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Joint Optimization of Beamforming and Transmission Power Based on Digital Twins Control System 19

Joint Optimization of Beamforming and Transmission Power Bas...

引用

2022 ieee SmartWorld, 19th ieee international conference on Ubiquitous Intelligence and computing, 2022 ieee international conference on Autonomous and Trusted Vehicles conference, 22nd ieee international conference on Scalable computing and Communications, 2022 ieee international conference on Digital Twin, 8th ieee international conference on Privacy computing and 2022 ieee international conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Li, Lang Xu, Xunwen Bian, Baoyin Yang, Hongzhen Li, Wenmeng Zhao, Yujing Zhang, Hua Wang, Jun-Bo Elec. Power Research Inst. State Grid Nanjing211106 China Southeast University National Mobile Comm. Research Laboratory Nanjing211111 China Info. & Telecomm. Br. State Grid Z.j. Elec. Corp. Hangzhou31005 China Henan Info. & Telecom. Co. State Grid Zhengzhou450000 China

ISBN: (纸本)9798350346558

As one of the key components in sixth-generation (6G) wireless networks, ultra-reliable low latency communications (URLLC) can facilitate the realization of real-time wireless control systems. For such systems, the wireless communication in URLLC scenarios and the control process are no longer independent and jointly affect the overall performance. Therefore, this paper introduces digital twins (DT) to model the wireless control systems and investigates the joint optimization of the communication resources and the control performance. To maintain the control stabilization of DT in the power system while improving the transmission efficiency, we propose a joint beamforming and transmission power optimization algorithm for uplink transmission to minimize the total transmission power. Then we solve the beamforming vector and power by sequential convex approximation method, and optimize them iteratively. The simulation results illustrate that the proposed algorithm can effectively guarantee the stability and transmission efficiency of DT system, and it has better performance than other resource allocation schemes. © 2022 ieee.

关键词： Convex optimization

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Development Process of electrical Arc Movement on Ice-covered HVDC Outdoor Insulators 5

Development Process of Electrical Arc Movement on Ice-covere...

引用

5th ieee international conference on Dielectrics, ICD 2024

作者： Wang, Bohan Liu, Yong Li, Chao Wang, Minxin Li, Xiaowen Du, B.X. School of Electrical and Information Engineering Tianjin University Key Laboratory of Smart Grid of Ministry of Education Tianjin300072 China State Grid Jiangxi Electric Power Co. Ltd Xinfeng Power Supply Branch Xinfeng341600 China

ISBN: (纸本)9798350308976

This study concentrates on the dynamics of electrical arcs on ice-accreted outdoor insulators under DC voltage, essential for ensuring the reliability of electrical grids in extreme icy conditions. employing a comprehensive methodology that combines ice accretion experiments, theoretical analysis, and simulation modeling, the research aims to explore the characteristics and attenuation mechanisms of arc formation and development. Key findings include the effective characterization of arc formation, development, and pre-flashover dynamics under DC voltage, particularly noting intensified discharge activities under negative DC voltage. Furthermore, the study establishes a quantitative relationship between arc burning time, leakage current, and arc parameters, identifying distinct contact-type and non-contact-type arc attenuation mechanisms. These insights not only enhance the understanding of flashover mechanisms on iced insulators but also contribute significantly to improving their operational reliability in harsh environments. © 2024 ieee.

关键词： Flashover

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Vulnerability Modelling and Status Assessment Method for Key equipment in Substations under HeMP e1 Impact 4

Vulnerability Modelling and Status Assessment Method for Key...

引用

4th international conference on Intelligent Power and Systems, ICIPS 2024

作者： Huang, Weizhao Ren, Sirui Yang, Songhao Ou, Xuan Xin, Tuo China Southern Power Grid Shenzhen Power Supply co. LTD Shenzhen China School of Electrical Engineering Xi'an Jiaotong University Xi'an China

ISBN: (纸本)9798331532765

High-altitude electromagnetic Pulse (HeMP) poses a significant threat to substation equipment, especially with the increasing deployment of intelligent electronic device (IeD). Due to the complex wiring, wide-area distribution, and high automation of modern substations, vulnerability assessments of system components are limited and incomplete. This study focuses on e1 effects, which can penetrate electronic devices, damaging sensitive internal components. By using the generalized extreme value (GeV) distribution and analyzing failure modes of critical secondary equipment, a probabilistic model to estimate the likelihood of equipment transitioning into failure states is developed, highlighting the risks of abnormal and failure states. This work addresses gaps in the current vulnerability assessment framework, leveraging existing experimental results and state transfer models, providing a basis for more comprehensive strategies and damage assessment for substations under electromagnetic threats. © 2024 ieee.

关键词： GeV distribution HeMP state transfer model substation

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OASIS: An Ontology-Driven Autonomic Management System for Intelligent Substations 24

OASIS: An Ontology-Driven Autonomic Management System for In...

引用

24th ieee international conference on High Performance computing and Communications, 8th ieee international conference on Data science and Systems, 20th ieee international conference on Smart City and 8th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Xie, Xiaodong Yang, Jianxu Hua, Xiong Zhou, Qianru State Grid Corporation of China Beijing100031 China Nari Group Corporation State Grid Electric Power Research Institute Hefei230088 China School of Compute Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350319934

With the soar of the demand for electricity all over the so-ciety, an intelligent autonomic management system for substations is in urgent need. Ontology driven knowledge based system plays a vital role in achieving a truly autonomic, zero-touch substation management system. This paper proposes OASIS, an ontology driven autonomic management system for intelligent substations, which is able to organize first-nary devices and verify human operations to prevent mis-operation in real-time. The ontology used and develop procedures are presented in detail. OASIS has been tested in a real-life substation, and the test results are presented in detail. It is evident that OASIS is competent for autonomic substation management, including device power status calculation, main device running status retrieval, interval organization, general faulty check and single operation mis-operation verification, in a efficient way, with the responding time less than two seconds in general. At last, several lessons learnt during the developing procedure are discussed as a conclusion. © 2022 ieee.

关键词： Ontology

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enhancing grid-Connected PV System Performance: Loss Analysis and efficiency Improvement Strategies 23

Enhancing Grid-Connected PV System Performance: Loss Analysi...

引用

23rd ieee international conference on Software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Darteh, Oscar Famous Wang, Guojun Liu, Qi Liu, Xiaodong Acakpovi, Amevi School of Electronics and Information Engineering Nanjing University of Information Science and Technology Nanjing Jiangsu China Yancheng Polytechnic College China Wuxi University Wuxi Jiangsu China School of Computing Napier University Edinburgh United Kingdom Accra Technical University Department of Electrical Engineering Greater Accra Accra Ghana

ISBN: (纸本)9798350359398

This study delves into solar photovoltaic (PV) systems as a beacon of sustainable energy transition, emphasizing their environmental benefits and potential for decentralized power generation, the research focuses on integrating load demand into PV systems through Simulink-based experiments. Four integral components-the boost converter, grid inverter, control unit, and load design-are optimized for seamless operation and minimal grid disruptions. The study acknowledges common power losses during the conversion of DC to AC process, highlighting the minimal loss of 2.15 per cent of the total input power and approximately 98 per cent of efficiency while ensuring practical feasibility. The transformation of harvested solar energy into usable AC power is emphasized, along with the potential contributions of the proposed strategies to sustainable energy development and environmental preservation. In addressing the balance between energy demand and environmental responsibility, this research offers insights into enhancing the performance and viability of grid-connected PV systems. © 2023 ieee.

关键词： Solar energy

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DRL Model for Distributed Agent-based IoT on Multi-Access edge computing for Accident Forecast 8

DRL Model for Distributed Agent-based IoT on Multi-Access Ed...

引用

8th ieee/ACIS international conference on Big Data, Cloud computing, and Data science, BCD 2023

作者： Seol, Jongho Kim, Jongyeop Kancharla, Abhilash Middle Georgia State University Department of Information Technology MaconGA United States Georgia Southern University Department of Information Technology StatesboroGA United States University of Tampa Department of Computer Science TampaFL United States

ISBN: (纸本)9798350373615

This paper introduces a state-of-The-Art Deep Reinforcement Learning (DRL) model designed for IoT devices in Multi-Access edge computing (MeC), with a focus on urban accident prediction. The edge server serves as a central hub, collecting reinforcement learning data from distributed IoT devices and distributing custom deep learning models for reinforcement learning back to these devices, all within the network. The study also addresses privacy concerns by combining Differential Privacy (DP) with Federated Learning (FL) in DRL within MeC environments. DRL combines deep learning and reinforcement learning principles, enabling wireless IoT devices to optimize actions for maximum rewards in their environments. The surge in IoT devices in networks not only generates massive data but also raises privacy concerns. Differential Privacy, rooted in Google's FL technology, safeguards data privacy. IoT devices utilize DRL and FL to improve learning efficiency and uphold privacy policies. The paper outlines how wireless IoT devices accumulate traceable data tables through reinforcement learning within a grid-world framework. Within the MeC architecture, edge servers efficiently execute deep neural networks tailored for reinforcement learning data and distribute results to other IoT devices upon request. This paper presents a tailored DRL model for MeC and a federated DRL framework for maintaining differential privacy while handling shared data generated through deep learning in edge servers. © 2023 ieee.

关键词： Accidents

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An electricity Demand Forecasting Model Based on GCN and Transformer

An Electricity Demand Forecasting Model Based on GCN and Tra...

引用

2024 international conference on Artificial Intelligence and Power Systems, AIPS 2024

作者： Na, Qionglan Li, Xin Wang, Yifei Yang, Yixi Zhang, Shijun Li, Xinyi State Grid Jibei Information & Telecommunication Company Beijing Xicheng China State Grid Information & Telecommunication Branch Beijing Xicheng China

ISBN: (纸本)9798350392210

electricity demand forecasting is a pivotal aspect of the rapidly evolving power industry, enabling utility companies to optimize generation scheduling, reduce reserve capacities, and enhance economic productivity. existing forecasting models still face several limitations, particularly in not having fully explored the potential of large-scale models. This paper proposes a novel electricity demand forecasting model that combines the long-range dependency modeling capabilities of Transformer with the ability of a Graph Convolutional Network to capture the underlying correlations between different users. By deploying a large language model (LLM) as the prediction engine, our model aims to further improve the accuracy of electricity load forecasting. We demonstrate the effectiveness of our approach on real-world power datasets. © 2024 ieee.

关键词： Demand response

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Knowledge Graph Model for computing Continuum Over Smart grid 15

Knowledge Graph Model for Computing Continuum Over Smart Gri...

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15th international conference on Information, Intelligence, Systems and Applications, IISA 2024

作者： Martin, Ioana Ramona Cioara, Tudor Arcas, Gabriel Ioan Anghel, Ionut Bertoncini, Massimo Bosch Engineering Center Cluj-Napoca Romania Technical University of Cluj-Napoca Computer Science Department Cluj-Napoca Romania Engineering Ingegneria Informatica Rome Italy

ISBN: (纸本)9798350368833

The evolution of the smart grid has been driven by the emergence of Internet of Things devices, coupled with advancements in powerful Information and Communications Technology and the integration of distributed energy sources at the edge. However, as the amount of collected data grows, there's a strong need to shift the computational load of energy management services as close as possible to the data generation source to be able to meet the time and latency constraints. To support the offloading and orchestration of computation workload in a smart grid, we propose a knowledge graph model designed to semantically represent relevant information and knowledge about the types of resources available from edge devices to cloud servers that can be further utilized for processing tasks. each model layer comprises specialized nodes interlinked through a hierarchical structure and provides context-rich information on the components of the electrical grid infrastructure, such as substations, renewable energy sources, and consumers at the intersection of the data with energy networks. For evaluation, we have defined rules covering a broad spectrum of energy applications, showing that the model serves as a foundation for enhancing the management of smart grid systems by also considering edge computation requirements. © 2024 ieee.

关键词： Knowledge graph

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MISA: UNVeILING THe VULNeRABILITIeS IN SPLIT FeDeRATeD LeARNING 49

MISA: UNVEILING THE VULNERABILITIES IN SPLIT FEDERATED LEARN...

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49th ieee international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wan, Wei Ning, Yuxuan Hu, Shengshan Xue, Lulu Li, Minghui Zhang, Leo Yu Jin, Hai Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Huazhong Univ Sci & Technol Sch Software Engn Wuhan Peoples R China Griffith Univ Sch Informat & Commun Technol Nathan Australia Natl Engn Res Ctr Big Data Technol & Syst Beijing Peoples R China Serv Comp Technol & Syst Lab Wuhan Peoples R China Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China Hubei Engn Res Ctr Big Data Secur Wuhan Peoples R China Cluster & Grid Comp Lab Wuhan Peoples R China

ISBN: (纸本)9798350344868;9798350344851

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users' devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of robustness. In this paper, we present a novel poisoning attack known as MISA. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

关键词： Split Federated Learning Poisoning Attack Robustness

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An Integrated and Optimized Fog computing enabled Framework to minimize Time Complexity in Smart grids 6

An Integrated and Optimized Fog Computing enabled Framework ...

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6th international conference on Contemporary computing and Informatics, IC3I 2023

作者： Nag, Rohan Samantraj, Adhinayak S. Mishra, Sushruta Sharma, Vandana School of Computer Engineering Kalinga Institute of Industrial Technology Bhubaneswar India Computer Science Department Delhi NCR India

ISBN: (纸本)9798350304480

A distributed computing paradigm known as 'cloud computing'works as a connection between IoT devices and cloud data centres. The environment system model in this work is on basis of clouds and fog and includes smart grids, which we explore. Prior to understanding the use of fog computing in smart grids we discuss about various features of cloud computing and talk about how to manage the connection between fog and cloud computing. Along with the usual performance of low latency, low cost, and high intelligence, the distinctive characteristics and service scenarios are also explored. Based on the outcome of the simulation, it appears that our suggested PSO-SA algorithm outperforms other optimization algorithms. It recorded a least mean response time of 3.86 seconds only. While the model build up delay was 4.6 seconds, the model execution delay was also found to be only 4.9 seconds with PSO-SA method. The improved efficiency of the technique can be credited to the best aspects of particle swarm optimisation (PSO) and a modified inertia weight obtained by simulated annealing. © 2023 ieee.

关键词： Cloud computing edge computing Fog computing Smart grid

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