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evaluation of Dynamic Power Flow Control and P2P energy Sharing of Community Microgrids Using PSS Sincal

Evaluation of Dynamic Power Flow Control and P2P Energy Shar...

引用

2024 international conference on Smart Systems for electrical, electronics, Communication and Computer engineering, ICSSeeC 2024

作者： Thirunavukkarasu, Gokul Sidarth Ismath, Mohamed Ishraf Mohamed Seyedmahmoudian, Mehdi Mekhilef, Saad Stojcevski, Alex School of Science Computing and Engineering Technologies Swinburne University of Technology VIC Australia

ISBN: (纸本)9798350378177

This paper presents an in-depth evaluation of peer-to-peer (P2P) energy sharing in community microgrids using PSS Sincal for power flow simulations. The study focuses on an ieee bus system configured as community microgrids, where each bus functions as an independent microgrid equipped with loads, Solar PV systems, batteries, and converters. These microgrids are connected to the main grid and leverage a dynamic power flow control mechanism through strategically placed switches managed by a custom algorithm by ensuring the power flow converges. Four distinct scenarios were rigorously tested: 'Isolated Operation,' where each community grid meets its power requirements solely through PV generation;'Total Dependence on the Main grid,' where PV systems generate no power, and communities rely entirely on the main grid;'Partial PV Generation,' where PV systems partially meet power needs, supplemented by the main grid;and 'Surplus PV Generation,' where PV systems generate excess power for sharing. The PSS Sincal simulations demonstrated the algorithm's effectiveness in enhancing energy distribution efficiency, stability, and the utilization of renewable resources. This study underscores the practical viability of P2P energy sharing in community mi- crogrids, contributing valuable insights to the development of resilient and intelligent power networks. © 2024 ieee.

关键词： Microgrids

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MScheduler: Leveraging Spot Instances for High-Performance Reservoir Simulation in the Cloud 14

MScheduler: Leveraging Spot Instances for High-Performance R...

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14th ieee international conference on Cloud computing Technology and science (CloudCom)

作者： Portella, Felipe A. estrela, Paulo J. B. Malini, Renzo Q. Teylo, Luan Berral, Josep L. Drummond, Lucia M. A. Petroleo Brasileiro SA PETROBRAS Rio De Janeiro Brazil Barcelona Supercomp Ctr BSC Barcelona Spain Univ Politecn Catalunya UPC Barcelona Spain Inst Natl Rech Informat & Automat Inria Bordeaux France Univ Fed Fluminense UFF Rio De Janeiro Brazil

ISBN: (纸本)9798350339826

Petroleum reservoir simulation uses computer models to predict fluid flow in porous media, aiding to forecast oil production. engineers execute numerous simulations with different geological realizations to refine the accuracy of the model. These experiments require considerable computational resources, which are not always available within the on-premises infrastructure. Commercial public cloud platforms can offer many advantages, such as virtually unlimited scalability and pay-per-use pricing. This paper introduces MSCHeDULeR, a meta scheduler framework for reservoir simulations at Petrobras, a Brazilian energy company. It efficiently executes jobs in the cloud, utilizing spot Virtual Machines (VMs) to reduce costs and ensure job completion even with VM termination. Contributions include a novel methodology for reservoir simulation checkpointing, a cost-based scheduler, and an analysis of the strategy using real production jobs from Petrobras.

关键词： Cloud computing Spot Instances Reservoir Simulation

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Generalized Collective Algorithms for the exascale era 25

Generalized Collective Algorithms for the Exascale Era

引用

25th ieee international conference on Cluster computing (CLUSTeR)

作者： Wilkins, Michael Wang, Hanming Liu, Peizhi Pham, Bangyen Guo, Yanfei Thakur, Rajeev Dinda, Peter Hardavellas, Nikos Northwestern Univ Evanston IL 60201 USA Argonne Natl Lab 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9798350307924

exascale supercomputers have renewed the exigence of improving distributed communication, specifically MPI collectives. Previous works accelerated collectives for specific scenarios by changing the radix of the collective algorithms. However, these approaches fail to explore the interplay between modern hardware features, such as multi-port networks, and software features, such as message size. In this paper, we present a novel approach that uses system-agnostic, generalized (i.e., variable-radix) algorithms to capture relevant features and provide broad speedups for upcoming exascale-class supercomputers. We identify hardware commonalities found on announced exascale systems and three omnipresent communication kernels (binomial tree, ring, and recursive doubling) that can be generalized to better leverage these features, creating 10 total implementations. For each kernel, we develop analytical models to intuit algorithm performance with varying radix values. experiments on the world's first exascale supercomputer (Frontier at ORNL) and a pre-exascale system (Polaris at ANL) show that our generalized algorithms outperform the baseline open-source and proprietary vendor MPI implementations by a significant margin, up to over 4.5x. We empirically determine optimal algorithms and parameter values, identifying where the analytical models are accurate and where hardware features directly determine performance. Most notably, we show how a single, system-agnostic implementation of a generalized algorithm can optimize for multiple hardware/software features across multiple systems.

关键词： exascale computing collective communication MPI

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Multi-dimensional Resource Measurement Based on entropy Weight Method in computing Power Optical Networks 22

Multi-dimensional Resource Measurement Based on Entropy Weig...

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22nd international conference on Optical Communications and Networks, ICOCN 2024

作者： Sang, Huiying Yang, Zhichao Lijie, W. Wang, Chunying Liu, Yan Kai, L. Hou, Xiaojie Sun, Zhichun Zhu, Ruijie State Grid Henan Electric Power Information & Telecommunication Company Zhengzhou China Zhengzhou University Zhengzhou China Songshan Laboratory Zhengzhou China

We propose a multi-dimensional resource measurement approach based on the entropy weight method, which dynamically determines index weights and calculates scores of nodes. Simulation results show that it can effective... 详细信息

ISBN: (纸本)9798350367652

关键词： Velocity measurement

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evaluation and Improvement of Interpretability for Self-explainable Part-Prototype Networks

Evaluation and Improvement of Interpretability for Self-Expl...

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ieee/CVF international conference on Computer Vision (ICCV)

作者： Huang, Qihan Xue, Mengqi Huang, Wenqi Zhang, Haofei Song, Jie Jing, Yongcheng Song, Mingli Zhejiang Univ Hangzhou Peoples R China China Southern Power Grid Digital Grid Res Inst Guangzhou Peoples R China Zhejiang Univ China Southern Power Grid Joint Res Ctr AI Hangzhou Peoples R China Univ Sydney Sydney NSW Australia

ISBN: (纸本)9798350307184

Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the similarities in the feature space and that in the input space. In this work, we strive to address this challenge by making the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. Specifically, we propose two evaluation metrics, termed as "consistency score" and "stability score", to evaluate the explanation consistency across images and the explanation robustness against perturbations, respectively, both of which are essential for explanations taken into practice. Furthermore, we propose an elaborated part-prototype network with a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and provide substantial discussions to uncover the interpretability of existing part-prototype networks. experiments on three benchmarks across nine architectures demonstrate that our model achieves significantly superior performance to the state of the art, in both the accuracy and interpretability. Our code is available at https://***/hqhQAQ/evalProtoPNet.

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A Study on Privacy Protection of Medical Text Data Based on Cloud-edge Cooperative computing and Homomorphic encryption

A Study on Privacy Protection of Medical Text Data Based on ...

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2024 ieee international conference on e-Health Networking, Application and Services, HealthCom 2024

作者： Chen, Peng Guo, Zhen Zhang, Yuqing Li, Yuheng Liu, Xuxing Xiao, Xiangwen School of Cyberspace Security Hainan University Hainan Province China

ISBN: (纸本)9798350350548

This study aims to address the problem of medical text data privacy protection in the context of extensive development of cloud computing. This paper comprehensively researches the relevant literature and technical progress in recent years on privacy protection in medical scenarios using homomorphic encryption and other advanced cryptography techniques, and synthesizes the scalability of cloud computing, the timeliness of edge computing, and the inerrancy of blockchain technology to achieve the privacy protection of medical text data through the cloud storage of medical data and the dense state computation, and protects the privacy of patient's medical data in order to achieve the purpose of protecting individual privacy, property security, and security. It protects the privacy of patients' medical data to realize the purpose of protecting personal privacy and property security. By changing the encryption parameters of the existing DGHV multi-key homomorphic encryption scheme to realize the characteristics of the adapted scheme, and at the same time, the public key parity restriction can be eliminated. In addition, this paper also discusses the application of blockchain technology in homomorphic encryption key management, and proposes a blockchain-based key security storage system to ensure the key's tamperability and security. The privacy security problem in medical data transmission, storage and processing is solved by constructing this medical text data privacy protection system based on cloud-edge cooperative computing and homomorphic encryption. Simulation experiments verify that this scheme is practically feasible, while the correct parameters and the choice of the edge computing approach take into account both security and feasibility. © 2024 ieee.

关键词： Differential privacy

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Push-Pull based grid tied Micro inverter for Photovoltaic Applications

Push-Pull based Grid tied Micro inverter for Photovoltaic Ap...

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2023 ieee international conference on Power electronics, Smart grid, and Renewable energy, PeSGRe 2023

作者： Paul, Preenu Jose, Babita R Shahana, T.K. Abraham, Chikku Mathew, Jimson Sandeep, J. Cochin University of Science and Technology Division of Electronics School of Engineering Cochin India Muthoot Institute of Technology and Science Department of Electrical and Electronics Engineering Ernakulam India Indian Institute of Technology Department of Computer Science and Engineering Patna India Sfo Technologies R & D Solutions Power Electronics Cochin India

ISBN: (纸本)9798350310573

This paper presents the harnessing of solar energy using a two-stage grid-tied micro inverter with an isolated high gain DC-DC converter as first stage and a single-phase grid tied inverter as second stage. High gain converter is implemented using a quasi-switched boost network, switched capacitor topology and push-pull circuit. High frequency transformer in first stage improves gain and provides isolation between solar Photovoltaic (PV) circuit and grid connection which add safety margins. Micro inverter topology, control logic for Maximum Power Point Tracking (MPPT) and H - bridge inverter is presented. The operation of a 250 V micro inverter is verified using MATLAB Simulink tool. © 2023 ieee.

关键词： grid tied inverter Isolated DC-DC converters MPPT One cycle control Switched boost topology Switched capacitor

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MODGF: Multi-Objective Optimization Approach for Directed Greybox Fuzzing 25

MODGF: Multi-Objective Optimization Approach for Directed Gr...

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25th ieee international conferences on High Performance computing and Communications, 9th international conference on Data science and Systems, 21st ieee international conference on Smart City and 9th ieee international conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC/DSS/SmartCity/DependSys 2023

作者： Zuo, Xueying Song, Yubo Mao, Run Qin, Zhongyuan Sun, Xin School of Cyberspace Security Southeast University Nanjing China Internet Technology Center State Grid Zhejiang Electric Power Co. Ltd. Research Institute Hangzhou China

ISBN: (纸本)9798350330014

Directed Greybox Fuzzing employs code analysis to predict critical paths or potential vulnerabilities. Multi-objective optimization is a challenge to Directed Greybox Fuzzing. Current approaches employ evolutionary algorithms which may trap in local optima during mutation operation procedure. In this paper, we propose MODGF, a Directed Greybox Fuzzing method based on the elitist multi-objective evolutionary algorithm. The key insight of our approach is to get a more diverse distribution along the Pareto front during evolutionary iterations, which speed the convergence of the test cases. In MODGF, we use function distance, basic block distance, and seed distance as objective functions, and calculate distance metrics of the nearest neighbor solutions to infer the distribution density of solutions. We further introduce convergence measure and diversity measure as fitness values to ensure that the generated test cases are more uniformly distributed with more vulnerability types. In comparison to AFL and AFL-GO, the experiment reveals that MODGF exhibits significant performance enhancements in terms of convergence time, target function coverage, and vulnerability triggering efficiency. © 2023 ieee.

关键词： Multiobjective optimization

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Research on Pseudo Measurement Generation Method Based on 1D-CNN and Transfer Learning for Medium-Voltage Distribution Networks 2

Research on Pseudo Measurement Generation Method Based on 1D...

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2nd international conference on Cyber-energy Systems and Intelligent energy, ICCSIe 2024

作者： Sun, Zhiqing Lei, Shuya Yi, Xuan Zhang, Wense Wang, Yishen Ni, Xiabing Wang, Qifeng Liang, Xiao Huang, Yan State grid Hangzhou power supply company Zhejiang China State Grid Smart Grid Research Institute Co. Ltd. State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China State Grid Smart Grid Research State Grid Laboratory of Grid Advanced Computing and Applications Zhejiang China Tsinghua University Sichuan Energy Internet Research Institute Chengdu China

ISBN: (纸本)9798350373318

With the increasing scale and complexity of new power systems, ensuring the safe and optimized operation of distribution networks becomes particularly important. Due to constraints such as economic factors, the comprehensive deployment of real-time measurement devices in mediumvoltage distribution networks faces significant challenges, resulting in insufficient observability of the distribution network. To address this issue, this paper proposes a method that combines one-dimensional convolutional neural networks (1D-CNN) with transfer learning for pseudo measurement generation. This method first constructs a 1D-CNN pseudo measurement generation model using a large amount of historical measurement data, and then trains and optimizes the 1D-CNN model through transfer learning to achieve pseudo measurement generation even under topological changes. The test results of actual engineering data demonstrate that the proposed method can generate high-frequency pseudo measurement data for nodes in medium-voltage distribution networks in real time, accurately, and efficiently. Additionally, the proposed method exhibits good adaptability when the distribution network topology changes. In this way, this paper provides a new technical path for the perception of the operational status of medium-voltage distribution networks and provides technical support for the safe and optimized operation of distribution networks. © 2024 ieee.

关键词： Power distribution networks

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Fedgrid: Federated Model Aggregation via grid Shifting 16

FedGrid: Federated Model Aggregation via Grid Shifting

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16th ieee/ACM international conference on Utility and Cloud computing (UCC) / 10th ieee/ACM international conference on Big Data computing, Applications and Technologies (BDCAT)

作者： Kraychev, Boris Kiyamousavi, ensiye Koychev, Ivan GATE Inst Sofia Bulgaria Sofia Univ St Kliment Ohridski Sofia Bulgaria

ISBN: (纸本)9798400702341

Federated Learning is a machine learning technique where independent devices (clients) cooperatively train a machine learning model by working on decentralized training data. A fundamental challenge in federated learning is the client model aggregation. The goal is to combine and preserve the knowledge that each client has acquired during its local training phase and generate an aggregated model with superior performance than any of the clients. Most current state-of-the-art algorithms for model aggregation still rely on simple weight parameter averaging. Though effective, these methods do not consider the internal structure of a deep neural network. In addition, the element-wise aggregation of the model parameters may create a conflict and loss of knowledge if a particular weight parameter serves different classification tasks over other learning clients. To decrease the probability of such conflicts, we propose Fedgrid, a method that involves rotated (shifted) weight parameter grids during the clients' local training phase, which stimulates the models to build non-overlapping neural network structures. Fedgrid can be applied to any existing algorithm that performs parameter averaging to improve the aggregation of deep NN models. To estimate the method's performance, we generate an ensemble of datasets with preset heterogeneity, measured via entropy balance metric, ranging from 0.1 to 1.0. Our results indicate that Fedgrid outperforms the state-of-the-art methods when applied to deep neural network models. Moreover, the proposed method is simple and does not add computational complexity to the learning process. We believe that our effective method for grid application provides a new field for future research and improvement of the federation of deep neural network models.

关键词： Federated Learning Neural Networks Model Aggregation Strategy FL Algorithm decentralized Learning Learning Rate grid

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