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2nd international conference on Smart grids and Energy Systems, SGES 2023

作者： Ren, Xuesong Chen, Ke Chen, Qiudi You, Qilin Gan, Yufang Liu, Qianwen State Grid Chongqing Information and Telecommunication Company Chongqing400012 China

ISBN: (纸本)9798350371659

In this paper, firstly, the business characteristics of the distribution network are sorted out, and the demand of power business for 5G communication is analyzed. Thus, a 5G distribution scheme is established. On this basis, a distributed distribution differential protection scheme based on 5G edge calculation is proposed for the monitoring ability of distributed power supply and differential protection of the distribution network. In this scheme, the multi-access edge computing server and distributed power supply monitoring are used. The monitoring information unloading model of differential protection with joint communication and calculation is designed. The delay optimization problem of differential protection under the constraints of computing power and power is established, and a solution is proposed by combining the alternating optimization algorithm. The time delay of differential protection tasks under different conditions is simulated and analyzed. The results show that the scheme can effectively reduce the time delay of differential protection. © 2023 IEEE.

关键词： Edge computing

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parallel computing Optimization of Tourism Big Data based on Crawler Programs 1

Parallel Computing Optimization of Tourism Big Data based on...

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1st international conference on Intelligent Systems and Computational Networks, ICISCN 2025

作者： Zhou, Xiaoyu Wuhan Donghu University School of Management Hubei Wuhan China

ISBN: (纸本)9798331529246

With the rapid development of the tourism industry, traditional tourism methods are undergoing significant transformation, and online tourism is gradually becoming a new highlight in the market. However, faced with the challenges of big data, online tourism data experiences tremendous pressure in terms of storage, computation, and management, and the issue of information overload seriously affects the user experience. Therefore, there is an urgent need for innovative platforms and recommendation algorithms to optimize the screening and recommendation of tourism information. This paper proposes an optimization scheme based on the Spark cloud computing platform to improve the efficiency of tourism data processing. First, utilizing Spark's distributed computing and memory management capabilities, tourism data can be stored and processed in a distributed manner, thus accelerating the computation speed of recommendation algorithms. Secondly, a Python-written crawler program is used to obtain real tourism data, including user ratings and comments on attractions, to evaluate the effectiveness of the improved algorithm. Finally, a weighted algorithm combining the LDA topic model is proposed. This algorithm analyzes the topic distribution of comment texts and weights the comments based on user ratings, calculating the predicted score for unrated attractions for the target user, thereby achieving accurate attraction recommendations. The experimental results show that the algorithm proposed in this study demonstrates significant advantages in prediction accuracy. © 2025 IEEE.

关键词： Cloud platforms

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distributed Robust Optimization Method for AC/MTDC Hybrid Power Systems with DC Network Cognizant

Distributed Robust Optimization Method for AC/MTDC Hybrid Po...

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international conference on Smart Energy Systems and Technologies (SEST) - Driving the Advances for Future Electrification

作者： Li, Haixiao Lekic, Aleksandra Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Netherlands Chongqing Univ Technol Sch Elect & Elect Engn Chongqing Peoples R China

ISBN: (纸本)9798350386509;9798350386493

AC/ multi-terminal DC (MTDC) hybrid power systems have emerged as a solution for the large-scale and long-distance accommodation of power produced by renewable energy systems (RESs). To ensure the optimal operation of such hybrid power systems, this paper addresses three key issues: system operational flexibility, centralized communication limitations, and RES uncertainties. Accordingly, a specific AC/DC optimal power flow (OPF) model and a distributed robust optimization method are proposed. Firstly, we apply a set of linear approximation and convex relaxation techniques to formulate the mixed-integer convex AC/DC OPF model. This model incorporates the DC network-cognizant constraint, enabling DC topology reconfiguration. Next, generalized Benders decomposition (GBD) is employed to provide distributed optimization. Enhanced approaches are incorporated into GBD to achieve parallel computation and asynchronous updating. Additionally, the extreme scenario method (ESM) is embedded into the constructed AC/DC OPF model to provide robust decisions to hedge against RES uncertainties. ESM is further extended to align the GBD procedure. Numerical results are finally presented to validate the effectiveness of our proposed optimization method.

关键词： Multi-terminal DC grid DC network cognizance distributed robust optimization generalized benders decomposition extreme scenario method

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Interleaved Zero-CMV Zero-DMV PWM for parallel Operation of Two Three-level Converters

Interleaved Zero-CMV Zero-DMV PWM for Parallel Operation of ...

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2023 IEEE international conference on Power Electronics, Smart grid, and Renewable Energy, PESGRE 2023

作者： Durga Nair, S. Arun Rahul, S. Indian Institute of Technology Electrical Engineering Kerala Palakkad India

ISBN: (纸本)9798350310573

Interleaved operation of parallel converters are preferred for better grid current quality but it contribute to high frequency circulating current flow. Space vector modulation (SVM) methods are developed for minimising circulating currents and other parameters while guaranteeing interleaved operation. This work proposes a four-vector interleaved SVM for two three-level (3l) flying capacitor (FC) converters in parallel for eliminating both common-mode voltage (CMV) and differential mode voltage (DMV). The zero CMV vectors of a single 3l-FC alone are utilised for reference generation. Zero DMV also eliminates the zero-sequence circulating current flow. The switching state redundancy of '0 'state of 3l-FC is utilised for voltage balancing in the flying capacitors. With the interleaved SVM, the parallel operation of two 3l converters is equivalent to a single five-level converter. Simulation analysis using MATLAB/Simulink is provided to substantiate the efficacy of the proposed interleaved four-vector SVM. © 2023 IEEE.

关键词： circulating current common-mode voltage interleaving parallel converters

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distributed computing and Data Processing Technology under the Power Data Middleware Architecture 3

Distributed Computing and Data Processing Technology under t...

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2023 IEEE 3rd international conference on Data Science and Computer Application, ICDSCA 2023

作者： Zou, Yuelin Shen, Jia Yin, Rui Guo, Jiangtao Guo, Yi State Grid Xinjiang Electric Power Co. Ltd. Information and Communication Company Urumqi830002 China State Grid Xinjiang Electric Power Co. Ltd. Urumqi830002 China State Grid Info-Telecom Great Power Science and Technology Co. Ltd Fuzhou350001 China

ISBN: (纸本)9798350341546

With the establishment and development of integrated monitoring platforms and communication information platforms for intelligent substations, the data volume of the power system is showing explosive growth. However, existing data centers have poor processing capabilities for massive amounts of data, making it difficult to meet the multiple service quality requirements of users. How to effectively manage massive and complex data, fully utilize the value of data, improve service quality, reduce operational costs, and achieve rapid response to users is an urgent problem to be solved. Therefore, based on the actual needs of power enterprises, this article proposed a cloud computing power data middleware architecture based on distributed technology, which effectively managed, cleaned, converted, and evaluated power data sources through cloud computing platforms. This article mainly analyzed the shortcomings of traditional power data processing platforms and the feasibility of cloud computing based power data platforms. Finally, through load balancing bias comparison and pressure testing experiments under different task numbers, it was proven that cloud computing systems outperformed traditional power data processing systems in terms of utility value, completion time, and load balancing. Moreover, in the pressure testing experiment, under the condition of 10 concurrent users and 673 system request transactions, 91.3% of the interface response time in the cloud computing system was within 50ms. Therefore, cloud computing systems have certain advantages in processing power data. © 2023 IEEE.

关键词： Cloud computing Data Center System Data Processing distributed computing Electricity Data

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Network Performance Analysis of Smart grid Communications Over LTE cat-M 14

Network Performance Analysis of Smart Grid Communications Ov...

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14th IEEE international conference on Communications, Control, and computing Technologies for Smart grids, SmartgridComm 2023

作者： Dash, Biswajit Kumar Shah, Sureel Malandra, Filippo State University of New York at Buffalo Department of Electrical Engineering NY United States University of California Department of Electrical and Computer Engineering San DiegoCA United States

ISBN: (纸本)9781665455541

Smart grid applications heavily rely on communication infrastructures that offer flexibility, scalability, and cost-effectiveness to enable bi-directional information exchange across geographically distributed grid elements. Wireless cellular networks, such as LTE cat-M, provide extensive coverage at a reduced cost, both in terms of installation and power consumption. This paper presents a multi-node testbed to assess the suitability of LTE cat-M technology for a variety of smart grid applications, with distributed nodes collecting and transmitting data at a variable rate, from 0.25 to 100 frames per second (fps). Based on field experiments, an extensive performance analysis is presented with Key Performance Indicators (KPIs), such as delay, jitter, and frame loss. The impact of the number of smart grid nodes and propagation quality are also considered in the analysis. Finally, a calibration method to optimize the packet transmission time is presented with a 33.91% delay reduction under different conditions. © 2023 IEEE.

关键词： Network performance

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A two-layer frequency control method for large-scale distributed energy storage clusters

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international JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2022年 143卷

作者： Lin, Yujun Li, Xing Zhai, Baoyu Yang, Qiufan Zhou, Jianyu Chen, Xia Wen, Jinyu Huazhong Univ Sci & Technol Sch Elect & Elect Engn State Key Lab Adv Electromagnet Engn & Technol Wuhan 430074 Peoples R China State Grid Xinjiang Elect Power Co LTD State Grid Xinjiang Elect Power Res Inst Xinjiang Key Lab Whole Proc Simulat Power Syst Urumqi 830011 Peoples R China

A large number of small-capacity distributed energy storages (ESs) are expected to play an important role in grid frequency regulation. However, without suitable control scheme, it may lead to heavy computational burden and low utilizing efficiency. In this paper, a two-layer frequency control framework is proposed for large-scale distributed ESs that can be separated into several ES clusters. On the upper layer, the frequency regulation is achieved through distributed model predictive control (DMPC) among different ES clusters. The parallel computing of the distributed controllers reduces the optimization problem dimension brought by a large number of ES clusters. In addition, both the frequency deviation and rate of change of frequency are considered to achieve better frequency regulation. On the lower layer, state-of-charge (SoC) balance is considered within each cluster by the leader-follower consensus algorithm in a distributed mode, and it only requires communication between neighboring ESs in a cluster to aggregate for the upper layer. Unlike the conventional SoC balancing strategy, circulating currents are prevented to reduce the transmission losses. Moreover, a detailed small-signal model is established to show the impact of the proposed control strategy on the system stability. The effec-tiveness of the entire control scheme and the small-signal analysis are validated under a variety of scenarios by simulations. (C) 2017 Elsevier Inc. All rights reserved.

关键词： Frequency regulation distributed model predictive control (DMPC) Consensus algorithm Energy storage systems

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Prediction and Analysis of Seepage Flow of UpperReservoir of a Pumped Storage Power Station Based on Bi-LSTM 24

Prediction and Analysis of Seepage Flow of UpperReservoir of...

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2024 international Academic conference on Edge computing, parallel and distributed computing, ECPDC 2024

作者： Chen, Guofeng Li, Guangkai Ma, Fangxiao Zhejiang Huadong Surveying and Mapping and Engineering Safety Technology Co. Ltd Hangzhou China North China Development and Construction Branch State Grid Xinyuan Group Co. Ltd. Tianjin China

ISBN: (纸本)9798400718168

The seepage flow of concrete face rockfill dam is the key technical index to feedback the safety performance of the dam body. How to accurately predict and analyze it is a very important research topic. By taking the seepage behind the dam of a pumped storage power station as the research object, this paper introduced a Bi-directional long-short-term memory neural network model (Bi-LSTM). Bi-LSTM could simultaneously consider the influence of the seepage change law before and after the research time node on the prediction results so that the influential factors of different time nodes on the seepage flow can be accurately simulated. Based on the basic principles of dam seepage, a statistical mathematical model of dam seepage flow composed of water pressure component, temperature component, rainfall component and time effect component was established. By taking 18 groups of environmental components as input variables and the seepage flow of the dam body as the training target, a seepage prediction model based on Bi-LSTM neural network model was established. The case study showed that the predicted dam seepage values by Bi-LSTM in this paper have higher correlation coefficient R with the site measured values, and the root mean square error (RMSE) and the mean absolute error (MAE) are superior compared with the traditional RNN model. This method has certain scientific research and engineering application value for future research on the prediction and analysis of seepage flow of concrete face rockfill dams. © 2024 ACM.

关键词： Seepage

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Design and Implementation of a Cross-Platform Real-Time Power System Simulation Software for the New power system 4

Design and Implementation of a Cross-Platform Real-Time Powe...

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

作者： Huang, Ying Lu, Yuanhong Zhang, Jingyue Zhan, Jie Zhao, Yanjun Hu, Yufeng China Southern Power Grid State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid Guangzhou Guangzhou510663 China China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System Guangzhou Guangzhou510663 China China Southern Power Grid National Energy Power Grid Technology R & D Centre Guangzhou Guangzhou510663 China China Southern Power Grid Guangzhou CSG Key Laboratory for Power System Simulation Electric Power Research Institute Guangzhou510663 China

ISBN: (纸本)9798331532765

With the rapid increase in renewable energy installation nodes and the substantial enhancement of switching frequencies in the new power system, this paper proposes a design scheme for a cloud simulation software that employs the Latency Insertion Method (LIM) to decompose large-scale new power system models into several subsystems, enabling multi-platform integrated parallel computing. The self-developed platform meets the demands of ultra-large simulation scales, ultra-small simulation time steps, and massive simulation computations in power systems. Experimental results demonstrate that the accelerated simulation technology, which integrates model segmentation using LIM with multi-core parallel execution, can enhance the simulation speed by up to 858 times while maintaining simulation accuracy. This significantly improves simulation efficiency and is suitable for large-scale simulations of new power systems. © 2024 IEEE.

关键词： Power systems computer aided design

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Network-Offloaded Bandwidth-Optimal Broadcast and Allgather for distributed AI 24

Network-Offloaded Bandwidth-Optimal Broadcast and Allgather ...

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2024 international conference for High Performance computing, Networking, Storage and Analysis

作者： Khalilov, Mikhail Di Girolamo, Salvatore Chrapek, Marcin Nudelman, Rami Bloch, Gil Hoefler, Torsten Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland NVIDIA Corp Zurich Switzerland NVIDIA Corp Santa Clara CA USA NVIDIA Corp Yokneam Illit Israel

ISBN: (数字)9798350352917

ISBN: (纸本)9798350352924;9798350352917

In the Fully Sharded Data parallel (FSDP) training pipeline, collective operations can be interleaved to maximize the communication/computation overlap. In this scenario, outstanding operations such as Allgather and Reduce-Scatter can compete for the injection bandwidth and create pipeline bubbles. To address this problem, we propose a novel bandwidth-optimal Allgather collective algorithm that leverages hardware multicast. We use multicast to build a constant-time reliable Broadcast protocol, a building block for constructing an optimal Allgather schedule. Our Allgather algorithm achieves 2x traffic reduction on a 188-node testbed. To free the host side from running the protocol, we employ SmartNIC offloading. We extract the parallelism in our Allgather algorithm and map it to a SmartNIC specialized for hiding the cost of data movement. We show that our SmartNIC-offloaded collective progress engine can scale to the next generation of 1.6 Tbit/s links.

关键词： Networking AI accelerators Clusters

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