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Topology Identification Technology for Low-voltage distributed Photovoltaic Distribution Network Lines 3

Topology Identification Technology for Low-voltage Distribut...

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3rd international conference on Energy and Electrical Power systems (ICEEPS)

作者： Zheng, Guoquan Zhu, Enguo Zhang, Hailong Liu, Yan Wang, Junlong Liu, Zhe China Elect Power Res Inst Beijing Peoples R China State Grid Hebei Elect Power Beijing Peoples R China

ISBN: (纸本)9798350375145;9798350375138

The large-scale penetration of distributed photovoltaic (PV) power generation systems has brought new challenges to the topology identification and detection of traditional distribution networks. This article mainly studies the topology identification technology (TIT) of distributed PV low-voltage (LV) distribution network lines, aiming to design a topology identification method that can adapt to the dynamic changes of the power grid, have large-scale capacity, and improve system accuracy under the same conditions. At the same time, the data processing speed has been improved. This article first constructs a system model, including node model, edge model, and parameter model. It mathematically represents the topology structure of the power grid using graph theory and designs a topology recognition algorithm based on optimization techniques and state estimation. This algorithm is used to solve the distributed characteristics of power grid topology recognition, the difficulty of data collection, the dynamic diversity of power grid structure, the uncertainty of equipment parameters, the high computational complexity of data processing, and the communication constraints of power grid topology recognition. This algorithm adopts modern programming languages and parallel computing frameworks, making it easy to implement efficiently. The results on the simulation platform show that the highest recall rate for 22 test cases is 93.8%, and the response time for test cases is 425 ms to 980 ms, providing a fast response to the information space of the grid.

关键词： Low-voltage distributed Photovoltaic Distribution Network Topology Identification Technology Simulation Platform Recall Ratio

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mpiPython: A Streamlined Python Collective Communication Library to MPI 24

mpiPython: A Streamlined Python Collective Communication Lib...

引用

9th international conference on Cloud Computing and Internet of Things, CCIOT 2024

作者： Lee, Jaden Jinu Nava, Judah J. Lee, Hanku Computer Science & Information Systems Department Minnesota State University Moorhead MoorheadMN United States

ISBN: (纸本)9798400717161

Python's popularity as an interpreted language, particularly among scientists and engineers, is due to its ease of use and flexibility, despite certain performance limitations. Enhancing Python for parallel programming opens significant opportunities in both parallel and cloud computing environments. mpiPython, a Python library designed for message-passing, enables Single Program Multiple Data (SPMD) execution and supports efficient parallel processing. One of its key advantages is its simplicity - both in installation and use - making it accessible to a wide range of users without compromising performance. This paper introduces an extended version of mpiPython, which addresses shortcomings in collective operations for parallel computing while maintaining the library's strengths: ease of installation and minimal overhead. mpiPython is designed with a direct implementation in C, avoiding additional layers, unlike mpi4py, allowing for more efficient communication without unnecessary overhead. The latest version introduces key enhancements to collective communication, including MPI All-Gather, MPI-Send, and MPI-Receive functions. These additions significantly improve performance, particularly in large-scale distributed systems, while preserving mpiPython's focus on being lightweight and user-friendly. Our results show that mpiPython offers competitive performance compared to other MPI libraries while remaining accessible to users who prioritize simplicity and efficiency. The latest fully functional release of mpiPython is now officially available, providing a streamlined solution for Python-based parallel computing. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Python

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Optimal Factored LT codes for distributed Matrix Multiplication 29

Optimal Factored LT codes for Distributed Matrix Multiplicat...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Zhang, Lei Liu, Jie Capital Normal University Information Engineering College Beijing China

ISBN: (纸本)9798350330717

Due to the presence of slow or failed worker computers (called stragglers), distributed matrix multiplication over large clusters may encounter delays. To tackle this issue, Factored Luby Transform (FLT) codes have been proposed for edge computing scenarios involving distributed matrix multiplication of the form C = A ∗ B. However, the decoding time of FLT codes becomes a bottleneck in coded distributed matrix multiplication, primarily due to the high computational complexity associated with peeling decoding. To overcome this limitation, this paper introduces the modified generalized degree distribution algorithm (MGDDA) for designing an optimal degree distribution in FLT codes with BP decoding. The MGDDA algorithm minimizes overhead and maximizes performance efficiency in coded distributed matrix multiplication scenarios. Additionally, we propose an optimal encoding algorithm that achieves a balanced probability of connecting input symbols from matrices A and B to encoded symbols. This algorithm further enhances the performance of FLT codes. Simulation results consistently demonstrate that our proposed Optimal FLT (OFTL) method outperforms other existing approaches in terms of average overhead, block error rate (BER) and computational delay. © 2023 IEEE.

关键词： Coded Computation Degree Distribution LT codes Matrix Multiplication

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Accelerating SAT Solving Using Switching ASICs 29

Accelerating SAT Solving Using Switching ASICs

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Huang, Zhenpei Fan, Xiwen Jiang, Jinghui Song, Mingyuan Tang, Lu Xiang, Qiao Shu, Jiwu Xiamen University School of Information Xiamen China

ISBN: (纸本)9798350330717

People have been leveraging the capabilities of programmable switches, which are programmable in the data plane and process packets at the line rate, to improve the performance of distributed systems. However, few have explored whether programmable switches can speed up problem-solving. In this paper, we select the SAT problem, one of the most fundamental problems in computer science, as a case study to first explore the feasibility and benefits of this line of research. Our intuition is that by exploiting the parallel lookup capability of programmable switches, we can substantially speed up the process of checking whether an assignment is a solution to a SAT problem. Consequently, we design conflict tables using TCAM to quickly check assignment satisfiability. Building on the conflict tables, we propose two SAT solvers, P4-DPLL and Antler. Specifically, P4-DPLL is based on the classical DPLL algorithm and implements a stack data structure using registers and SRAM to efficiently make variable search decisions in the data plane, while Antler utilizes the mirroring capability of the programmable switch to further accelerate the SAT solving, which improves the non-parallel depth-first search-based P4-DPLL into a parallel breadth-first search-based SAT solver. We implement the prototypes of P4-DPLL and Antler on the Tofino switch, and evaluate their performance extensively. Results show that P4-DPLL and Antler improve the solving time by 2x and 169x on 90% quantile of test cases, compared to a CPU-based DPLL implementation. Besides, compared with the most popular SAT solvers, MathSAT and Z3, Antler improves the solving time by 42x and 35x, respectively. © 2023 IEEE.

关键词： Programmable Switches SAT Solver

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FEMD: Feature Enhancement-aided Multimodal Feature Fusion Approach for Smart Contract Vulnerability Detection 30

FEMD: Feature Enhancement-aided Multimodal Feature Fusion Ap...

引用

30th IEEE international conference on parallel and distributed systems, ICPADS 2024

作者： Li, Juan Li, Rui Lu, Youshui Cai, Bowen Cao, Yulin Lin, Hang Li, Xingxing Li, Chan Qinghai Normal University School of Computer Science Xining China Xidian University The State Key Laboratory of Tibetan Intelligent Information Processing and Application School of Computer Science Qinghai Normal University School of Computer Science and Technology Xian China School of the Instrument Science and Technology School of Electrical Engineering Xi'an Jiaotong University Xian China School of Science and Engineering University of Minnesota - Twin City Minneapolis United States

ISBN: (纸本)9798331515966

Smart contracts, due to their immutability and transparency upon deployment, entail significant economic and systemic risks from any vulnerabilities present. Traditional vulnerability detection methods suffer from low automation and high false positive rates, while existing deep learning-based approaches inadequately extract contract features, thereby limiting detection accuracy. To address these issues, this paper proposes FEMD: a feature-enhanced aided multimodal feature fusion method for smart contract vulnerability detection. Building on the foundation of addressing the low automation of traditional detection tools, our method improves the model's feature extraction performance and detection capabilities. Specifically, we construct a contract graph through Comprehensive Expert-Graph Fusion, combining multi-modal feature fusion using a multi-head attention mechanism with expert patterns to ensure the capture and effective preservation of all critical information during the fusion process. To delve deeper into potential information within smart contract graphs, we employ a Feature Enhancer that leverages transpose operations on feature matrices to extract complex interaction patterns across different dimensions. Our approach is validated through batches of experiments on the Ethereum open dataset focusing on reentrancy and timestamp dependency vulnerabilities, demonstrating significant improvements in detection accuracy and robustness. © 2024 IEEE.

关键词： Deep learning

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Research on improving sagging control strategy based on distributed inverter

Research on improving sagging control strategy based on dist...

引用

2024 international conference on Electronics, Electrical and information Engineering, ICEEIE 2024

作者： Li, Mengda Zhu, Hanghang Sun, Yueyue Pei, Guangyao Ning, Han School of Electrical Engineering Shanghai Dianji University Shanghai201306 China

ISBN: (数字)9781510686809

ISBN: (纸本)9781510686793

In microgrids, sag control strategies are commonly employed by inverters across various levels. This study introduces an innovative solution to address challenges in sag control, specifically targeting issues related to uneven reactive power distribution and present transmission line impedance causes the AC bus voltage to drop. The proposed approach involves the implementation of an adaptive virtual impedance method and a feedback bus voltage recovery strategy, both grounded in the principle of multibody consistency. firstly, the paper analyzes the causes of uneven reactive power distribution and busbar voltage deviation caused by traditional inverter sag control with multi-inverters parallel model. Secondly, the improvement strategy of sag control is proposed, and the virtual impedance and bus voltage feedback method of control based on distributed consistency principle is introduced to achieve the purpose of reactive power equalizing and restoring bus voltage. Finally, two inverter models were built in the MATLAB/Simulink, validation of the proposed strategy and demonstrate the effectiveness of the proposed strategy. Simulation results show that the proposed strategy can effectively improve the performance of the system of rational reactive power distribution and stable bus voltage of multiple inverters and can provide reference for the parallel use of multiple inverters. © 2024 SPIE.

关键词： Control systems Resistance Telecommunications Adaptive control Solar energy Wind energy Digital filtering Power grids Tunable filters Virtual reality

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A Framework for Real-Time Monitoring of Power Consumption of distributed Calculation on Computational Cluster 15

A Framework for Real-Time Monitoring of Power Consumption of...

引用

15th international conference on Cloud Computing and Services Science, CLOSER 2025

作者： Krechowicz, Adam Faculty of Electrical Engineering Automatic Control and Computer Science Kielce University of Technology al. 1000-lecia PP 7 Kielce Poland

ISBN: (纸本)9789897587474

This paper proposes a framework for real-time monitoring of the power consumption of distributed calculation on the nodes of the cluster. The framework allows to visualize and analyze the provider results based on the context information about the performed calculation. The first part of the framework is devoted to monitoring the power consumption during the execution of machine learning algorithms and the performance of NoSQL storage. The second part is dedicated to the testing of distributed data storage, called Scalable distributed Two-Layered Data Structure (SD2DS). The results show that the framework can be used in the development of a management system that could schedule computations to take full advantage of renewable energy. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.

关键词： Cluster computing

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Accelerating Big Data Application by Eliminating Redundancy on Hadoop Cluster 29

Accelerating Big Data Application by Eliminating Redundancy ...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Lei, Kelun Du, Shaokang You, Xin Xuan, Zhibo Kong, Haoran Yang, Hailong Shang, Jing Xiao, Zhiwen Wu, Zhihui Luan, Zhongzhi Qian, Depei Beihang University School of Computer Science and Engineering Beijing China China Mobile Information Technology Center China

ISBN: (纸本)9798350330717

Big data applications are widely adopted to mine valuable information from a tremendous amount of industry data, which is commonly represented as a series of map-reduce operations. Among various map-reduce frameworks, Hadoop is most commonly adopted for data processing at large scale. Although Hadoop eases the development of highly scalable distributed big data applications, inefficient implementation due to poor coding practice and deep software abstractions can cause severe performance issues such as unreasonable slowdown, high response latency, and waste of computing resources, which can lead to unsatisfactory serving delay or significant maintenance cost. In this paper, we first categorize three common types of redundant patterns in big data applications. Then we propose a tool-assisted optimization workflow to detect the redundant patterns automatically, which profiles the application by sampling hardware performance monitoring units. Moreover, we present a profiling visualization method that can help to pinpoint the redundant codes. Based on these approaches, we optimize several big data applications by eliminating redundancies, yielding up to 14.8% performance improvement. © 2023 IEEE.

关键词： Hadoop Performance Optimization Redundant Patterns

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Improved distributed Algorithms for Random Colorings 27

Improved Distributed Algorithms for Random Colorings

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27th international conference on Principles of distributed systems, OPODIS 2023

作者： Carlson, Charlie Frishberg, Daniel Vigoda, Eric Department of Computer Science University of California Santa BarbaraCA United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

ISBN: (纸本)9783959773089

Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic tool for sampling from high-dimensional distributions, a notable example is the equilibirum distribution of graphical models. The Glauber dynamics, also known as the Gibbs sampler, is the simplest example of an MCMC algorithm;the transitions of the chain update the configuration at a randomly chosen coordinate at each step. Several works have studied distributed versions of the Glauber dynamics and we extend these efforts to a more general family of Markov chains. An important combinatorial problem in the study of MCMC algorithms is random colorings. Given a graph G of maximum degree ∆ and an integer k ≥ ∆ + 1, the goal is to generate a random proper vertex k-coloring of G. Jerrum (1995) proved that the Glauber dynamics has O(n log n) mixing time when k > 2∆. Fischer and Ghaffari (2018), and independently Feng, Hayes, and Yin (2018), presented a parallel and distributed version of the Glauber dynamics which converges in O(log n) rounds for k > (2 + Ε)∆ for any Ε > 0. We improve this result to k > (11/6 − δ)∆ for a fixed δ > 0. This matches the state of the art for randomly sampling colorings of general graphs in the sequential setting. Whereas previous works focused on distributed variants of the Glauber dynamics, our work presents a parallel and distributed version of the more general flip dynamics presented by Vigoda (2000) (and refined by Chen, Delcourt, Moitra, Perarnau, and Postle (2019)), which recolors local maximal two-colored components in each step. © Charlie Carlson, Daniel Frishberg, and Eric Vigoda

关键词： Coloring

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Weighted Sampled Split Learning (WSSL): Balancing Privacy, Robustness, and Fairness in distributed Learning Environments 29

Weighted Sampled Split Learning (WSSL): Balancing Privacy, R...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Osti, Manish Thakuri, Aashray Qolomany, Basheer Mulahuwaish, Aos Saginaw Valley State University University Center Department of Computer Science and Information Systems MI United States University of Michigan - Dearborn College of Engineering and Computer Science Dearborn United States University of Nebraska at Kearney Cyber Systems Department Kearney United States

ISBN: (纸本)9798350330717

This study presents Weighted Sampled Split Learning (WSSL), an innovative framework tailored to bolster privacy, robustness, and fairness in distributed machine learning systems. Unlike traditional approaches, WSSL disperses the learning process among multiple clients, thereby safeguarding data confidentiality. Central to WSSL's efficacy is its utilization of weighted sampling. This approach ensures equitable learning by tactically selecting influential clients based on their contributions. Our evaluation of WSSL spanned various client configurations and employed two distinct datasets: Human Gait Sensor and CIFAR-10. We observed three primary benefits: heightened model accuracy, enhanced robustness, and maintained fairness across diverse client compositions. Notably, our distributed frameworks consistently surpassed centralized counterparts, registering accuracy peaks of 82.63% and 75.51% for the Human Gait Sensor and CIFAR-10 datasets, respectively. These figures contrast with the top accuracies of 81.12% and 58.60% achieved by centralized systems. Collectively, our findings champion WSSL as a potent and scalable successor to conventional centralized learning, marking it as a pivotal stride forward in privacy-focused, resilient, and impartial distributed machine learning. © 2023 IEEE.

关键词： and distributed Learning Privacy-Preserving Machine Learning Robust Learning Unbiased Learning Weighted Sampled Split Learning (WSSL)

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