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Enhancing Smart Microgrid Resilience and Virtual Power Plant Profitability Through Hybrid IGWO-PSO Optimization With a Three-Phase Bidding Strategy

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IEEE Access 2025年 13卷 80796-80820页

作者： Yuvaraj, T. Sengolrajan, T. Prabaharan, Natarajan Devabalaji, K.R. Uehara, Akie Senjyu, Tomonobu Chennai Institute of Technology Centre for Smart Energy Systems Chennai600069 India Kongunadu College of Engineering and Technology Department of Electrical and Electronics Engineering Tiruchirappalli621215 India SASTRA Deemed University School of Electrical and Electronics Engineering Thanjavur613401 India Aarupadai Veedu Institute of Technology Vinayaka Missions Research Foundation Department of Electrical and Electronics Engineering Chennai603104 India University of the Ryukyus Faculty of Engineering Nishihara903-0213 Japan

The increasing energy demand and rising fossil fuel prices are accelerating the transition to renewable energy, supported by government initiatives due to their environmental and economic advantages. However, challenges such as limited capacity and stability constraints hinder the widespread adoption of distributed energy resources (DERs). Virtual Power Plants (VPPs) enhance market participation by aggregating DERs, while electric vehicles (EVs) contribute to environmental sustainability by reducing emissions. Additionally, integrating distribution static compensators (DSTATCOMs) within VPPs improves microgrid stability and reactive power support. This study proposes a two-stage optimization approach to enhance network resilience and VPP profitability in a radial distribution network (RDN). The first stage focuses on minimizing resilience-related costs and energy not supplied (ENS) during natural disasters, while the second stage optimizes VPP profit using a three-phase bidding strategy, which includes the day-ahead market, real-time market, and overall market. A hybrid improved grey wolf optimization-particle swarm optimization (IGWO-PSO) algorithm is developed to solve this complex optimization problem. To demonstrate the effectiveness of the proposed approach, IGWO-PSO is compared with other hybrid optimization algorithms. Validation on a modified IEEE 33-bus RDN confirms that the proposed model enhances VPP placement and sizing, leading to improved economic, operational, and resilience metrics. Furthermore, the model accounts for uncertainties in load demand, renewable generation, energy prices, and equipment availability, ensuring a robust and adaptable energy management strategy. © 2013 IEEE.

关键词： Particle swarm optimization (PSO)

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Enhancing Data Security and Privacy in Cloud Computing: A Survey of Modern Techniques 3

Enhancing Data Security and Privacy in Cloud Computing: A Su...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Suganya, N. Gouthami, P. Sathiya, R. Sivaranjani, S. Murugesan, M. Department of Computer Science and Business Systems Dr. N.G.P. Institute of Technology Coimbatore India Department of Information Technology Thangavelu Engineering College Chennai India Departement of Electrical and Electronics Engineering Sri Krishna College of Engineering and Technology Coimbatore India Departement of Electrical and Electronics Engineering Karpagam Institute of Technology Coimbatore India

ISBN: (纸本)9798331527549

A computing system that is based on the internet and offers users all resources as on-demand services. Servers, storage, databases, software, and networking are among these on-demand services. Usually, the user must pay for the use of the resources;they are not their property. The number of people using this technology is increasing dramatically as a result of these features. Although it has benefits, cloud computing poses certain security and privacy risks. The authorized user’s data can be stolen easily. This current research thoroughly investigates modern strategies to these problems, highlighting identity management protocols, effective encryption and blockchain-based solutions. The article identifies research gaps, focuses attention to the inadequacies of current approaches, and recommends the possible ways to improve the efficiency, scalability and trustworthiness. © 2025 IEEE.

关键词： Cloud security

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An Underdetermined Single-Channel Blind-Source-Separation for Multisource Acoustic-Emission-Based Partial Discharge Signals in Power Transformers

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IEEE Transactions on Plasma Science 2025年第4期53卷 788-797页

作者： Mandal, Raj Kumar Devarajan, Harimurugan Nandi, Sounak Shanker, T. Bhavani Ghassemi, Mona Dr. B. R. Ambedkar National Institute of Technology Jalandhar144011 India Central Power Research Institute Power Capacitors Division Bengaluru560080 India University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

Condition monitoring of the power transformer is important in extending the lifetime of the transformer through timely maintenance. Acoustic-emission (AE)-based partial discharge (PD) monitoring in a transformer is a commonly used technique for assessing the PD levels inside the transformer. The classification of the type of PD or localization of PD generally involves using multiple acoustic sensors, and recently, deep learning techniques have been widely used with the acquired PD data for classification. In the present work, a single-channel synchrosqueezing transform (SST)-based FastIVA blind-source-separation (BSS) technique in the time-frequency (TF) domain is proposed for the separation of multiple PD signals. SST, along with singular value decomposition (SVD), is used to identify the number of sources. The SST-FastIVA-based method is used for PD signal separation for the first time and it performs better than other separation techniques in noisy conditions. An SVM-based machine learning model is developed with original unmixed source signals for a classification task. The separated signals are tested using the developed model, and it attains an F1 score of 78.70% on the separated signals. Furthermore, the proposed methodology is used with the experimental data, and the F1 score obtained with the separated signal is 84.44%. The proposed single-channel (sensor) technique with BSS is a cost-effective method that can be used for the classification of multiple PD sources. © 1973-2012 IEEE.

关键词： Blind source separation

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Bidirectional LSTM-Based Energy Consumption Forecasting:Advancing AI-Driven Cloud Integration for Cognitive City Energy Management

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computers, Materials & Continua 2025年第5期83卷 2907-2926页

作者： Sheik Mohideen Shah Meganathan Selvamani Mahesh Thyluru Ramakrishna Surbhi Bhatia Khan Shakila Basheer Wajdan Al Malwi Mohammad Tabrez Quasim Department of Computer Science and Engineering Srinivasa Ramanujan CentreSASTRA Deemed UniversityKumbakonam612001India Department of Computer Science and Engineering Faculty of Engineering and TechnologyJAIN(Deemed-to-be University)Bangalore562112India School of Science Engineering and EnvironmentUniversity of SalfordManchesterM54WTUK University Centre for Research and Development Chandigarh UniversityMohali140413PunjabIndia Centre for Research Impact and Outcome Chitkara University Institute of Engineering and TechnologyChitkara UniversityRajpura140401PunjabIndia Department of Information Systems College ofComputer and Information SciencePrincessNourah bintAbdulrahmanUniversityP.O.Box 84428Riyadh11671Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid UniversityAbha61421Saudi Arabia Department of Computer Science and Artificial Intelligence College of Computing and Information TechnologyUniversity of BishaBisha61922Saudi Arabia

Efficient energy management is a cornerstone of advancing cognitive cities,where AI,IoT,and cloud computing seamlessly integrate to meet escalating global energy *** this context,the ability to forecast electricity consumption with precision is vital,particularly in residential settings where usage patterns are highly variable and *** study presents an innovative approach to energy consumption forecasting using a bidirectional Long Short-Term Memory(LSTM)*** a dataset containing over twomillionmultivariate,time-series observations collected froma single household over nearly four years,ourmodel addresses the limitations of traditional time-series forecasting methods,which often struggle with temporal dependencies and non-linear *** bidirectional LSTM architecture processes data in both forward and backward directions,capturing past and future contexts at each time step,whereas existing unidirectional LSTMs consider only a single temporal *** design,combined with dropout regularization,leads to a 20.6%reduction in RMSE and an 18.8%improvement in MAE over conventional unidirectional LSTMs,demonstrating a substantial enhancement in prediction accuracy and *** to existing models—including SVM,Random Forest,MLP,ANN,and CNN—the proposed model achieves the lowest MAE of 0.0831 and RMSE of 0.2213 during testing,significantly outperforming these *** results highlight the model’s superior ability to navigate the complexities of energy usage patterns,reinforcing its potential application in AI-driven IoT and cloud-enabled energy management systems for cognitive *** integrating advanced machine learning techniqueswith IoT and cloud infrastructure,this research contributes to the development of intelligent,sustainable urban environments.

关键词： Deep learning bidirectional LSTM energy consumption forecasting time-series analysis predictive modeling machine learning in energy management

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Intelligent Cyber Defense: Leveraging LLMs for Real-Time Threat Detection and Analysis 21st

Intelligent Cyber Defense: Leveraging LLMs for Real-Time Th...

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21st European, Mediterranean, and Middle Eastern Conference on Information systems, EMCIS 2024

作者： Kakoulli, Elena Zacharioudakis, Eleftherios Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine

ISBN: (纸本)9783031813214

This paper reviews the application of Large Language Models (LLMs) in addressing the growing sophistication of cybersecurity threats. By evaluating numerous studies, we delineate the role of LLMs in improving the detection, analysis, and response to cyber threats in real-time. Our findings demonstrate that LLMs significantly enhance the efficiency and accuracy of cybersecurity measures compared to traditional models. However, their deployment in cybersecurity frameworks involves critical considerations around ethics, privacy, and security. This review provides a succinct overview of LLMs’ current capabilities and practical implementations within the cybersecurity landscape, identifies research gaps, and suggests future research directions. The insights presented here highlight the vital contribution of LLMs to the advancement of cybersecurity solutions and adapting to evolving threat environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cyber attacks

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XMeta-OS: A Conceptual Framework for Unified Resource Management in Distributed Edge-Cloud Environments 3rd

XMeta-OS: A Conceptual Framework for Unified Resource Manag...

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3rd International Conference on Advances in Computing research, ACR 2025

作者： Zacharioudakis, Eleftherios Kakoulli, Elena Evripidou, Salomi Intelligent Systems Laboratory Department of Computer Science Neapolis University Pafos Pafos Cyprus National Technical University of Ukraine Igor Sikorsky Kyiv Polytechnic Institute Kyiv Ukraine Electrical Engineering and Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

ISBN: (纸本)9783031876462

This paper introduces XMeta-OS, a meta-operating system specially designed with Linux as its foundation to unify and optimize resource management for the distributed edge-cloud when dynamic use of GPU resources is possible. This approach offer a new GPU abstraction layer and dynamic edge-cloud allocation model that offer enhanced flexibility and resource utilization over existing systems like Kubernetes and OpenStack. By using likely sensitive data, the model applied cloud-pushed deployment model. XMeta-OS manages computer resources in real-time. Therefore, fulfilling high performance various needs. It allocates CPU and GPU in real-time to workloads requiring massive parallel processing, such as machine learning, scientific simulations, real-time data analytics, and transportation tasks. Fabrication in the laboratory of an edge-cloud environment can help to assess the scalability, fault tolerance and efficiency of the system. It showcases the overall architectural design of XMeta-OS. Changes to the kernel and security solutions for safe and effective distribution are suggested. This article presents the design of the XMeta-OS. The future of XMeta-OS entails usage and testing with which we see latency, energy, computation and other such measurements in labs. XMeta-OS is a solution to cost performance, emerging application, large-scale edge-cloud deployments, and distributed GPU-enabled infrastructures. Meta-Operating System in Edge-Cloud Continuum for dynamic resource management of GPU. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Cluster computing

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Dual-Polarized Reconfigurable Reflectarray Using Vanadium Dioxide for 5G mmWave Communication

Dual-Polarized Reconfigurable Reflectarray Using Vanadium Di...

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2025 IEEE International Workshop on Antenna Technology, iWAT 2025

作者： Disharoon, Walter Dasari, Sree Adinarayana Williamson, Thomas G. Kovitz, Joshua Ghalichechian, Nima Electrical and Computer Engineering Georgia Institute of Technology Atlanta United States Georgia Tech Research Institute Sensors and Electromagnetic Applications Laboratory Atlanta United States Georgia Tech Research Institute Advanced Concepts Laboratory Atlanta United States

ISBN: (纸本)9798331527365

A vanadium dioxide (VO2) reconfigurable reflectarray was designed and simulated for operation between 28-32 GHz. VO2 is an insulator-metal-transition phase-change material that exhibits a sharp, reversible conductivity change at 68 °C. The design is dual-polarized to support LP, dual-LP, or dual-CP applications. The simulated reflection loss is less than 1 dB over the entire band. The phase difference is 180 ± 20° between 28-33 GHz. The simulated realized gain of the finite array peaks at 22.9 dBi, which corresponds to an aperture efficiency of 29%. The 3-dB Gain bandwidth covers 27.7-33.2 GHz, which is an 18% bandwidth. Ongoing fabrication and measurement results will be presented at the conference. © 2025 IEEE.

关键词： Reflection

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A Hierarchical Progressive Perception System for Autonomous Luggage Trolley Collection

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IEEE Transactions on Automation Science and engineering 2025年 22卷 14484-14497页

作者： Sun, Zhirui Zhang, Zhe Zhao, Jieting Ye, Hanjing Wang, Jiankun Southern University of Science and Technology Shenzhen Key Laboratory of Robotics Perception and Intelligence Department of Electronic and Electrical Engineering Shenzhen518055 China Southern University of Science and Technology Jiaxing Research Institute Jiaxing314000 China Southern University of Science and Technology Shenzhen Key Laboratory of Robotics and Computer Vision Department of Electronic and Electrical Engineering Shenzhen518055 China

Advancements in intelligent vehicle technologies and autonomous driving are now used for tasks like collecting luggage trolleys at airports. The robotic autonomous luggage trolley collection system employs robots to gather and transport scattered luggage trolleys. However, existing methods for detecting and locating luggage trolleys often fail when they are not fully visible. To address this, we introduce the hierarchical progressive perception system, which enhances the detection and localization of luggage trolleys under partial occlusion. The proposed system integrates the hierarchical process structure and progressive perception strategy. This innovative structure processes the luggage trolley’s position and orientation separately. It can accurately determine the luggage trolley’s position with just one well-detected keypoint and estimate its orientation when partially occluded. Once the luggage trolley’s initial pose is detected, the progressive perception strategy continuously refines this information until the robot begins grasping. The proposed system only needs RGB images for labeling and training, eliminating the need for complex data collection and annotation. The experiments on detection and localization demonstrate that the proposed system is more reliable under partial occlusion compared to existing methods. Its effectiveness and robustness have also been confirmed through practical tests in actual luggage trolley collection tasks. © 2004-2012 IEEE.

关键词： Intelligent robots

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation Science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Design of Intelligent Ship Wireless Charging System 19th

Design of Intelligent Ship Wireless Charging System

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Yu, Chunlai Ding, Haolun Wang, Shiqing Wang, Zhikai Zhu, Hao Zhu, Junkang Zhang, Chenxi Luo, Zhanpeng Marine Engineering College Dalian Maritime University Dalian116026 China CSSC Systems Engineering Research Institute Beijing100094 China College of Electrical Automation and Information Engineering Tianjin University Tianjin300072 China

ISBN: (纸本)9789819613823

With the implementation of greenhouse gas emission reduction strategy, energy saving and emission reduction in the shipping industry is imperative. As a kind of clean energy, ship wireless charging, which can not only provide auxiliary electricity to ships, but also charge electric ships, has been increasingly emphasized by the industry. However, the existing ship wireless charging system has the problems of low berthing accuracy, low number of degrees of freedom of charging mechanism, cumbersome charging process, low charging power and so on. In this paper, an intelligent ship wireless charging system is proposed and realized. It mainly includes: firstly, developing a multi-degree-of-freedom robotic arm as a charging carrier, and designing a visual control system to realize the charging robotic arm automatically docking with the ship’s receiving end. Secondly, an under driven electric unmanned ship is developed as the receiving ship, and the PTESO-LOS algorithm should be utilized to control the unmanned ship to berth automatically;once again, wireless charging based on coupled magnetic field is used to realize wireless energy transmission. Finally, the wireless charging system for ships proposed in this paper is experimentally verified to meet the wireless charging demand of ships in the state of sea movement, which improves the safety and convenience of wireless charging. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Robotic arms

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