analytic conditions for selecting ordinary vs intensive monitoring in the asymptotic regime where the number of health states is large. In the general case, we investigate the optimal policy numerically. We observe a threshold behavior, that is, when the patient's health drops below a certain threshold the service switches them to intensive monitoring, while ordinary monitoring is used during adequately good health states of the patient. The modeling and analysis provides a general framework for managing RPM services for various health conditions with medically/clinically defined system parameters.
The Role of Electric Grid Research in Addressing Climate Change
Authors: Le Xie, Subir Majumder, Tong Huang, Qian Zhang, Ping Chang, David J. Hill, Mohammad Shahidehpour
Date: 2024-06-25 00:00:00

Addressing the urgency of climate change necessitates a coordinated and inclusive effort from all relevant stakeholders. Critical to this effort is the modeling, analysis, control, and integration of technological innovations within the electric energy system, which plays a crucial role in scaling up climate change solutions. This perspective article presents a set of research challenges and opportunities in the area of electric power systems that would be crucial in accelerating Gigaton-level decarbonization. Furthermore, it highlights institutional challenges associated with developing market mechanisms and regulatory architectures, ensuring that incentives are aligned for stakeholders to effectively implement the technological solutions on a large scale.
Koopman-LQR Controller for Quadrotor UAVs from Data
Authors: Zeyad M. Manaa, Ayman M. Abdallah, Mohammad A. Abido, Syed S. Azhar Ali
Date: 2024-06-25 00:00:00

Quadrotor systems are common and beneficial for many fields, but their intricate behavior often makes it challenging to design effective and optimal control strategies. Some traditional approaches to nonlinear control often rely on local linearizations or complex nonlinear models, which can be inaccurate or computationally expensive. We present a data-driven approach to identify the dynamics of a given quadrotor system using Koopman operator theory. Koopman theory offers a framework for representing nonlinear dynamics as linear operators acting on observable functions of the state space. This allows to approximate nonlinear systems with globally linear models in a higher dimensional space, which can be analyzed and controlled using standard linear optimal control techniques. We leverage the method of extended dynamic mode decomposition (EDMD) to identify Koopman operator from data with total least squares. We demonstrate that the identified model can be stabilized and controllable by designing a controller using linear quadratic regulator (LQR).
Translation of Nagumo's Foundational Work on Barrier Functions: On the Location of Integral Curves of Ordinary Differential Equations
Authors: Marcel Menner, Eugene Lavretsky
Date: 2024-06-25 00:00:00
Tasks: Translation

In 1942, Prof. Mitio Nagumo published his seminal paper on the location of integral curves of ordinary differential equations. Nagumo's paper provides the foundation of the set invariance of ordinary differential equations and barrier functions, which have recently gained popularity for the control design of safety critical dynamical systems. This translation shall serve the community with an easily accessible version of the original 1942 paper in English. A copy of Nagumo's paper in German is also attached as a reference. That copy was created by the Boeing Company, Germany, in an attempt to improve pdf format readability of the original paper.
Delta-V-Optimal Centralized Guidance Strategy For Under-actuated N-Satellite Formations
Authors: Ahmed Mahfouz, Gabriella Gaias, Florio Dalla Vedova, Holger Voos
Date: 2024-06-25 00:00:00

This paper addresses the computation of Delta-V-optimal, safe, relative orbit reconfigurations for satellite formations in a centralized fashion. The formations under consideration comprise an uncontrolled chief spacecraft flying with an arbitrary number, N, of deputy satellites, where each deputy is equipped with a single electric thruster. Indeed, this represents a technological solution that is becoming widely employed by the producers of small-satellite platforms. While adopting a single electric thruster does reduce the required power, weight, and size of the orbit control system, it comes at the cost of rendering the satellite under-actuated. In this setting, the satellite can provide a desired thrust vector only after an attitude maneuver is carried out to redirect the thruster nozzle opposite to the desired thrust direction. In order to further extend the applicability range of such under-actuated platforms, guidance strategies are developed to support different reconfiguration scenarios for N-satellite formations. This paper starts from a classical non-convex quadratically constrained trajectory optimization formulation, which passes through multiple simplifications and approximations to arrive to two novel convex formulations, namely a second-order cone programming formulation, and a linear programming one. Out of five guidance formulations proposed in this article, the most promising three were compared through an extensive benchmark analysis that is applied to fifteen of the most widely-used solvers. This benchmark experiment provides information about the key distinctions between the different problem formulations, and under which conditions each one of them can be recommended.
Equity-aware Load Shedding Optimization
Authors: Xin Fang, Wenbo Wang, Fei Ding
Date: 2024-06-25 00:00:00

Load shedding is usually the last resort to balance generation and demand to maintain stable operation of the electric grid after major disturbances. Current load-shedding optimization practices focus mainly on the physical optimality of the network power flow. This might lead to an uneven allocation of load curtailment, disadvantaging some loads more than others. Addressing this oversight, this paper introduces an innovative equity-aware load-shedding optimization model that emphasizes a fair allocation of load curtailment across the network. By proposing a novel equity indicator for load shedding and integrating it into an ACOPF-based optimization framework, we offer grid operators a more balanced and equitable load shedding strategy. Case studies highlight the importance of equity considerations in determining optimal load curtailment between buses.
Physics-Informed AI Inverter
Authors: Qing Shen, Yifan Zhou, Peng Zhang, Yacov A. Shamash, Roshan Sharma, Bo Chen
Date: 2024-06-25 00:00:00

This letter devises an AI-Inverter that pilots the use of a physics-informed neural network (PINN) to enable AI-based electromagnetic transient simulations (EMT) of grid-forming inverters. The contributions are threefold: (1) A PINN-enabled AI-Inverter is formulated; (2) An enhanced learning strategy, balanced-adaptive PINN, is devised; (3) extensive validations and comparative analysis of the accuracy and efficiency of AI-Inverter are made to show its superiority over the classical electromagnetic transient programs (EMTP).
Position and Time Determination without Prior State Knowledge via Onboard Optical Observations of Delta Scuti Variable Stars
Authors: Linyi Hou, Ishaan Bansal, Clark Davis, Siegfried Eggl
Date: 2024-06-25 00:00:00
Tasks: Position

We present a navigation concept for solving the lost in space and time problem using optical observations of $\delta$ Scuti variable stars. Only a small number of techniques exist that allow a spacecraft to recover from being lost in both space and time, which can be caused by a failure of the onboard clock and navigation systems. Optical observations of $\delta$ Scuti stars, which can be collected onboard from star trackers or navigation cameras, may enable autonomous position and time determination without requiring additional equipment or external communication. Our results indicate that less than one day of observation by the OSIRIS-APEX PolyCam may enable position and time determination accuracy within 0.03 au (3$\sigma$) and 3 seconds (3$\sigma$).
Optimal DC-link Voltage from Weight and Loss Perspective for eVTOLs
Authors: Abhijit Kulkarni, Torbjörn Thiringer, Remus Teodorescu
Date: 2024-06-25 00:00:00

Electric vertical takeoff and landing (eVTOL) aircraft are emerging as a modern transportation solution aimed at reducing urban traffic congestion and improving the carbon footprint. The power architecture in eVTOLs is defined by the dc bus formed by the battery packs and the power converter used to drive eVTOL motors. A high dc bus voltage is preferred for the power architecture since it can reduce the weight of power cables for a given power rating. However, the impact of high dc bus voltage on the efficiency of the drivetrain power converter must be considered, since reduced efficiency leads to poor battery pack utilization. In this paper, a systematic optimization study is performed considering SiC-based inverter for the drivetrain power converter. Optimal value of dc bus voltage is determined considering the flight profile of eVTOLs. A power converter topology is proposed that can provide optimal performance and enhance the lifetime of the batteries along with providing better monitoring, diagnostics and protection. The optimization strategy is validated experimentally, demonstrating the proposed power architecture's ability to maximize efficiency while enhancing the safety of the battery energy storage system in eVTOLs.
Robust Pareto Transistor Sizing of GaN HEMTs for Millimeter-Wave Applications
Authors: Rafael Perez Martinez, Stephen Boyd, Srabanti Chowdhury
Date: 2024-06-25 00:00:00
Tasks: Avg

This paper introduces a robust Pareto design approach for transistor sizing of Gallium Nitride (GaN) High Electron Mobility Transistors (HEMTs), particularly for power amplifier (PA) and low-noise amplifier (LNA) designs in 5G applications. We consider five key design variables and two settings (PAs and LNAs) where we have multiple objectives. We assess designs based on three critical objectives, evaluating each by its worst-case performance across a range of Gate-Source Voltages ($V_{\text{GS}}$). We conduct simulations across a range of $V_{\text{GS}}$ values to ensure a thorough and robust analysis. For PAs, the optimization goals are to maximize the worst-case modulated average output power ($P_{\text{out,avg}}$) and power-added efficiency ($\text{PAE}_{\text{avg}}$) while minimizing the worst-case average junction temperature ($T_{\text{j,avg}}$) under a modulated 64-QAM signal stimulus. In contrast, for LNAs, the focus is on maximizing the worst-case maximum oscillation frequency ($f_{\text{max}}$) and Gain, and minimizing the worst-case minimum noise figure ($\text{NF}_{\text{min}}$). We utilize a derivative-free optimization method to effectively identify robust Pareto optimal device designs. This approach enhances our comprehension of the trade-off space, facilitating more informed decision-making. Furthermore, this method is general across different applications. Although it does not guarantee a globally optimal design, we demonstrate its effectiveness in GaN transistor sizing. The primary advantage of this method is that it enables the attainment of near-optimal or even optimal designs with just a fraction of the simulations required for an exhaustive full-grid search.
A Near-Field Super-Resolution Network for Accelerating Antenna Characterization
Authors: Yuchen Gu, Hai-Han Sun, Daniel W. van der Weide
Date: 2024-06-25 00:00:00
Tasks: Super-Resolution

We present a deep neural network-enabled method to accelerate near-field (NF) antenna measurement. We develop a Near-field Super-resolution Network (NFS-Net) to reconstruct significantly undersampled near-field data as high-resolution data, which considerably reduces the number of sampling points required for NF measurement and thus improves measurement efficiency. The high-resolution near-field data reconstructed by the network is further processed by a near-field-to-far-field (NF2FF) transformation to obtain far-field antenna radiation patterns. Our experiments demonstrate that the NFS-Net exhibits both accuracy and generalizability in restoring high-resolution near-field data from low-resolution input. The NF measurement workflow that combines the NFS-Net and the NF2FF algorithm enables accurate radiation pattern characterization with only 11% of the Nyquist rate samples. Though the experiments in this study are conducted on a planar setup with a uniform grid, the proposed method can serve as a universal strategy to accelerate measurements under different setups and conditions.
Perturbed Decision-Focused Learning for Modeling Strategic Energy Storage
Authors: Ming Yi, Saud Alghumayjan, Bolun Xu
Date: 2024-06-24 00:00:00
Tasks: Model Predictive Control, Prediction

This paper presents a novel decision-focused framework integrating the physical energy storage model into machine learning pipelines. Motivated by the model predictive control for energy storage, our end-to-end method incorporates the prior knowledge of the storage model and infers the hidden reward that incentivizes energy storage decisions. This is achieved through a dual-layer framework, combining a prediction layer with an optimization layer. We introduce the perturbation idea into the designed decision-focused loss function to ensure the differentiability over linear storage models, supported by a theoretical analysis of the perturbed loss function. We also develop a hybrid loss function for effective model training. We provide two challenging applications for our proposed framework: energy storage arbitrage, and energy storage behavior prediction. The numerical experiments on real price data demonstrate that our arbitrage approach achieves the highest profit against existing methods. The numerical experiments on synthetic and real-world energy storage data show that our approach achieves the best behavior prediction performance against existing benchmark methods, which shows the effectiveness of our method.
Remaining useful life prediction of rolling bearings based on refined composite multi-scale attention entropy and dispersion entropy
Authors: Yunchong Long, Qinkang Pang, Guangjie Zhu, Junxian Cheng, Xiangshun Li
Date: 2024-06-22 00:00:00
Tasks: Denoising

Remaining useful life (RUL) prediction based on vibration signals is crucial for ensuring the safe operation and effective health management of rotating machinery. Existing studies often extract health indicators (HI) from time domain and frequency domain features to analyze complex vibration signals, but these features may not accurately capture the degradation process. In this study, we propose a degradation feature extraction method called Fusion of Multi-Modal Multi-Scale Entropy (FMME), which utilizes multi-modal Refined Composite Multi-scale Attention Entropy (RCMATE) and Fluctuation Dispersion Entropy (RCMFDE), to solve the problem that the existing degradation features cannot accurately reflect the degradation process. Firstly, the Empirical Mode Decomposition (EMD) is employed to decompose the dual-channel vibration signals of bearings into multiple modals. The main modals are then selected for further analysis. The subsequent step involves the extraction of RCMATE and RCMFDE from each modal, followed by wavelet denoising. Next, a novel metric is proposed to evaluate the quality of degradation features. The attention entropy and dispersion entropy of the optimal scales under different modals are fused using Laplacian Eigenmap (LE) to obtain the health indicators. Finally, RUL prediction is performed through the similarity of health indicators between fault samples and bearings to be predicted. Experimental results demonstrate that the proposed method yields favorable outcomes across diverse operating conditions.