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Multi-Period Resilient Model for VSC-Based AC-DC HDS Considering Public-Safety Power Shutoff to Mitigate Wildfires

CSEE Journal of Power and Energy Systems 2024年第2期10卷 821-833页

作者： Zekai Wang Tao Ding Xiaosheng Zhang Chenggang Mu Pengwei Du Fangxing Li School of Electrical Engineering Xi'an Jiaotong UniversityXi'an 710049China Electric Reliability Council of Texas AustinTX76574 USA Department of Electrical Engineering and Computer Science The University of TennesseeKnoxville(UTK)TN37996USA

This paper proposes a voltage source converter (VSC) -based AC-DC hybrid distribution system (HDS) resilient model to mitigate power outages caused by wildfires. Before a wildfire happens, the public-safety power shutoff (PSPS) strategy is applied to actively cut some vulnerable lines which may easily cause wildfires, and reinforce some lines that are connected to critical loads. To mitigate load shedding caused by active line disconnection in the PSPS strategy, network reconfiguration is applied before the wildfire occurrence. During the restoration period, repair crews (RCs) repair faulted lines, and network reconfiguration is also taken into consideration in the recovery strategy to pick up critical loads. Since there exists possible errors in the wildfire prediction, several different scenarios of wildfire occurrence have been taken into consideration, leading to the proposition of a stochastic multi-period resilient model for the VSC-based AC-DC HDS. To accelerate the computational performance, a progressive hedging algorithm has been applied to solve the stochastic model which can be written as a mixed-integer linear program. The proposed model is verified on a 106-bus AC-DC HDS under wildfire conditions, and the result shows the proposed model not only can improve the system resilience but also accelerate computational speed.

关键词： AC-DC hybrid distribution system restoration network reconfiguration progressive hedging public safety power shutoffs strategy resilience

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Transforming the electrical grid: the role of AI in advancing smart, sustainable, and secure energy systems

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Energy Informatics 2025年第1期8卷 1-43页

作者： Rajaperumal, T.A. Columbus, C. Christopher School of Electrical Engineering Vellore Institute of Technology Tamil Nadu Chennai India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Chennai India

The evolution of the electrical grid from its early centralized structure to today’s advanced "smart grid" reflects significant technological progress. Early grids, designed for simple power delivery from large plants to consumers, faced challenges in efficiency, reliability, and scalability. Over time, the grid has transformed into a decentralized network driven by innovative technologies, particularly artificial intelligence (AI). AI has become instrumental in enhancing efficiency, security, and resilience by enabling real-time data analysis, predictive maintenance, demand-response optimization, and automated fault detection, thereby improving overall operational efficiency. This paper examines the evolution of the electrical grid, tracing its transition from early limitations to the methodologies adopted in present smart grids for addressing those challenges. Current smart grids leverage AI to optimize energy management, predict faults, and seamlessly integrate electric vehicles (EVs), reducing transmission losses and improving performance. However, these advancements are not without limitations. Present grids remain vulnerable to cyberattacks, necessitating the adoption of more robust methodologies and advanced technologies for future grids. Looking forward, emerging technologies such as Digital Twin (DT) models, the Internet of Energy (IoE), and decentralized grid management are set to redefine grid architectures. These advanced technologies enable real-time simulations, adaptive control, and enhanced human–machine collaboration, supporting dynamic energy distribution and proactive risk management. Integrating AI with advanced energy storage, renewable resources, and adaptive access control mechanisms will ensure future grids are resilient, sustainable, and responsive to growing energy demands. This study emphasizes AI’s transformative role in addressing the challenges of the early grid, enhancing the capabilities of the present smart grid, and shaping a secure

关键词： Risk management

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Nanocryotron ripple counter integrated with a superconducting nanowire single-photon detector for megapixel arrays

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Physical Review Applied 2024年第2期22卷 024020-024020页

作者： Matteo Castellani Owen Medeiros Reed A. Foster Alessandro Buzzi Marco Colangelo Joshua C. Bienfang Alessandro Restelli Karl K. Berggren Electrical Engineering and Computer Science

Decreasing the number of cables that bring heat into the cryostat is a critical issue for all cryoelectronic devices. In particular, arrays of superconducting nanowire single-photon detectors (SNSPDs) could require more than 106 readout lines. Performing signal-processing operations at low temperatures could be a solution. Nanocryotrons, superconducting nanowire three-terminal devices, are good candidates for integrating sensing and electronics on the same technological platform as SNSPDs in photon-counting applications. In this work, we demonstrate that it is possible to read out, process, encode, and store the output of SNSPDs using exclusively superconducting nanowires patterned on niobium nitride thin films. In particular, we present the design and development of a nanocryotron ripple counter that detects input voltage spikes and converts the number of pulses to an N-digit value. The counting base can be tuned from 2 to higher values, enabling higher maximum counts without enlarging the circuit. As a proof of principle, we first experimentally demonstrate the building block of the counter, an integer-N frequency divider with N ranging from 2 to 5. Then, we demonstrate photon-counting operations at 405 nm and 1550 nm by coupling an SNSPD with a two-digit nanocryotron counter partially integrated on chip. The two-digit counter can operate in either base 2 or base 3, with a bit-error rate lower than 2×10−4 and a count rate of 107s−1. We simulate circuit architectures for integrated readout of the counter state and we evaluate the capabilities of reading out an SNSPD megapixel array that would collect up to 1012 counts per second. The results of this work, combined with our recent publications on a nanocryotron shift register and logic gates, pave the way for the development of nanocryotron processors, from which multiple superconducting platforms may benefit.

关键词： Cryogenic technology Optoelectronics Devices for digital logic, storage & processing Nanowires Optical sources & detectors Superconducting devices Microwave techniques Photon counting Single-photon detectors

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Sustainable Forest Fire detection: A Mapping through Bibliometrics and Scientific Knowledge Research 4

Sustainable Forest Fire detection: A Mapping through Bibliom...

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4th International Conference on electrical engineering and computer science, ICECOS 2024

作者： Exaudi, Kemahyanto Stiawan, Deris Suprapto, Bhakti Yudho Universitas Sriwijaya Faculty of Computer Science & Faculty of Engineering Palembang Indonesia Universitas Sriwijaya Faculty of Computer Science Departement of Computer Science Palembang Indonesia Universitas Sriwijaya Faculty of Engineering Departement of Electrical Engineering Palembang Indonesia

ISBN: (纸本)9798350368253

The increasing incidence of forest and land fires has become an urgent global concern in recent decades. In an effort to improve detection and understanding of forest fires, information technology-based approaches have become a major focus. This study explores the sustainability of forest and land fire detection through the perspective of scientific knowledge and bibliometric analysis to determine current developments and the latest insights in research. This study integrates open data with network-based visualization to present the latest developments in the scientific literature about forest fire detection. This article identified research trends, collaboration patterns, and contributions to relevant knowledge. The results highlight the evolution and technological trends in forest fire detection approaches as well as a focal point for future research. This approach provides valuable insights for researchers, practitioners and decision makers in efforts to mitigate and handle forest fires earlier and more effectively. © 2024 IEEE.

关键词： Forest ecology

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Data Augmentation and Random Multi-Model Deep Learning for Data Classification

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computers, Materials & Continua 2023年第3期74卷 5191-5207页

作者： Fatma Harby Adel Thaljaoui Durre Nayab Suliman Aladhadh Salim EL Khediri Rehan Ullah Khan Computer Science Department Future Academy-Higher Future Institute for Specialized Technological StudiesEgypt Department of Computer Systems Engineering Faculty of Electrical and Computer EngineeringUniversity of Engineering and TechnologyPeshawar25120Pakistan Department of Information Technology College of ComputerQassim UniversityBuraydahSaudi Arabia Department of Computer Sciences Faculty of Sciences of GafsaUniversity of GafsaGafsaTunisia

In the machine learning(ML)paradigm,data augmentation serves as a regularization approach for creating ML *** increase in the diversification of training samples increases the generalization capabilities,which enhances the prediction performance of classifiers when tested on unseen *** learning(DL)models have a lot of parameters,and they frequently ***,to avoid overfitting,data plays a major role to augment the latest improvements in ***,reliable data collection is a major limiting ***,this problem is undertaken by combining augmentation of data,transfer learning,dropout,and methods of normalization in *** this paper,we introduce the application of data augmentation in the field of image classification using Random Multi-model Deep Learning(RMDL)which uses the association approaches of multi-DL to yield random models for *** present a methodology for using Generative Adversarial Networks(GANs)to generate images for data *** experiments,we discover that samples generated by GANs when fed into RMDL improve both accuracy and model *** across both MNIST and CIAFAR-10 datasets show that,error rate with proposed approach has been decreased with different random models.

关键词： Data augmentation generative adversarial networks classification machine learning random multi-model deep learning

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Grid-Interactive Novel Resilient Control of Solar PVWind-Battery Storage Microgrid Under Distorted and Unbalanced Grid Voltages

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 1744-1757页

作者： Gali, Vijayakumar Jamwal, Prashant Kumar Gupta, Nitin Babu, B. Chitti Ahmadi, Mohammad Jawid Morey, Meghraj Sudhakar University of Hertfordshire School of Physics Engineering and Computer Science Department of Electrical and Electronics Engineering HatfieldAL109AB United Kingdom Nazarbayev University Department of Electrical and Computer Engineering Astana Kazakhstan Malaviya National Institute of Technology Department of Electrical Engineering Jaipur India Indian Institute of Information Technology Design and Manufacturing Department of Electronics and Communication Kancheepuram India

Integrating solar photovoltaic (PV), wind, and battery storage (BS) systems into the grid introduces significant power quality (PQ) challenges. In particular, the intermittent nature of solar PV and wind energy systems (WES), combined with nonlinear loads, can lead to grid instability. As a result, maintaining a reliable and high-quality power supply to consumers becomes a substantial challenge. This article presents two novel solutions to address these challenges: the anti-windup mixed-order generalized integrator (AWMOGI) and the adaptive delay operation period filter (ADOPF). The AWMOGI effectively extracts fundamental components from distorted utility grid voltages, minimizing DC offset and reducing steady-state oscillations. Meanwhile, the ADOPF swiftly captures positive sequence voltage components by introducing a two-sampling-interval delay in the d-q coordinate system. The hierarchical-loop controller parameters are optimized using a customized Harris Hawks Optimization (HHO) algorithm, which enhances error reduction and facilitates rapid establishment of reference values for DC-link voltage loop control. An adaptive dynamic power management scheme also enables the microgrid (MG) to manage surplus power from MG sources, reducing stress on the BS and ensuring DC-link voltage stability. The experimental results demonstrate significant improvements in handling utility grid abnormalities, dynamic loads, and intermittent microgrid source operation. The experimental results show a zero DC-off set and steady-state errors, a fast computational time of 35e-5s, and the THD of grid currents of 3.16%, which complies with the IEEE 519 standards. © 1975-2011 IEEE.

关键词： Adaptive filters

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A color image encryption scheme based on a 2D coupled chaotic system and diagonal scrambling algorithm

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Chinese Physics B 2024年第7期33卷 233-243页

作者：苏静明方士辉洪炎温言 Anhui Mining Machinery and Electrical Equipment Coordination Innovation Center Anhui University of Science and TechnologyHuainan 232001China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China

A novel color image encryption scheme is developed to enhance the security of encryption without increasing the complexity. Firstly, the plain color image is decomposed into three grayscale plain images, which are converted into the frequency domain coefficient matrices(FDCM) with discrete cosine transform(DCT) operation. After that, a twodimensional(2D) coupled chaotic system is developed and used to generate one group of embedded matrices and another group of encryption matrices, respectively. The embedded matrices are integrated with the FDCM to fulfill the frequency domain encryption, and then the inverse DCT processing is implemented to recover the spatial domain signal. Eventually,under the function of the encryption matrices and the proposed diagonal scrambling algorithm, the final color ciphertext is obtained. The experimental results show that the proposed method can not only ensure efficient encryption but also satisfy various sizes of image encryption. Besides, it has better performance than other similar techniques in statistical feature analysis, such as key space, key sensitivity, anti-differential attack, information entropy, noise attack, etc.

关键词： color image encryption discrete cosine transform two-dimensional(2D)coupled chaotic system diagonal scrambling

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Highly Efficient Three-Level AC-AC Converter with Identical In-Phase and Antiphase Buck-Boost Operations

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IEEE Transactions on Power Electronics 2024年第8期39卷 9929-9942页

作者： Ahmed, Hafiz Furqan Khan, Ashraf Ali Al Zaabi, Omar Mousavi, Seyyed Mohammad Javad Babaei, Ebrahim National Sun Yat-Sen University Department of Electrical Engineering Kaohsiung804 Taiwan Memorial University of Newfoundland Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science St. John'sNLA1C 5S7 Canada Khalifa University Advanced Power and Energy Center Department of Electrical Engineering and Computer Science Abu Dhabi127788 United Arab Emirates University of Tabriz Faculty of Electrical and Computer Engineering Tabriz51666 Iran Near East University Engineering Faculty Nicosia99138 Turkey

In this article, a new coupled-inductors based three-level bipolar buck-boost ac-ac converter is proposed. The proposed converter can produce highly efficient and symmetric in-phase and antiphase buck and boost modes of operation with much lower component voltage and current stresses. This reduces the operation and design complexity, and helps to achieve a high-efficiency operation. The high-frequency modulating switches of the proposed converter are implemented with coupled-inductor-based dual-buck phase leg, producing three-level input and output voltages, and providing inherent protection from voltage-source short-circuit issue during switch transitions. The other salient features of the proposed converter are no-commutation issue, no need for RC snubbers or dedicated safe-commutation algorithms, provision of high-quality and continuous input/output currents, and support for nonresistive loads. An in-depth circuit analysis of the proposed converter is provided based on the proposed switch modulation strategies. The guidelines for component design/selection are discussed, followed by the comparisons with state-of-the-art three-level ac-ac converters. Finally, practical circuit verifications are performed on a laboratory-assembled prototype. © 1986-2012 IEEE.

关键词： Buck-Boost converter

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Orbital angular momentum-mediated machine learning for high-accuracy mode-feature encoding

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Light(science & Applications) 2024年第3期13卷 466-477页

作者： Xinyuan Fang Xiaonan Hu Baoli Li Hang Su Ke Cheng Haitao Luan Min Gu Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghai 200093China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghai 200093China

Machine learning with optical neural networks has featured unique advantages of the information processing including high speed,ultrawide bandwidths and low energy consumption because the optical dimensions(time,space,wavelength,and polarization)could be utilized to increase the degree of ***,due to the lack of the capability to extract the information features in the orbital angular momentum(OAM)domain,the theoretically unlimited OAM states have never been exploited to represent the signal of the input/output nodes in the neural network ***,we demonstrate OAM-mediated machine learning with an all-optical convolutional neural network(CNN)based on Laguerre-Gaussian(LG)beam modes with diverse diffraction *** proposed CNN architecture is composed of a trainable OAM mode-dispersion impulse as a convolutional kernel for feature extraction,and deep-learning diffractive layers as a *** resultant OAM mode-dispersion selectivity can be applied in information mode-feature encoding,leading to an accuracy as high as 97.2%for MNIST database through detecting the energy weighting coefficients of the encoded OAM modes,as well as a resistance to eavesdropping in point-to-point free-space ***,through extending the target encoded modes into multiplexed OAM states,we realize all-optical dimension reduction for anomaly detection with an accuracy of 85%.Our work provides a deep insight to the mechanism of machine learning with spatial modes basis,which can be further utilized to improve the performances of various machine-vision tasks by constructing the unsupervised learning-based auto-encoder.

关键词： polarization mode momentum

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Consensus in Heterogeneous Higher-Order Agents Driven by Geometric Mean under Weight-Unbalanced Digraph 10

Consensus in Heterogeneous Higher-Order Agents Driven by Geo...

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10th Indian Control Conference, ICC 2024

作者： Sanskar, Prajjwal Tejal, R. Dunna, Vijaya Kumar Sahoo, Soumya Ranjan Sen, Arijit Department of Electrical Engineering and Computer Science IISER Bhopal India Department of Electrical Engineering IIT Kanpur India

ISBN: (纸本)9798331517212

This paper proposes a distributed algorithm to drive all agents in a heterogeneous multi-agent system (MAS) to the geometric mean of their initial conditions. It is defined as the GM-consensus problem. The proposed GM-consensus algorithm has a distributed observer for estimating the geometric mean of agents’ initial states. Its later part is a local controller for tracking these observed states. This structure facilitate the scaling of the algorithm for the agents having higher-order model. The numerical example exemplifies the efficiency of the proposed distributed GM-consensus algorithm. © 2024 IEEE.

关键词： Consensus algorithm

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