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检索条件"任意字段=IEEE International Conference on e-Science and Grid Computing"

共 17242 条记录，以下是111-120 订阅

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Computation Tasks Offloading Method for Digital Distribution Networks Based on edge-edge Collaboration 7

Computation Tasks Offloading Method for Digital Distribution...

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7th international conference on energy, electrical and Power engineering (CeePe)

作者： Liu, Tong Zeng, Rong Zhang, Ruifeng Xu, Min Zhang, Hengrong Fu, Yu Wu, Peng Huang, Chaoming China Southern Power Grid Elect Power Res Inst Guangzhou Peoples R China Guizhou Power Grid Co Ltd Elect Power Res Inst Guiyang Guizhou Peoples R China

ISBN: (纸本)9798350375794;9798350375800

Computation offloading is an effective strategy for leveraging the computing resources of edge-side devices, enhancing performance to swiftly respond to real-time computation tasks while ensuring successful completion using limited resources. This paper proposes an offloading method that takes into account the collaboration of multiple edge-side devices for real-time computation tasks in distribution networks. The data transmission process is analyzed in detail, while characteristics of real-time computation tasks, logic correlations between each split part of real-time computation tasks, and hardware resources for edge computing devices are considered as complex constraints. The proposed method reduces the overall completion delay and improves the successful completion rate of real-time tasks by splitting these tasks properly, transmitting computation data orderly, and allocating resources reasonably. The effectiveness of the proposed method is verified by a typical case.

关键词： edge computing digital distribution network resource scheduling computation offloading real-time tasks

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Leveraging Compact Data Accumulator to enable In-Network Anomaly Detection in Programmable Switches for Power grids

Leveraging Compact Data Accumulator to Enable In-Network Ano...

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2024 international conference on Communications Control and computing Technologies for Smart grids

作者： Hu, Zheng Lin, Hui Qu, Yanfeng Jin, Dong Univ Arkansas Elect Engn & Comp Sci Fayetteville AR 72701 USA Univ Rhode Isl Elect Comp & Biomed Engn Kingston RI USA

ISBN: (纸本)9798350318562;9798350318555

The escalating cyber-attacks targeting power infrastructure underscore the critical importance of smart grid security. However, existing solutions often struggle with the challenge of balancing security and performance overhead, leading to suboptimal protection or increased operational latency. To address this, we propose an intrusion detection system (IDS) designed to operate within P4-based programmable network devices, enabling real-time identification of critical attacks like distributed denial-of-service (DDoS) and false data injection (FDI). Central to our approach is a novel data structure optimized for time series data, capturing key information such as packet timing and data payload distribution. Leveraging decision trees, a robust machine learning technique, enables effective anomaly detection and prediction. Additionally, we integrate data compression techniques to reduce device memory usage while maintaining detection accuracy. Our evaluation results demonstrate minimal overhead in packet processing speed with 1 to 20 nanoseconds differences per packet, and enhanced data storage efficiency with compression ratios reaching up to 60.9%. Despite these optimizations, there is only a slight decrease in detection accuracy, such as a 2.81% drop in detecting false data injection attack (FDIA).

关键词： Data Compression Distributed Network Protocol (DNP3) Network Security P4 Programmable Network Supervisory Control and Data Acquisition (SCADA) System Smart grid

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A Day-ahead Photovoltaic Power Generation Prediction Method Based on Data Mining and Micro-meteorological Information 6

A Day-ahead Photovoltaic Power Generation Prediction Method ...

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6th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2024

作者： Ye, Xuanrong Ye, Jinkun Liang, Guixin Guangdong Power Grid Corporation Limited Yunfu Power Supply Bureau Yunfu China

ISBN: (纸本)9798350374315

With the rapid development of renewable energy, photovoltaic power generation has become a current research hotspot. This paper proposes a photovoltaic power generation forecasting method and system based on data mining and micrometeorological information. In the context of day-ahead short-term and ultra-short-term photovoltaic power generation forecasting applications, an improved similar day theory based on time factors and weather factors is used, and the data The preprocessing model applies the OPTICS clustering method to obtain the day-ahead photovoltaic power generation timing sequence prediction results for a certain period. The simulation results show that the method proposed in this article can effectively improve the accuracy of photovoltaic prediction. © 2024 ieee.

关键词： Data mining Day-ahead forecast Micrometeorological factors Photovoltaic power generation

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An Auditable and efficient Prepaid Scheme with Privacy Preservation in Smart grids

An Auditable and Efficient Prepaid Scheme with Privacy Prese...

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ieee international conference on Big Data and Smart computing (BigComp)

作者： Sui, Zhiyuan Li, Jing Cent Univ Finance & Econ Informat Sch Beijing Peoples R China

ISBN: (纸本)9781665475785

existing security and privacy preserving schemes have too much wastage in token-based prepaid smart grids. In this paper, we propose a novel token concept and construct a security and privacy preserving prepaid scheme based on the power request model. In the proposed scheme, honest consumers keep their anonymity, while consumers who send used tokens can be tracked for law enforcement and maintenance purposes. In addition to security and privacy preservation, the computational cost of verification is independent of the number of tokens based on the novel token concept in the power request model. Therefore, the number of points can be the basic unit in order to minimize the wastage. Finally, we prove the security features and demonstrate the performances of our scheme.

关键词： smart grid anonymity privacy cybersecurity

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Strategies and Practices of edge computing in Real-Time Optimization and Control of Smart Distribution Networks

Strategies and Practices of Edge Computing in Real-Time Opti...

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2024 international conference on New Power System and Power electronics, NPSPe 2024

作者： Ge, Chang Tianjin University of Technology Tianjin300100 China

ISBN: (纸本)9798350389869

Smart grid technology is advancing bravely in the tide of the development of the times, and edge computing plays an increasingly prominent role in the real-time optimal control of smart distribution networks. This article discusses how edge computing technology is applied to intelligent distribution network to improve its operation efficiency and stability, describes the design of edge computing architecture, the implementation of real-time optimization control strategy, and the construction of system integration and simulation environment. This article verifies the performance improvement of edge computing in resource utilization and failure rate through experiments, and compares it with the traditional centralized control method. The experimental results show that edge computing technology significantly improves the resource utilization rate of distribution network, reaching 99.7%. The experimental data of voltage deviation shows that edge computing can maintain a low level of voltage deviation, and the average voltage deviation is only 0.61V, which ensures the stability and reliability of the power grid. These results prove the potential of edge computing technology in improving the performance of smart distribution networks, and provide strong technical support and strategic guidance for the development of smart grids in the future. © 2024 ieee.

关键词： Power distribution networks

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Cybersecurity strategies for protecting smart grid in Residential building complex from attacks 2

Cybersecurity strategies for protecting smart grid in Reside...

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2nd ieee international conference on Advancements and Key Challenges in Green energy and computing, AKGeC 2024

作者： Pritish, V. Kumar, Praneeth Annapurani, K. Choudhary, Anirban Srm Institute of Science and Technology Department of Networking and Communications Tamilnadu Kattankulathur India

ISBN: (纸本)9798350390179

The addition of digital technology to energy systems, smart grids have been created, which make power delivery more reliable, efficient, and long-lasting. But this progress comes with big security risks, especially in apartment buildings with lots of smart devices and monitors that are all connected to each other. This paper looks at important cybersecurity methods that can be used to protect smart grid infrastructure in residential settings from cyberattacks. Some of the main goals are to protect important assets, keep data private and secure, make sure communication networks are safe, and make sure systems can handle attacks. Strong access controls, ongoing monitoring, and effective incident response methods are all part of the suggested procedures that put a high focus on lowering risks and keeping the smart grid running. The study also stresses how important it is to follow rules, teach users, and encourage smart grid components to work together. By following these thorough cybersecurity steps, smart grid security and dependability in homes may be kept, which will eventually lead to a safer and more resilient energy environment. © 2024 ieee.

关键词： Power grids

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efficiency electrical Power edge-end computing with Patch-based Low-rank Transformer

Efficiency Electrical Power Edge-end Computing with Patch-ba...

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2024 international conference on energy and electrical engineering, eee 2024

作者： Zhou, Mi Qian, Bin Lin, Xiaoming Zhang, Fan Luo, Yi Wu, Binquan Electric Power Research Institute Csg Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China Guangdong Provincial Key Laboratory of Intelligent Measurement and Advanced Metering of Power Grid Guangzhou China

ISBN: (纸本)9798350341768

The development of communication technology has made the need to build more reliable and efficient smart power grid systems imminent. The emergence of edge computing has significantly alleviated the pressure of data transmission in systems, enhancing the reliability and stability of cloud-edge based smart grid systems. However, the deep algorithms on which current edge computing platforms rely increase the overall computation time consumption and memory consumption of the whole system, which is inconsistent with the responsiveness and reliability requirements of smart power grid system services. Therefore, in this paper, we propose Patch-based Low-rank Transformer (PLT) that balances accuracy and efficiency, which can greatly alleviate the computation time consumption and memory consumption of edge computing platforms. Specifically, we cut the original electric power time series into subsequence-level patches to ensure the accuracy of the model for long and short-term sequential dependencies learning. Then, we introduce a low-rank attention mechanism to reduce the computational consumption of the model, and design a simple but efficient knowledge distillation module that transferring knowledge from the Transformer-based model to a lightweight MLP to speed up the computation of the model at inference phrase. extensive experiments on 11 public datasets (10 UCR and 1 cluster-trace-v2018 released by Alibaba Cloud) and 13 collected electric power time series datasets show that the proposed PLT outperforms baseline models and reaches the design purpose. © 2024 ieee.

关键词： Power systems computer aided design

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Distributionally Robust Planning for Renewable Sources and Soft Open Points With energy Storage under Multiple Uncertainties 4

Distributionally Robust Planning for Renewable Sources and S...

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4th international conference on electrical engineering and Control science, IC2eCS 2024

作者： Cao, Huazhen Xu, Min Guo, Zuogang Power Grid Planning Research Center China Southern Power Grid Co. Ltd Guangdong Power Grid Co. Ltd Guangzhou China Csg Guangdong Electric Power Research Institute Guangzhou China

ISBN: (纸本)9798331510107

With the rapid development of flexible interconnection technology in active distribution networks, many power electronic devices have been employed to improve system operational performance. As a novel fully-controlled power electronic device, energy storage integrated soft open point (e-SOP) is gradually replacing traditional. While e-SOP can bring significant operational benefits, its high cost will also affect its promotion and use in the distribution network. Thus, this paper proposes a coordinated planning method for DGs and e-SOPs. First, the structure and modelling of e-SOP is proposed. Secondly, based on this framework, the distributionally robust Co-optimization of the DG and e-SOP is established, Finally, case studies are conducted on ieee 33-node system to prove the effectiveness of the proposed method. © 2024 ieee.

关键词： Power distribution planning

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Utilizing Blockchain Technology for Financial Services in Parallel, Distributed, and grid computing Frameworks

Utilizing Blockchain Technology for Financial Services in Pa...

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2024 international conference on computing and Intelligent Reality Technologies, ICCIRT 2024

作者： Chokkamreddy, Prakash Palagati, Anusha Rao, Akurathi Lakshmi Pathi Maheswari, P. Mahadevan, Vanitha Balan, Santhosh Kumar School of Management Studies Guru Nanak Institutions Technical Campus Ibrahimpatnam Hyderabad India Dept. of Computer Science and Engineering Guru Nanak Institute of Technology Ibrahimpatnam Hyderabad India Department of Computer Applications KSR College of Arts and Science for Women Tiruchengode India College of Engineering and Technology University of Technology and Applied Sciences Al Mussanah Oman

ISBN: (纸本)9798331510299

Application of blockchain in financial services has opened new ways of efficiency in transaction processing, assets management and security. The application of parallel, distributed, and grid computing with blockchain in improving the functionality of financial services like real time financial transactions, high frequency trading, portfolio and risk analysis is examined in this paper. It has been ascertained that namely parallel and distributed computing together with blockchain are the most suitable for facing increasing needs in data in the sphere of financial services. It also investigates the use of grid computing frameworks and their effects on the correspondingly balanced flows of medium amongst multiple systems. Various issues such as security, privacy violations, and adherence to the law are also reviewed, and existing prospects for developing through smart contracts and consensus mechanisms using blockchain. exploring how some of the most successful pilot use-cases of blockchain are applicable for future growth in the financial services industry offers insights into how blockchain will soon enter mainstream as a disruptive technology in the broader global financial sector. © 2024 ieee.

关键词： Smart contract

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Prediction of Transformer Oil Temperature Based on Feature Selection and Deep Neural Network 15

Prediction of Transformer Oil Temperature Based on Feature S...

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15th ieee international conference on Software engineering and Service science (ICSeSS)

作者： Chen, Yongxin Shiping, e. Du, Zhenan Zhang, Kanjun Zhang, Gaofeng State Grid Hubei Elect Power Res Inst Wuhan Hubei Peoples R China State Key Lab Adv Electromagnet Engn & Technol HU Wuhan Hubei Peoples R China State Grid Hubei Elect Power Co Ltd Wuhan Hubei Peoples R China NR Elect Co Ltd Nanjing Jiangsu Peoples R China

ISBN: (纸本)9798350389166;9798350389173

The prediction of transformer oil temperature is a time series prediction task, which is of great significance for the maintenance of substation equipment. In the process of multivariate prediction, not all variables are strongly correlated with the prediction of the transformer oil temperature. This paper proposes a transformer oil temperature prediction method based on Feature Selection and Smooth Residual blocks, named FSSR. A two-stage feature selection method is applied to select the related features. Residual connection is applied to convolutional neural network in smooth residual blocks. experimental study is performed on the eTTh1 and eTTh2 datasets. With different number of selected features, the best one can reduce the average MSe (mean squared error) by at least 20% than the worst one. Compared to the baseline models, FSSR has the best accuracy. Compared with LSTM (Long Short-Term Memory network), the average MSe of FSSR decreased by 27.62% on the eTTh1 dataset, and 39.34% on the eTTh2 dataset.

关键词： oil temperature prediction feature selection convolutional neural network

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