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Thermal Dynamic Embedded Contingency Analysis for Heat-electrical Integrated Energy System

IEEE Transactions on Sustainable Energy 2024年

作者： Guan, Aobo Zhou, Suyang Gu, Wei Lu, Shuai Zhao, Alexis Pengfei Zhang, Xiao-Ping Southeast University School of Electrical Engineering Nanjing210096 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Birmingham School of Electronic Electrical and Computer Engineering BirminghamB15 2TT United Kingdom

To address security challenges in Heat-electrical Integrated Energy Systems (HE-IES), this paper introduces a simulation-based contingency analysis method aimed at identifying potentially threatening faults. We begin by modeling common faults in HE-IES and outlining a comprehensive procedure for simulation-based contingency analysis. Next, we analyze the factors determining temperature drop in the heat medium transfer process and propose a novel analytical-numerical method for thermal dynamic simulation. Real-world experimental results demonstrate that, compared to existing methods, the proposed approach stands out in eliminating numerical dispersion and reducing simulation time by 81.7% while maintaining accuracy, making it particularly effective for HE-IES contingency analysis where solution efficiency is crucial. Subsequently, the proposed contingency analysis method is applied to a 49-node testbed to explore fault propagation mechanism across subsystems. The result reveals that faults originating from the power system and combined heat and power units can cause cascading effects, leading to severe impacts on power supply and heating temperatures. In contrast, faults from the heating system tend to propagate in a less complicated manner but pose a greater risk to the hydraulic system. To mitigate fault propagation, we recommend enhanced monitoring of the operational status of coupling equipment, which plays a critical role in ensuring HE-IES security. © 2010-2012 IEEE.

关键词： Thermal modeling

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FedDA: Faster Framework of Local Adaptive Gradient Methods via Restarted Dual Averaging

arXiv

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arXiv 2023年

作者： Li, Junyi Huang, Feihu Huang, Heng Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh United States College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China

Federated learning (FL) is an emerging learning paradigm to tackle massively distributed data. In Federated Learning, a set of clients jointly perform a machine learning task under the coordination of a server. The FedAvg algorithm is one of the most widely used methods to solve Federated Learning problems. In FedAvg, the learning rate is a constant rather than changing adaptively. The adaptive gradient methods show superior performance over the constant learning rate schedule;however, there is still no general framework to incorporate adaptive gradient methods into the federated setting. In this paper, we propose FedDA, a novel framework for local adaptive gradient methods. The framework adopts a restarted dual averaging technique and is flexible with various gradient estimation methods and adaptive learning rate formulations. In particular, we analyze FedDA-MVR, an instantiation of our framework, and show that it achieves gradient complexity Õ(∊−1.5) and communication complexity Õ(∊−1) for finding a stationary point ∊. This matches the best known rate for first-order FL algorithms and FedDA-MVR is the first adaptive FL algorithm that achieves this rate. We also perform extensive numerical experiments to verify the efficacy of our method. Copyright © 2023, The Authors. All rights reserved.

关键词： Learning algorithms

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A Generative Denoising Approach for Near-Field XL-MIMO Channel Estimation

A Generative Denoising Approach for Near-Field XL-MIMO Chann...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Zhenzhou Jin Li You Derrick Wing Kwan Ng Xiang-Gen Xia Xiqi Gao National Mobile Communications Research Laboratory Southeast University Nanjing China Purple Mountain Laboratories Nanjing China School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia Department of Electrical and Computer Engineering University of Delaware Newark DE USA

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we investigate the near-field (NF) channel estimation (CE) for extremely large-scale multiple-input multiple-output (XL-MIMO) systems. Considering the pronounced NF effects in XL-MIMO communications, we first establish a joint angle-distance (AD) domain-based spherical-wavefront physical channel model that captures the inherent sparsity of XL-MIMO channels in the NF region. Leveraging the sparsity of the channel, the CE is approached as a task of reconstructing sparse signals. Anchored in this framework, we first propose a compressed sensing algorithm to acquire a preliminary channel estimation. Harnessing the powerful latent representation capability of generative artificial intelligence (GenAI), we further propose a GenAI-based approach to refine the estimated channel by employing advanced image denoising techniques. Specifically, we perceive the estimated channel as a noisy color image. Then, we derive the evidence lower bound (ELBO) of the design objective utilizing variational inference and reparameterization techniques, and propose a generative diffusion probabilistic model (GDM) dedicated to denoising. Experimental results indicate that the proposed GDM is capable of offering substantial performance gain in CE compared to existing benchmark approaches in NF XL-MIMO systems.

关键词： Lower bound Generative AI Noise reduction Channel estimation Performance gain MIMO Noise measurement Global communication Image reconstruction Image denoising

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OH-DRL: An AoI-Guaranteed Energy-Efficient Approach for UAV-Assisted IoT Data Collection

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IEEE Transactions on Wireless Communications 2025年第6期24卷 5009-5022页

作者： Yang, Bowen Yu, Yao Hao, Xin Yeoh, Phee Lep Zhang, Junxiong Guo, Lei Li, Yonghui Northeastern University School of Computer Science and Engineering Ministry of Education Northeastern University and Key Laboratory of Intelligent Computing in Medical Image Shenyang110819 China Deakin University School of Information Technology VIC3216 Australia University of the Sunshine Coast School of Science Technology and Engineering Australia Chongqing University of Posts and Telecommunications School of Communication and Information Engineering Chongqing400065 China University of Sydney School of Electrical and Computer Engineering SydneyNSW2006 Australia

In this paper, we propose a hierarchical optimization approach that guarantees the maximum age of information (AoI) for unmanned aerial vehicle (UAV) assisted Internet-of-Things (IoT) data collection. Our model is based on an energy-efficient simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) beamforming model. We formulate the optimization to minimize the UAV flight energy consumption subject to a maximum average AoI threshold by optimizing the UAV trajectory, IoT device scheduling, and STAR-RIS beamforming. To solve this, we develop an optimization-based hierarchical deep reinforcement learning (OH-DRL) algorithm that decomposes the formulated problem into an inter-cluster UAV visiting policy and STAR-RIS-based intra-cluster IoT scheduling policy. In OH-DRL, we jointly optimize the two policies in a high-level loop and a low-level loop, respectively. In the high-level loop, we design an AoI-guided DRL algorithm to determine the AoI-guaranteed UAV hovering position with minimal flight distance. In the low-level loop, a semidefinite relaxation (SDR)-based optimization algorithm further reduces the UAV’s flying time by minimizing the average AoI. Simulation results validate that OH-DRL achieves better convergence performance and energy-saving efficiency across different network scales. Compared to the state-of-the-art DRL algorithm, OH-DRL reduces the UAV flight energy consumption by 14.4% and decreases the number of training episodes required for convergence by 66%. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Hypergraph-based Source Codes for Function Computation Under Maximal Distortion

arXiv

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arXiv 2022年

作者： Basu, Sourya Seo, Daewon Varshney, Lav R. The Coordinated Science Laboratory The Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States The Department of Electrical Engineering and Computer Science DGIST Daegu42988 Korea Republic of

This work investigates functional source coding problems with maximal distortion, motivated by approximate function computation in many modern applications. The maximal distortion treats imprecise reconstruction of a function value as good as perfect computation if it deviates less than a tolerance level, while treating reconstruction that differs by more than that level as a failure. Using a geometric understanding of the maximal distortion, we propose a hypergraph-based source coding scheme for function computation that is constructive in the sense that it gives an explicit procedure for finding optimal or good auxiliary random variables. Moreover, we find that the hypergraph-based coding scheme achieves the optimal rate-distortion function in the setting of coding for computing with side information and achieves the Berger-Tung sum-rate inner bound in the setting of distributed source coding for computing. It also achieves the El Gamal-Cover inner bound for multiple description coding for computing and is optimal for successive refinement and cascade multiple description problems for computing. Lastly, the benefit of complexity reduction of finding a forward test channel is shown for a class of Markov sources. Copyright © 2022, The Authors. All rights reserved.

关键词： Signal distortion

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Control of Chirality and Directionality of Nonlinear Metasurface Light Source via Moiré engineering

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Physical Review Letters 2025年第4期134卷 043801-043801页

作者： Huanyu Zhou Xueqi Ni Beicheng Lou Shanhui Fan Yuan Cao Haoning Tang Department of Electrical Engineering and Computer Science University of California at Berkeley Berkeley California 94720 USA Department of Physics National University of Singapore Singapore 119077 Department of Applied Physics and Ginzton Laboratory Stanford University Stanford California 94305 USA School of Engineering and Applied Sciences Harvard University Cambridge Massachusetts 02138 USA

Metasurfaces have revolutionized nonlinear and quantum light manipulation in the past decade, enabling the design of materials that can tune polarization, frequency, and direction of light simultaneously. However, tuning of metasurfaces is traditionally achieved by changing their microscopic structure, which does not allow in situ tuning and dynamic optimization of the metasurfaces. In this Letter, we explore the concept of twisted bilayer and tetralayer nonlinear metasurfaces, which offer rich tunability in its effective nonlinear susceptibilities. Using gold-based metasurfaces, we demonstrate that a number of different singularities of nonlinear susceptibilities can exist in the parameter space of twist angle and interlayer gap between different twisted layers. At the singularities, reflected or transmitted light from the nonlinear process (such as second-harmonic generation) can either become circularly polarized (for C points), or entirely vanish (for V points). By further breaking symmetries of the system, we can independently tune all aspects of the reflected and transmitted nonlinear emission, achieving unidirectional emission with full-Poincaré polarization tunability, a dark state (V−V point), or any other bidirectional output. Our work paves the way for multidimensional control of polarization and directionality in nonlinear light sources, opening new avenues in ultrafast optics, optical communication, sensing, and quantum optics.

关键词： Quantum optics

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Fault Arc Detection Method Based on Multi Feature Analysis and PNN 1

引用

3rd International Conference on Neural Computing for Advanced Applications, NCAA 2022

作者： Li, Chao Shi, Jiachuan Ma, Jiankai Fu, Rao Zhao, Jin Shandong Key Laboratory of Intelligent Buildings Technology School of Information and Electrical Engineering Shandong Jianzhu University Jinan250101 China Shandong Taishan Pumped Storage Power Station Co. Ltd. Taian271000 China Department Electrical and Computer Engineering The University of Alabama TuscaloosaAL35487 United States

ISBN: (数字)9789811961359

ISBN: (纸本)9789811961342

In order to accurately and timely identify the low-voltage DC fault arcs and make protection actions, this paper proposes a kind of fault arc detection method based on multi-feature analysis and PNN. Through the multi-dimensional feature analysis of the electromagnetic waves of low-voltage DC series arc fault, the effective characteristic quantity is selected to form the eigenvector group to express the fault arc information. In terms of fault detection, a low-voltage DC fault arc diagnosis algorithm based on PNN is used to identify eigenvectors. The experimental results show that the method can effectively detect DC arc fault and improve the detection accuracy, which is of great significance to protect the safe operation of low voltage DC systems. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Eigenvalues and eigenfunctions

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Bayesian Neural Networks for Direct Short-Term Net Load Forecasting in Solar-Integrated Residential Buildings

Bayesian Neural Networks for Direct Short-Term Net Load Fore...

引用

International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Georgios Tziolis Andreas Livera Javier Lopez-Lorente Panagiotis Herodotou George Makrides George E. Georghiou Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus DNV Arnhem Netherlands

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Short-term net load forecasting (STNLF) is of utmost importance for the efficient operation and control of residential buildings, with integrated photovoltaic (PV) systems. The intermittent PV generation increases the uncertainty of STNLF and therefore the error. This study presents a machine learning model using Bayesian neural networks (BNNs) for direct STNLF in solar-integrated residential buildings. Historical data from PV-integrated households in Cyprus, with self-sufficiency between 88.59% and 294.12%, were used for assessing the performance of the BNN model. The proposed model was applied to data from 68 individual and aggregated households in Cyprus. The results indicated that the machine learning model exhibited normalized root mean square error of 7.20% for the aggregated data, rendering it suitable for STNLF in residential buildings. Finally, the model was compared to a naïve persistence model, achieving a skill score of 46.37% at the aggregated household level.

关键词： Accuracy Uncertainty Load forecasting Buildings Neural networks Machine learning Predictive models Data models Bayes methods Load modeling

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Joint Interference Cancellation with Imperfect CSI

Joint Interference Cancellation with Imperfect CSI

引用

MILCOM, Military Communications Conference

作者： Zhibin Zou Xue Wei Xin Tian Genshe Chen Aveek Dutta Khanh Pham Erik Blasch Intelligent Fusion Technology Inc. Germantown MD USA Department of Electrical and Computer Engineering SUNY University at Albany NY USA Air Force Research Laboratory Arlington VA USA

ISBN: (数字)9798350374230

ISBN: (纸本)9798350374247

Precoding and Modulation techniques are widely investigated to mitigate interference in the space, time, and frequency domains, respectively. However, next-generation (xG) channels are increasingly dense and mobile, making these domains highly correlated and resulting in high-dimensional channel tensors. Separate interference cancellation methods treating the channel as independent 2-D matrices fail to cancel the joint interference across multiple Degrees of Freedom (DoF). High Order Generalized Mercer’s Theorem (HOGMT) based precoding and Multidimensional Eigenwave Multiplexing (MEM) modulation have been proposed to cancel the joint interference by leveraging the jointly orthogonal eigencomponents of the channel. However, these methods rely heavily on perfect Channel State Information (CSI), and their performance degrades with imperfect CSI. In this paper, we propose a joint interference cancellation method based on the Convolutional Neural Network (CNN) with a single linear kernel (JIC-CLK). We show that a CNN with a single linear kernel is equivalent to a wireless system, where the linear kernel itself is identical to the transmitted signal with the CSI as input. By training the output to approach the desired signal at the receiver with MSE loss, the kernel converges to the optimal transmitted signal in the MMSE sense. The proposed method adapts to both perfect and imperfect CSI and can be extended to high-dimensional channels. The accuracy and generality of the proposed method are validated by simulations in three cases: 1) Perfect CSI, 2) Imperfect CSI, and 3) 4-D spatio-temporal channels with imperfect CSI.

关键词： Wireless communication Micromechanical devices Training Interference cancellation Tensors Precoding Modulation Convolutional neural networks Kernel Signal to noise ratio

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Tracking of Multiple Spawning Targets with Heterogeneous Sensors for Seabed-To-Space Situational Awareness

Tracking of Multiple Spawning Targets with Heterogeneous Sen...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Domenico Gaglione Leonardo M. Millefiori Paolo Braca Peter Willett Moe Z. Win NATO STO-CMRE La Spezia Italy Electrical and Computer Engineering Dep. University of Connecticut Storrs CT USA Laboratory for Information and Decision Systems MIT Cambridge MA USA

Seabed-to-space situational awareness (S3A) aims to organize, fuse, and synthesize the massive volume of information collected from heterogeneous sensors, i.e., underwater, terrestrial, and space-based sensors, and therefrom extract knowledge thence available to defence operators, enabling informed decision-making. Heterogeneous sensors provide observations of targets in different domains (e.g., air, water surface, undersea), and with different modalities, perspectives, latencies, and update rates. They complement each other, and an effective approach is needed to combine the data they generate. This paper introduces a comprehensive framework for multi-target tracking based on the sum-product algorithm (SPA) that models the different characteristics of the sensors and handles the appearance of targets in a complex multi-domain environment both through spontaneous births and through spawning from existing targets. The efficacy of this approach is demonstrated through a simulated maritime scenario informed by real-world observation streams.

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