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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 34-40页

作者： Joice G.Philip Jaesung Jung Ahmet Onen the department of Energy Systems Research Ajou UniversitySuwonSouth Korea the department of electrical and Electronic engineering Abdullah Gul UniversityKayseri38080Turkey the department of electrical and computer engineering College of EngineeringSultan Qaboos UniversityAl-KhoudMuscatOman

This paper proposes an empirical wavelet transform(EWT)based method for identification and analysis of sub-synchronous oscillation(SSO)modes in the power system using phasor measurement unit(PMU)data.The phasors from PMUs are preprocessed to check for the presence of oscillations.If the presence is established,the signal is decomposed using EWT and the parameters of the mono-components are estimated through Yoshida algorithm.The superiority of the proposed method is tested using test signals with known parameters and simulated using actual SSO signals from the Hami Power Grid in Northwest China.Results show the effectiveness of the proposed EWT-Yoshida method in detecting the SSO and estimating its parameters.

关键词： Empirical wavelet transform(EWT) sub-synchronous oscillation Prony-based method Yoshida algorithm variational mode decomposition phasor measurement unit(PMU)

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Rotor Angle Stability Prediction Using Temporal and Topological Embedding Deep Neural Network Based on Grid-informed Adjacency Matrix

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 695-706页

作者： Peiyuan Sun Long Huo Xin Chen Siyuan Liang the School of electrical engineering Xi'an Jiaotong University the School of electrical engineering and the Center of Nanomaterials for Renewable Energy State Key Laboratory of Electrical Insulation and Power Equipment Xi'an Jiaotong University the department of computer Science and engineering The Chinese University of Hong Kong

Rotor angle stability(RAS) prediction is critically essential for maintaining normal operation of the interconnected synchronous machines in power systems. The wide deployment of phasor measurement units(PMUs) promotes the development of data-driven methods for RAS prediction. This paper proposes a temporal and topological embedding deep neural network(TTEDNN) model to accurately and efficiently predict RAS by extracting the temporal and topological features from the PMU data. The grid-informed adjacency matrix incorporates the structural and electrical parameter information of the power grid. Both the small-signal RAS with disturbance under initial operating conditions and the transient RAS with short circuits on transmission lines are considered. Case studies of the IEEE 39-bus and IEEE 300-bus power systems are used to test the performance, scalability, and robustness against measurement uncertainties of the TTEDNN model. Results show that the TTEDNN model performs best among existing deep learning models. Furthermore, the superior transfer learning ability from small-signal RAS conditions to transient RAS conditions has been proved.

关键词： Power system stability Phasor measurement units Rotors Transient analysis Stability criteria Deep learning Circuit stability

WebFLex:A Framework for Web Browsers-Based Peer-to-Peer Federated Learning Systems Using WebRTC

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computers, Materials & Continua 2024年第3期78卷 4177-4204页

作者： Mai Alzamel Hamza Ali Rizvi Najwa Altwaijry Isra Al-Turaiki department of computer Science College of Computer and Information SciencesKing Saud UniversityRiyadhKingdom of Saudi Arabia department of computer Science and engineering Punjab Engineering CollegeChandigarhIndia

Scalability and information personal privacy are vital for training and deploying large-scale deep learning models.Federated learning trains models on exclusive information by aggregating weights from various devices and taking advantage of the device-agnostic environment of web browsers.Nevertheless,relying on a main central server for internet browser-based federated systems can prohibit scalability and interfere with the training process as a result of growing client numbers.Additionally,information relating to the training dataset can possibly be extracted from the distributed weights,potentially reducing the privacy of the local data used for training.In this research paper,we aim to investigate the challenges of scalability and data privacy to increase the efficiency of distributed training models.As a result,we propose a web-federated learning exchange(WebFLex)framework,which intends to improve the decentralization of the federated learning process.WebFLex is additionally developed to secure distributed and scalable federated learning systems that operate in web browsers across heterogeneous devices.Furthermore,WebFLex utilizes peer-to-peer interactions and secure weight exchanges utilizing browser-to-browser web real-time communication(WebRTC),efficiently preventing the need for a main central server.WebFLex has actually been measured in various setups using the MNIST dataset.Experimental results show WebFLex’s ability to improve the scalability of federated learning systems,allowing a smooth increase in the number of participating devices without central data aggregation.In addition,WebFLex can maintain a durable federated learning procedure even when faced with device disconnections and network variability.Additionally,it improves data privacy by utilizing artificial noise,which accomplishes an appropriate balance between accuracy and privacy preservation.

关键词： Federated learning web browser privacy deep learning

Space/Air Covert Communications:Potentials,Scenarios,and Key Technologies

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China Communications 2024年第3期21卷 1-18页

作者： Mao Haobin Liu Yanming Zhu Lipeng Mao Tianqi Xiao Zhenyu Zhang Rui Han Zhu Xia Xianggen School of Electronic and Information engineering Beihang UniversityBeijing 100191China department of electrical and computer engineering National University of SingaporeSingapore 117583Singapore School of Science and engineering Shenzhen Research Institute of Big Datathe Chinese University of Hong KongShenzhenGuangdong 518172China department of electrical and computer engineering University of HoustonHoustonTX 77004USA department of computer Science and engineering Kyung Hee UniversitySeoulSouth Korea446-701 department of electrical and computer engineering University of DelawareNewarkDE 19716USA

Space/air communications have been envisioned as an essential part of the next-generation mobile communication networks for providing highquality global connectivity. However, the inherent broadcasting nature of wireless propagation environment and the broad coverage pose severe threats to the protection of private data. Emerging covert communications provides a promising solution to achieve robust communication security. Aiming at facilitating the practical implementation of covert communications in space/air networks, we present a tutorial overview of its potentials, scenarios, and key technologies. Specifically, first, the commonly used covertness constraint model, covert performance metrics, and potential application scenarios are briefly introduced. Then, several efficient methods that introduce uncertainty into the covert system are thoroughly summarized, followed by several critical enabling technologies, including joint resource allocation and deployment/trajectory design, multi-antenna and beamforming techniques, reconfigurable intelligent surface(RIS), and artificial intelligence algorithms. Finally, we highlight some open issues for future investigation.

关键词： artificial intelligence(AI) sixth generation(6G) space-air-ground integrated networks(SAGINs) space/air covert communications

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Relational Turkish Text Classification Using Distant Supervised Entities and Relations

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computers, Materials & Continua 2024年第5期79卷 2209-2228页

作者： Halil Ibrahim Okur Kadir Tohma Ahmet Sertbas department of computer engineering Faculty of Engineering and Natural SciencesIskenderun Technical UniversityHatay31200Turkey department of computer engineering Faculty of EngineeringIstanbul University-CerrahpasaIstanbul34310Turkey

Text classification,by automatically categorizing texts,is one of the foundational elements of natural language processing applications.This study investigates how text classification performance can be improved through the integration of entity-relation information obtained from the Wikidata(Wikipedia database)database and BERTbased pre-trained Named Entity Recognition(NER)models.Focusing on a significant challenge in the field of natural language processing(NLP),the research evaluates the potential of using entity and relational information to extract deeper meaning from texts.The adopted methodology encompasses a comprehensive approach that includes text preprocessing,entity detection,and the integration of relational information.Experiments conducted on text datasets in both Turkish and English assess the performance of various classification algorithms,such as Support Vector Machine,Logistic Regression,Deep Neural Network,and Convolutional Neural Network.The results indicate that the integration of entity-relation information can significantly enhance algorithmperformance in text classification tasks and offer new perspectives for information extraction and semantic analysis in NLP applications.Contributions of this work include the utilization of distant supervised entity-relation information in Turkish text classification,the development of a Turkish relational text classification approach,and the creation of a relational database.By demonstrating potential performance improvements through the integration of distant supervised entity-relation information into Turkish text classification,this research aims to support the effectiveness of text-based artificial intelligence(AI)tools.Additionally,it makes significant contributions to the development ofmultilingual text classification systems by adding deeper meaning to text content,thereby providing a valuable addition to current NLP studies and setting an important reference point for future research.

关键词： Text classification relation extraction NER distant supervision deep learning machine learning

Enhancement of Solar PV Panel Efficiency Using Double Integral Sliding Mode MPPT Control

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Tsinghua Science and Technology 2024年第1期29卷 271-283页

作者： Immadisetty Rahul Raju Hariharan department of electrical and Electronics engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical Sciences(SIMATS)Chennai 602105India

The extraction of maximum power from the solar panels,using the sliding mode control scheme,becomes popular for partial weather atmospheric conditions due to its effective dynamic duty cycle ratio.However,the sliding mode control scheme was sophisticated with single integral and double integral sliding mode control scheme,which offer enhanced maximum power extraction and support enhanced solar panel efficiency in partial weather conditions.The operation of the sliding mode control scheme depends on the selection of a sliding surface selection based on the atmospheric weather condition,which enables the effective sliding duty cycle ratio operation for the DC/DC boost converter.The duty cycle ratio of the sliding mode control resembles the usual dynamic behavior to achieve enhanced efficiency compared to the various maximum power point tracking(MPPT)schemes.The major limitation of the sliding mode control scheme is to achieve the steady state voltage error of the solar panel in minimum settling time duration.The single integral sliding mode control scheme achieves the expected steady state voltage error limit but fails to achieve minimum settling time duration.Hence,the single integral sliding mode control is extended to a double integral sliding mode control scheme to achieve both steady state voltage error limits within the minimum settling time duration.This double integral sliding mode control scheme allows us to obtain the higher sliding surface duty cycle ratio which acts as the input signal to the boost converter.This activates the enhanced stable and reliable system operation,and nullifies the lacuna of maximum solar panel efficiency under partial weather conditions.Hence,this paper aims to present the design and performance operation of the double integral sliding mode(DISM)MPPT control scheme.To validate the performance analysis of the proposed DISM MPPT control scheme,the MATLAB/Simulink model is designed and verified.Also,the performance analysis of the proposed DISM MPPT control scheme is compared with

关键词： maximum power point tracking(MPPT)schemes sliding mode controller(SMC) double integral sliding mode controller(DISMC) pulse width modulation(PWM) photovoltaic(PV)system

Laser‑Induced and MOF‑Derived Metal Oxide/Carbon Composite for Synergistically Improved Ethanol Sensing at Room temperature

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Nano-Micro Letters 2024年第6期16卷 210-220页

作者： Hyeongtae Lim Hyeokjin Kwon Hongki Kang Jae Eun Jang Hyuk‑Jun Kwon department of electrical engineering and computer Science DGISTDaegu 42988South Korea Convergence Research Advanced Centre for Olfaction DGISTDaegu 42988South Korea

Advancements in sensor technology have significantly enhanced atmospheric monitoring.Notably,metal oxide and carbon(MO_(x)/C)hybrids have gained attention for their exceptional sensitivity and room-temperature sensing performance.However,previous methods of synthesizing MO_(x)/C composites suffer from problems,including inhomogeneity,aggregation,and challenges in micropatterning.Herein,we introduce a refined method that employs a metal–organic framework(MOF)as a precursor combined with direct laser writing.The inherent structure of MOFs ensures a uniform distribution of metal ions and organic linkers,yielding homogeneous MO_(x)/C structures.The laser processing facilitates precise micropatterning(<2μm,comparable to typical photolithography)of the MO_(x)/C crystals.The optimized MOF-derived MO_(x)/C sensor rapidly detected ethanol gas even at room temperature(105 and 18 s for response and recovery,respectively),with a broad range of sensing performance from 170 to 3,400 ppm and a high response value of up to 3,500%.Additionally,this sensor exhibited enhanced stability and thermal resilience compared to previous MOF-based counterparts.This research opens up promising avenues for practical applications in MOF-derived sensing devices.

关键词： Metal-organic frameworks Metal oxide Carbon composite Laser Gas sensor

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Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

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Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the department of electrical engineering University of ArkansasFayettevilleAR 72701USA the department of computer Science and computer engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather conditions.The proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging activities.The scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action spaces.One of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy systems.The new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and exploitation.The new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep Q-learning.The online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

A Least-squares-based Iterative Method with Better Convergence for PF/OPF in Integrated Transmission and Distribution Networks

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 953-962页

作者： Kunjie Tang Shufeng Dong Yonghua Song College of electrical engineering Zhejiang UniversityHangzhou 310027China State Key Laboratory of Internet of Things for Smart City and department of electrical and computer engineering University of MacaoMacao SARChina IEEE

The limitations of the conventional master-slavesplitting(MSS)method,which is commonly applied to power flow and optimal power flow in integrated transmission and distribution(I-T&D)networks,are first analyzed.Considering that the MSS method suffers from a slow convergence rate or even divergence under some circumstances,a least-squares-based iterative(LSI)method is proposed.Compared with the MSS method,the LSI method modifies the iterative variables in each iteration by solving a least-squares problem with the information in previous iterations.A practical implementation and a parameter tuning strategy for the LSI method are discussed.Furthermore,a LSI-PF method is proposed to solve I-T&D power flow and a LSIheterogeneous decomposition(LSI-HGD)method is proposed to solve optimal power flow.Numerical experiments demonstrate that the proposed LSI-PF and LSI-HGD methods can achieve the same accuracy as the benchmark methods.Meanwhile,these LSI methods,with appropriate settings,significantly enhance the convergence and efficiency of conventional methods.Also,in some cases,where conventional methods diverge,these LSI methods can still converge.

关键词： Fixed-point theory integrated transmission and distribution networks least-squares-based iterative method master-slave-splitting method optimal power flow power flow

Topological Dirac-vortex microcavity laser for robust on-chip optoelectronics

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Light(Science & Applications) 2024年第4期13卷 591-592页

作者： Yuanpeng Wu Zetian Mi department of electrical engineering and computer Science University of Michigan1301 Beal AvenueAnn ArborMI 48109USA

Dirac-vortex microcavity laser based on InAs/InGaAs quantum dots have been experimentally realized on silicon substrate.The topological laser features a large spectral range and high robustness against variations such... 详细信息

关键词： laser optoelectronics cavity