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A Data-Driven Adaptive Control Approach for Enhancing the Dynamic Response of VSGs in Varying Grid Conditions

IEEE Transactions on Power Delivery 2025年第3期40卷 1421-1433页

作者： Fahad, Shah She, Buxin Yin, Junjie Li, Fangxing Cui, Hantao Bo, Rui Department of Electrical and Computer Engineering Missouri University of Science and Technology Rolla MO USA Senior Power Systems Engineer ETAP Irvine CA USA Optimization & Control Group Parcific Northwest National Laboratory Richland USA Department of Electrical Engineering and Computer Science University of Tennessee at Knoxville Knoxville TN USA Department of Electrical and Computer Engineering North Carolina State University Raleigh NC USA

Conventionally, a virtual synchronous generator (VSG) is designed for islanded mode (IM) operation to meet specific operational requirements such as the rate of change of frequency (RoCoF). However, the operation of VSG designed for IM may not meet the operational and control criteria in grid connected mode (GCM) when the grid conditions vary. In addition, conventional VSG control technology does not consider the influence of the presynchronization scheme when connected to a weak grid, which degrades the RoCoF in IM. To overcome the aforementioned challenges, the proposed study presents a twin-delayed deep deterministic policy gradient (TD3) algorithm to improve the seamless transition performance of VSG from IM to GCM and vice versa. In the first step, the VSG-based power system model is used as a foundation for problem formulation of the proposed TD3 algorithm. Secondly, a reward function is designed according to the performance requirements, i.e., frequency and RoCoF requirements, of the VSG in order to guide the training of the agent in varying load, power reference, and grid conditions. Finally, the superiority of the proposed algorithm over existing methods is validated in MATLAB/SIMULINK and RTDS based real-time simulation environment. © 1986-2012 IEEE.

关键词： Deep reinforcement learning

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Distributed Attention-Enabled Multi-Agent Reinforcement Learning Based Frequency Regulation of Power systems

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IEEE Transactions on Power systems 2025年第3期40卷 2427-2437页

作者： Zhao, Yunzheng Liu, Tao Hill, David J. University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong Hong Kong Monash University Department of Electrical and Computer Systems Engineering ClaytonVIC3800 Australia

This paper develops a new distributed attention-enabled multi-agent reinforcement learning method for frequency regulation of power systems. Specifically, the controller of each generator is modelled as an agent, and the reward and observation are designed based on the characteristics of power systems. All the agents learn their own control policies in the offline training phase and generate frequency control signals in the online execution phase. The target of the proposed algorithm is to conduct both offline training and online frequency control in a distributed way. To achieve this goal, two distributed information-sharing mechanisms are proposed based on the different global information to be discovered. First, a consensus-based reward-sharing mechanism is designed to estimate the globally averaged reward. Second, a distributed observation-sharing scheme is developed to discover the global observation information. Furthermore, the attention strategy is embedded in the observation-sharing scheme to help agents adaptively adjust the importance of observations from different neighbors. With these two mechanisms, a new distributed attention-enabled proximal policy optimization (DAPPO) based method is proposed to achieve model-free frequency control. Simulation results on the IEEE 39-bus system and the NPCC 140-bus system demonstrate that the proposed DAPPO achieves stable offline training and effective online frequency control. © 1969-2012 IEEE.

关键词： Reinforcement learning

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Generalized Regenerating Codes and Node Repair on Graphs

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IEEE Transactions on Information Theory 2025年第3期71卷 1613-1630页

作者： Patra, Adway Barg, Alexander University of Maryland Department of Electrical and Computer Engineering The Institute for Systems Research College ParkMD20742 United States

We consider regenerating codes in distributed storage systems where connections between the nodes are constrained by a graph. In this problem, the failed node downloads the information stored at a subset of vertices of the graph for the purpose of recovering the lost data. Compared to the standard setting, regenerating codes on graphs address two additional features. The repair information is moved across the network, and the cost of node repair is determined by the graphical distance from the helper nodes to the failed node. Accordingly, the helpers far away from the failed node may be expected to contribute less data for repair than the nodes in the neighborhood of that node. We analyze regenerating codes with nonuniform download for repair on graphs. Moreover, in the process of repair, the information moved from the helpers to the failed node may be combined through intermediate processing, reducing the repair bandwidth. We derive lower bounds for communication complexity of node repair on graphs, including repair schemes with nonuniform download and intermediate processing, and construct codes that attain these bounds. Additionally, some of the nodes may act as adversaries, introducing errors into the data moved in the network. For repair on graphs in the presence of adversarial nodes, we construct codes that support node repair and error correction in systematic nodes. © 1963-2012 IEEE.

关键词： Maintenance engineering Codes Costs Network coding IP networks Bandwidth Symbols Encoding Vectors Standards

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Real-Time Coordinated Operation of Electric Vehicle Fast Charging Stations With Energy Storage: An Efficient Spatiotemporal Decomposition Approach

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IEEE Transactions on Smart Grid 2025年第3期16卷 2464-2477页

作者： Zhu, Zhen Zhang, Hongcai University of Macau State Key Laboratory of Internet of Things for Smart City Department of Electrical and Computer Engineering China

Fast charging stations (FCSs) have been widely adopted to meet the increasing charging demands of electric vehicles. The intermittent and impulsive nature of fast charging might significantly deteriorate the safe and efficient operation of the distribution power grid. Integrating battery energy storage systems (BES) in FCSs presents a promising option to mitigate these challenges. However, it is nontrivial to effectively coordinate multiple BES-equipped FCSs due to the highly stochastic charging demand and the spatio-temporal coupling nature of FCS operation. To address these challenges, this paper proposes a two-layer approach for real-time stochastic scheduling of multiple BES-equipped FCSs in a distribution grid. In the upper layer, we propose a computationally efficient dynamic programming method to determine the total power of all BESs at FCSs based on observed real-time fast charging loads and electricity price. Specifically, we derive analytical expressions for efficient offline training and online scheduling of the dynamic programming problem. This approach allows for direct training of value functions without iterative updating and obtaining scheduling decisions without redundant calculations. In the lower layer, we design a consensus-based power allocation strategy to coordinate power dispatch among individual FCSs following the reference power determined in the upper layer. In this way, real-time responses for each BES-equipped FCS can be given sequentially and distributedly. The superiority of the proposed method is validated via numerical simulations in comparison with state-of-the-art benchmarks. © 2010-2012 IEEE.

关键词： Charging stations

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Orbit-Orbit Interaction in Spatiotemporal Optical Vortex

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engineering 2025年第2期45卷 44-51页

作者： Jian Chen Jie Zhao Xi Shen Dewei Mo Cheng-Wei Qiu Qiwen Zhan School of Optical-Electrical and Computer Engineering University of Shanghai for Science and TechnologyShanghai 200093China Shanghai Key Laboratory of Modern Optical System University of Shanghai for Science and TechnologyShanghai 200093China Department of Electrical and Computer Engineering National University of SingaporeSingapore 117583Singapore

While spin-orbit interaction has been extensively studied,few investigations have reported on the interaction between orbital angular momenta(OAMs).In this work,we study a new type of orbit-orbit coupling between the longitudinal OAM and the transverse OAM carried by a three-dimensional(3D)spatiotemporal optical vortex(STOV)in the process of tight *** 3D STOV possesses orthogonal OAMs in the x-y,t-x,and y-t planes,and is preconditioned to overcome the spatiotemporal astigmatism effect.x,y,and t are the axes in the spatiotemporal *** corresponding focused wavepacket is calculated by employing the Debye diffraction theory,showing that a phase singularity ring is generated by the interactions among the transverse and longitudinal vortices in the highly confined *** Fourier-transform decomposition of the Debye integral is employed to analyze the mechanism of the orbit-orbit *** is the first revelation of coupling between the longitudinal OAM and the transverse OAM,paving the way for potential applications in optical trapping,laser machining,nonlinear light-matter interactions,and more.

关键词： Spatiotemporal optical vortex Orbital angular momentum Momentum interaction Highly confined wavepacket Diffraction

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A New Approach to Determine Feasible Operating Region of Unbalanced Distribution Networks with Distributed Photovoltaics

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IEEE Transactions on Power Delivery 2025年第3期40卷 1493-1504页

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai India Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distribution network. The feasible operating region of an active distribution network is constituted by its range of active-reactive power exchanges with the bulk power system without breaching network constraints. This study explores the potential of highly penetrated single- and three-phase distributed photovoltaics and voltage-sensitive loads to determine the feasible operating region at different operating points. The cumulative response of Volt-Var controlled distributed photovoltaics and voltage-sensitive loads determines the power flow at the bulk power system-active distribution network interconnection, ensuring feasibility with respect to voltage magnitude and unbalance constraints. Further, the correspondence of feasible operating region with the tap-settings of substation transformer enables a novel feature to traverse throughout the available operating region and facilitate bulk power system-active distribution network power coordination. Additionally, a nodal sensitivity-based adjustable Volt-Var control scheme is proposed for distributed photovoltaics, improving the feasible operating region. The proposed strategy is validated on bulk power systemactive distribution network integrated test system. Bulk power system: IEEE-9 bus system-Active distribution network: (highly unbalanced IEEE-13 bus distribution system (medium voltage) + modified CIGRE low voltage system). © 1986-2012 IEEE.

关键词： Power distribution networks

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A survey on cross-user federated recommendation

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Science China(Information Sciences) 2025年第4期68卷 7-32页

作者： Enyue YANG Yudi XIONG Wei YUAN Weike PAN Qiang YANG Zhong MING College of Computer Science and Software Engineering Shenzhen University School of Electrical Engineering and Computer Science The University of Queensland WeBank AI Lab WeBank Department of Computer Science and Engineering Hong Kong University of Science and Technology College of Big Data and Internet Shenzhen Technology University Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)

Recommender systems are effective in mitigating information overload, yet the centralized storage of user data raises significant privacy concerns. Cross-user federated recommendation(CUFR) provides a promising distributed paradigm to address these concerns by enabling privacy-preserving recommendations directly on user devices. In this survey, we review and categorize current progress in CUFR, focusing on four key aspects: privacy, security, accuracy, and efficiency. Firstly,we conduct an in-depth privacy analysis, discuss various cases of privacy leakage, and then review recent methods for privacy protection. Secondly, we analyze security concerns and review recent methods for untargeted and targeted *** untargeted attack methods, we categorize them into data poisoning attack methods and parameter poisoning attack methods. For targeted attack methods, we categorize them into user-based methods and item-based methods. Thirdly,we provide an overview of the federated variants of some representative methods, and then review the recent methods for improving accuracy from two categories: data heterogeneity and high-order information. Fourthly, we review recent methods for improving training efficiency from two categories: client sampling and model compression. Finally, we conclude this survey and explore some potential future research topics in CUFR.

关键词： cross-user federated recommendation federated recommendation federated learning recommender systems user privacy

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Adversarial machine learning: a review of methods, tools, and critical industry sectors

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Artificial Intelligence Review 2025年第8期58卷 1-87页

作者： Pelekis, Sotiris Koutroubas, Thanos Blika, Afroditi Berdelis, Anastasis Karakolis, Evangelos Ntanos, Christos Spiliotis, Evangelos Askounis, Dimitris Decision Support Systems Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Superbo AI Athens Greece

The rapid advancement of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), has produced high-performance models widely used in various applications, ranging from image recognition and chatbots to autonomous driving and smart grid systems. However, security threats arise from the vulnerabilities of ML models to adversarial attacks and data poisoning, posing risks such as system malfunctions and decision errors. Meanwhile, data privacy concerns arise, especially with personal data being used in model training, which can lead to data breaches. This paper surveys the Adversarial Machine Learning (AML) landscape in modern AI systems, while focusing on the dual aspects of robustness and privacy. Initially, we explore adversarial attacks and defenses using comprehensive taxonomies. Subsequently, we investigate robustness benchmarks alongside open-source AML technologies and software tools that ML system stakeholders can use to develop robust AI systems. Lastly, we delve into the landscape of AML in four industry fields –automotive, digital healthcare, electrical power and energy systems (EPES), and Large Language Model (LLM)-based Natural Language Processing (NLP) systems– analyzing attacks, defenses, and evaluation concepts, thereby offering a holistic view of the modern AI-reliant industry and promoting enhanced ML robustness and privacy preservation in the future. © The Author(s) 2025.

关键词： Deep learning

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Minimizing Age of Information in UAV-Assisted Edge Computing System with Multiple Transmission Modes

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1060-1078页

作者： Yanhua Pei Yunzhi Zhao Fen Hou State Key Laboratory of IoT for Smart City and Department of Electrical and Computer Engineering University of MacauMacau 999078China

With the advance of 5G technologies and the development of space-air-ground-sea applications,the fast and efficient collection and processing of the explosive growth of sensing data have become significant and *** this paper,considering the Age of Information(AoI),the limited coverage of Base Stations(BS),and the constrained computation capability of Unmanned Aerial Vehicle(UAV),we propose a hybrid communication framework that utilizes UAVs as relays to optimize the collection of sensing *** aim to minimize the average AoI of the data among all sensor nodes while considering the energy consumption constraints of sensor nodes,which is formulated as a Mixed Integer NonLinear Programming(MINLP).To address this problem,we decompose it into communication resource allocation and computation resource ***,the average AoI of the whole system is minimized and the average energy consumption constraint of sensor nodes is *** simulation results show that our proposed method can achieve significant performance *** specific,our proposed method can reduce the average AoI by 20％,11％,and 43％compared to the three counterparts,Data Transmission Directly Algorithm(DTDA),Max Weight Algorithm(MWA),and matching algorithm,respectively.

关键词： edge computing hybrid relay Unmanned Aerial Vehicle(UAV)assisted Age of Information(AoI)

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Reliable Task Offloading for 6G-Based IoT Applications

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computers, Materials & Continua 2025年第2期82卷 2255-2274页

作者： Usman Mahmood Malik Muhammad Awais Javed Ahmad Naseem Alvi Mohammed Alkhathami Department of Computer Software Engineering National University of Science and Technology(NUST)Islamabad44000Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadIslamabad45550Pakistan Information Systems Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia

Fog computing is a key enabling technology of 6G systems as it provides quick and reliable computing,and data storage services which are required for several 6G *** Intelligence(AI)algorithms will be an integral part of 6G systems and efficient task offloading techniques using fog computing will improve their performance and *** this paper,the focus is on the scenario of Partial Offloading of a Task to Multiple Helpers(POMH)in which larger tasks are divided into smaller subtasks and processed in parallel,hence expediting task ***,using POMH presents challenges such as breaking tasks into subtasks and scaling these subtasks based on many interdependent factors to ensure that all subtasks of a task finish simultaneously,preventing resource ***,applying matching theory to POMH scenarios results in dynamic preference profiles of helping devices due to changing subtask sizes,resulting in a difficult-to-solve,externalities *** paper introduces a novel many-to-one matching-based algorithm,designed to address the externalities problem and optimize resource allocation within POMH ***,we propose a new time-efficient preference profiling technique that further enhances time optimization in POMH *** performance of the proposed technique is thoroughly evaluated in comparison to alternate baseline schemes,revealing many advantages of the proposed *** simulation findings indisputably show that the proposed matching-based offloading technique outperforms existing methodologies in the literature,yielding a remarkable 52 reduction in task latency,particularly under high workloads.

关键词： 6G IoT task offloading fog computing

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