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共 17266 条记录，以下是471-480 订阅

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enhancing Privacy Strategy in New Power Systems: Problem Assessment, Comparative Analysis and Suggestions 5th

Enhancing Privacy Strategy in New Power Systems: Problem Ass...

引用

5th international conference on Big Data and Security (ICBDS)

作者： Chen, Li Yao, Xuyan Zhang, Xin Cao, Yuxin Su, Jiahe Tian, Yuan Nanjing Inst Technol Nanjing 211167 Jiangsu Peoples R China Univ Oulu Sch Software Engn Oulu 90014 Finland

ISBN: (纸本)9789819743896;9789819743902

This paper meticulously examines the privacy challenges stemming from the escalating volume of data within the new power systems. It thoroughly explores various privacy computing techniques, including encryption and statistical security, with a particular emphasis on applications such as homomorphic encryption, federated learning and differential privacy within the power grid. The analysis underscores the pivotal role of privacy computing in addressing privacy-related concerns within the power industry, particularly focusing on issues like data transmission security and multi-party collaboration. Furthermore, the paper proposes innovative solutions aimed at bolstering privacy protection, fostering data sharing initiatives, and enhancing cost-effectiveness through the strategic implementation of privacy computing technologies. Recognizing the paramount importance of understanding privacy protection in the context of the digital transformation of power systems, this paper seeks to provide valuable insights to guide further research and application of privacy computing. Ultimately, these insights aim to facilitate secure data circulation within the energy sector.

关键词： New power systems Private computing Homomorphic encryption Federated learning Differential privacy

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Research on Network Intrusion Risk and Behavior Detection Methods for Power grid Information Physical System 11

Research on Network Intrusion Risk and Behavior Detection Me...

引用

11th Joint international Information Technology and Artificial Intelligence conference, ITAIC 2023

作者： Wang, Wei Sun, Qiang Mei, Wenming Zhang, Xiao Liu, Xin State Grid Electric Power Research Institute China State Grid Shandong Electric Power Research Institute China State Grid Corporation of China China

ISBN: (纸本)9798350333664

With the application of new technologies such as 'cloud computing, Big data, the Internet of Things, Artificial intelligence, 5G' in the power system and the development of smart grid, the multi stream integration of energy flow, business flow, and data flow, the interaction between the power internal network and the internet is becoming more frequently, and the boundaries of the power system network are constantly extending, shifting from closed to open, which will bring unprecedented network security risks and challenges. Traditional intrusion detection methods of information systems isn't suitable for power grid information physical system, and research on intrusion risk analysis for power grid information physical system is few currently. Therefore, according to the operation characteristics of the Power grid information physical system, this paper deeply analyzes the network intrusion risk of the power industrial control system, and summarizes the possible types of network attacks of the Power grid information physical system. Based on the risk types analyzed, this article proposes an intrusion detection method suitable for power grid information physical system, which also provides support for subsequent network tracing. © 2023 ieee.

关键词： Attack detection Network intrusion Power grid information physical system Power industry control protocol Risk analysis

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High-performance Distributed Feature Storage and Consumption Services in Power grid Scenarios 8

High-performance Distributed Feature Storage and Consumption...

引用

8th ieee international conference on Big Data Security on Cloud, 8th ieee international conference on High Performance and Smart computing, and 8th ieee international conference on Intelligent Data and Security, BigDataSecurity/HPSC/IDS 2022

作者： Wang, Guoqing Lu, Yiming Liu, Peng Zhou, Wang State Grid Shanghai Energy Interconnection Research Institute Beijing China Peking University Beijing China

ISBN: (纸本)9781665480697

As more and more services in the power grid introduce machine learning and deep learning technologies, feature engineering has become more and more complex. In order to improve the efficiency of feature engineering, this paper proposes a high-performance distributed feature storage and consumption service based on grid scenarios, referred to as HFSA (High-performance feature storage and access). HFSA designed a feature data storage and consumption platform with the help of the idea of message middleware. HFSA services include HFSA Cluster, Producer and Consumer. This article verifies whether the HFSA service meets the design requirements from three aspects: functional testing, accuracy testing and performance testing. The experimental results show that the HFSA service has complete functions and good performance, which basically solves the problem of characteristic storage and consumption of the power grid. © 2022 ieee.

关键词： Middleware

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Data-Driven Modeling of WPT Systems with Secondary Voltage Control 5

Data-Driven Modeling of WPT Systems with Secondary Voltage C...

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5th ieee international conference on Power, Intelligent computing and Systems, ICPICS 2023

作者： Wu, Xiaorui Xiao, Jing Wu, Ning Gong, Wenlan Mo, Yuhong Duan, Chanzhen Electric Power Research Institute of Guangxi Power Grid Co. Ltd Southern Power Grid Corporation Wireless Power Transmission Joint Laboratory Nanning China School of Automation Chongqing University Chongqing China

ISBN: (纸本)9798350333442

Dynamic models are of great importance for the analysis, design, and control of wireless power transfer (WPT) systems. This study concentrates on the WPT system with active rectifiers on the secondary side, and uses a data-driven modeling method to construct a correlation between the load voltage and the phase shift angle associated with the active rectifier. The data-driven modeling method only requires sampling the system's input and output signals, eliminating the necessity for a complete understanding of the system's internal operational mechanisms. The model obtained by this modeling method can not only be used for system dynamic analysis, but also provide reference for controller design. Finally, the effectiveness of the model obtained by this method was verified through simulation on the MATLAB platform. © 2023 ieee.

关键词： energy transfer

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GFL-GFM Dual-Mode Predictive Control of Power Converters for Stiffness-Varying Power grids 2

GFL-GFM Dual-Mode Predictive Control of Power Converters for...

引用

2nd ieee international Power electronics and Application Symposium, PeAS 2023

作者： Huang, Sijia Zhang, Zhenbin Xing, Qianli Yin, Yafei Li, Zhen School of Electrical Engineering Shandong University Jinan China

ISBN: (纸本)9798350313765

As the penetration of the offshore wind energy increases rapidly, the impedance of offshore collector fluctuates more widely, which leads to significant grid stiffness variations. This raises large challenges to system stability and power integration efficiency of grid-tied power converters, adopting classical grid-following control techniques, which perform properly solely when grid stiffness is high. In this work, a grid-following and grid-forming dual-mode predictive control for grid-tied power converters is proposed. The proposed method is more suitable for large changes of grid impedance, i.e., large grid stiffness variations. Finally, the proposal's validity has been substantiated by the simulation data. © 2023 ieee.

关键词： grid stiffness varying grid-following-and-forming dual mode grid-forming control grid-tied power converters predictive control wind energy integration

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Research on Online evaluation and Optimization of Power Supply Reliability Considering electric Vehicle Based Virtual energy Storages 5

Research on Online Evaluation and Optimization of Power Supp...

引用

5th international conference on Power engineering, ICPe 2024

作者： Zhu, Liang Xu, Jian Yu, Qingguang Yao, Xin Liu, Ding Yuan, Leidong Guo, Min Li, Xiaoyu State Grid Beijing Chaoyang Electric Power Supply Company Beijing China Tsinghua University Dept. of E.E. Beijing China

ISBN: (纸本)9798331531140

electric vehicle is one of the important ways to achieve the goal of 'dual carbon'. The disorderly charging behavior of eV has strong randomness in time and space, and its charging load will change the variation trend of daily load, which will affect the reliability of distribution network. This paper first analyzes the influence of eV on the reliability, and puts forward a reliability evaluation model of distribution network considering eV. Then, an online evaluation method of distribution network reliability based on real-time data is proposed from the two aspects of real-time operating status and reliability online evaluation efficiency. The power supply reliability index is obtained by loop search method based on load point, and finally verified by simulation. © 2024 ieee.

关键词： Power distribution reliability

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Navigating grid Dynamics with V2G Technology and Naive Bayes Optimization in IoT Systems 3

Navigating Grid Dynamics with V2G Technology and Naive Bayes...

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3rd international conference for Innovation in Technology, INOCON 2024

作者： Arulprakash, M. Raman, Ramakrishnan Gokhale, Atul A. Saravanan, K. Ishwarya, M.V. Sujatha, S. Srm Institute of Science and Technology Department of Computing Technologies Chennai Tamil Nadu Kattankulathur India Maharashtra Pune India Symbiosis School of Culinary Arts Maharashtra Pune India R.M.D. Engineering College Department of Information Technology Tamil Nadu Chennai India Agni College of Technology Artificial Intelligence and Data Science Department Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Saveetha University Saveetha School of Engineering Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350381931

Vehicle-to-grid (V2G) technology and Naive Bayes optimization, which might help IoT devices, navigate grid dynamics. electric vehicles (eVs) with bidirectional charging capability may provide grid assistance using their energy storage capacity. These cars can take and return energy from the grid using V2G technology, stabilizing the system. This paper proposes a unique technique to intelligently control energy flow between the grid and eVs using V2G technology and Naive Bayes optimization algorithms. Using previous data on energy consumption, grid circumstances, and vehicle charging behavior, the Naive Bayes algorithm makes real-time predictions and decisions. In IoT-enabled smart grids, this connection improves energy management efficiency and reliability. The implementation methods and experimental findings of the suggested strategy are discussed in the study. The system grid stability, energy usage, and grid dynamics management are also assessed. The results show that V2G technology and Naive Bayes optimization in IoT systems can intelligently navigate grid dynamics, creating a more sustainable and resilient energy infrastructure. To smart grid expertise and shows how V2G-enabled IoT devices may optimize energy distribution and ensure grid stability in the face of dynamic energy needs. © 2024 ieee.

关键词： Digital storage

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Joint Convolutional Neural Network and Bidirectional Gated Recurrent Unit Model for False Data Injection Attacks Detection in Smart grid 11

Joint Convolutional Neural Network and Bidirectional Gated R...

引用

11th international conference on Power and energy Systems engineering, CPeSe 2024

作者： Sun, Mengna Pan, Huan Na, Chunning Li, Hu Ningxia University School of Electronic and Electrical Engineering Yinchuan China

ISBN: (纸本)9798350377439

False data injection attack (FDIA), as a kind of covert cyber attack, can be secretly tampered with measurement data to steal power or destabilize the operation of the smart grid. With the upgrading of monitoring equipment, the amount of data in the smart grid is getting bigger, and the feature vectors are getting more and more complex. To further improve the detection performance of FDIA, this paper combines convolutional neural network (CNN) with bidirectional gated recurrent unit (Bi-GRU) to construct a new hybrid detection model. The model integrates the features of CNN and Bi-GRU can better extract data features, i.e., CNN adopts a layer-by-layer structure to extract data features, and Bi-GRU acts as a bi-directional network to capture both past and future information, and ultimately outputs the hidden state at each time step. We take a small smart grid as an example to simulate and verify the FDIA detection effect of different models, where the dataset comes from the phasor measurement units. The results show that for varying levels of attack intensity, CNN-Bi-GRU model consistently maintains high detection accuracy. © 2024 ieee.

关键词： Convolutional neural networks

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Real-Time Scheduling for energy Optimization: Smart grid Integration with Renewable energy 7

Real-Time Scheduling for Energy Optimization: Smart Grid Int...

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7th international conference on Contemporary computing and Informatics, IC3I 2024

作者： Gopalakrushna, A. Raj, Vijilius Helena Vani, V. Divya Singh, Navdeep Jethabhai, Patel Yogeshkumar Chakravarthi, M. Kalyan Vignana Bharathi Institute of technology Telangana Hyderabad India New Horizon College of Engineering Department of Applied Sciences Karnataka Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Sankalchand Patel University Faculty of Computer Science Shri C.J. Patel College of Computer Studies Gujarat Visnagar India University of Technology and Applied Sciences Department of Electronics and Communication Engineering Muscat Oman

ISBN: (纸本)9798350350067

Reliable power system operations, cost reduction, demand and supply balance maintenance, and efficient load scheduling all depend on real-time energy optimisation. Nevertheless, the unpredictability of electric loads and renewable energy sources (ReS) makes real-time energy optimization difficult. As a result, the energy Management Controller (eMC) was developed, which can automatically react to DR signals and resolve energy-related issues. In light of this, eDGe, a hybrid algorithm combining elements of genetic algorithm (GA) and enhanced differential evolution (eDe), is devised in this work. The eDGe algorithm is used to train the eMC to automatically react to DR signals and schedule the 3 classes of household loads - hybrid, non-interruptible, and interruptible - to handle energy management issues. The results show that in terms of power cost reduction, minimizing carbon release, PAR relief, and client discomfort maximization, the suggested eDGe algorithm performs better than benchmark methods. © 2024 ieee.

关键词： energy optimization renewable energy Smart grid

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Asynchronous Decentralized Bayesian Optimization for Large Scale Hyperparameter Optimization 19

Asynchronous Decentralized Bayesian Optimization for Large S...

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19th ieee international conference on e-science, e-science 2023

作者： egelé, Romain Guyon, Isabelle Vishwanath, Venkatram Balaprakash, Prasanna Université Paris-Saclay France Argonne National Laboratory United States Lisn U. Paris-Saclay France Google United States Argonne National Laboratory Leadership Computing Facility United States Oak Ridge National Laboratory Computing and Computational Sciences United States

ISBN: (纸本)9798350322231

Bayesian optimization (BO) is a promising approach for hyperparameter optimization of deep neural networks (DNNs), where each model training can take minutes to hours. In BO, a computationally cheap surrogate model is employed to learn the relationship between parameter configurations and their performance such as accuracy. Parallel BO methods often adopt single manager/multiple workers strategies to evaluate multiple hyperparameter configurations simultaneously. Despite significant hyperparameter evaluation time, the overhead in such centralized schemes prevents these methods to scale on a large number of workers. We present an asynchronous-decentralized BO, wherein each worker runs a sequential BO and asynchronously communicates its results through shared storage. We scale our method without loss of computational efficiency with above 95% of worker's utilization to 1,920 parallel workers (full production queue of the Polaris supercomputer) and demonstrate improvement in model accuracy as well as faster convergence on the CANDLe benchmark from the exascale computing project. © 2023 ieee.

关键词： Supercomputers

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