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International Conference on Embedded & distributed systems (EDiS)

Presents the front cover, title page, cover page, or splash screen of the proceedings record.

ISBN: (数字)9798350397543

ISBN: (纸本)9798350397550

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Fast Frequency Regulation in Islanded Microgrid Using Model-Based Load Estimation

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2021年第4期36卷 3188-3198页

作者： Hussain, Amir Hasan, Shamim Patil, Sumit Shireen, Wajiha Univ Houston Elect & Comp Engn Houston TX 77204 USA Elect Engn Mumbai 400076 Maharashtra India Univ Houston Syst Elect & Comp Engn Houston TX 77204 USA Univ Houston Elect Elect Technol Houston TX 77204 USA

Islanded microgrids have low inertia due to large penetration of non-inertial inverter based power sources. Such systems are prone to instability due to a higher rate of change of frequency (RoCoF) and large peak frequency deviation in case of a sudden load change or generation loss. This paper proposes a novel fast frequency control method for an islanded microgrid comprised of both inverter based distributed generators (DGs) and synchronous generators (SGs). The frequency controller estimates the power imbalance during the transient and compensates for it through a battery energy storage system (BESS). For the estimation of the power imbalance, an approximated linear model of the microgrid is developed considering multiple SGs operating in parallel. To transfer the compensated power from BESS to SGs, and also to maintain the state of charge (SoC) of the BESS, a SoC balancing control method is presented which is implemented by controlling the biased frequency of the SGs. The performance of the proposed frequency control method is verified using Typhoon real-time hardware-in-the-loop simulator.

关键词： Frequency control Microgrids Transient analysis Inverters Load modeling Transfer functions Synchronous generators Islanded microgrids primary frequency control model based estimation battery energy storage state of charge control distributed generators

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Fast cluster-based computation of exact betweenness centrality in large graphs

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JOURNAL OF BIG DATA 2021年第1期8卷 1-39页

作者： Daniel, Cecile Furno, Angelo Goglia, Lorenzo Zimeo, Eugenio Univ Gustave Eiffel Univ Lyon ENTPE LICIT UMR T9401 Lyon France Univ Sannio Dept Engn I-82100 Benevento Italy

Nowadays a large amount of data is originated by complex systems, such as social networks, transportation systems, computer and service networks. These systems can be modeled by using graphs and studied by exploiting graph metrics, such as betweenness centrality (BC), a popular metric to analyze node centrality of graphs. In spite of its great potential, this metric requires long computation time, especially for large graphs. In this paper, we present a very fast algorithm to compute BC of undirected graphs by exploiting clustering. The algorithm leverages structural properties of graphs to find classes of equivalent nodes: by selecting one representative node for each class, we are able to compute BC by significantly reducing the number of single-source shortest path explorations adopted by Brandes' algorithm. We formally prove the graph properties that we exploit to define the algorithm and present an implementation based on Scala for both sequential and parallel map-reduce executions. The experimental evaluation of both versions, conducted with synthetic and real graphs, reveals that our solution largely outperforms Brandes' algorithm and significantly improves known heuristics.

关键词： Complex networks analysis Betweenness centrality distributed computation Big data processing

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A Novel Extended Desired Compensation Adaptive Law for High-Speed Pick-and-Throw with PKMs 14th

A Novel Extended Desired Compensation Adaptive Law for High-...

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14th IFAC workshop on Adaptive and Learning Control systems (ALCOS)

作者： Hassan, G. Chemori, A. Gouttefarde, M. El Rafei, M. Francis, C. Herve, P. E. Salle, D. Univ Montpellier CNRS LIRMM Montpellier France Lebanese Univ Fac Engn CRSI Beirut Lebanon Basque Res & Technol Alliance BRTA TECNALIA San Sebastian Spain

This paper focuses on the development of a new revised Desired Compensation Adaptive Law (DCAL). DCAL is a model-based adaptive control strategy consisting of three main parts: (i) an adaptive feedforward term, (ii) a linear PD feedback term, and (iii) a nonlinear compensation term. In order to deal with highly nonlinear dynamic systems characterized by their abundant uncertainties and parameters variations, we propose to revise the original DCAL control law by adopting adaptive feedback gains depending on the system state errors. Besides, DCAL controller is known for its robustness against measurement noise thanks to its desired compensation design, but a large amount of external disturbances are still not compensated by such a design. Therefore, the proposed DCAL with adaptive gains (DCAL-AG) is extended with a sliding-based term to further improve its robustness and the overall performance. A model-based robust adaptive feedback controller appropriate to the control of nonlinear systems in real-time applications is thereby obtained. To demonstrate the improvements brought by the proposed control strategy, numerical simulations have been conducted on a Delta-link parallel robot named T3KR in a "Pick-and-Throw" application task at different operating conditions. Copyright (C) 2022 The Authors.

关键词： Robust DCAL adaptive gains parallel kinematic manipulator pick-and-throw numerical simulations

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workshop: Converge of Edge Intelligence in IoT (EdgeA-IoT 2022) 8

Workshop: Converge of Edge Intelligence in IoT (EdgeA-IoT 20...

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8th IEEE World Forum on the Internet of Things (WF-IoT) - Sustainability and the Internet of Things

作者： Anagnostopoulos, Christos Deligiani, Fani Kolomvatsos, Kostas Mateo Fornes, Jordi Univ Glasgow Glasgow Lanark Scotland Univ Thessaly Lamia Greece Univ Lleida Lleida Spain

ISBN: (纸本)9781665491532

Edge Intelligence is a synergy that seems to be imperative to conclude the convergence of the Edge Computing and Internet of Things to support intelligent application very close to end users. IoT has pervaded our daily life by making things, interconnected through the Internet, smarter, distributed and more autonomous. The emerging development of intelligent applications in IoT has now started to gain significant attention. Cloud provides many benefits to IoT devices, including high-performance computing, a storage infrastructure, processing and analysis of large-scale data giving to IoT the opportunity to be robust, smart and self-configuring. The forthcoming emergence of Edge AI will extend the capabilities of the `legacy' IoT, its potentials, the number of devices and the volumes of data. However, Cloud technologies face some accessibility challenges when providing services to end-users. For instance, mobile clients can move among different places, yet require Cloud services with minimum cost and short response time. The unstable connection between Cloud and mobile devices is expected to prevent providers from achieving the optimal performance. To cope with these limitations, we aspire that Edge AI converged with IoT environments materializes the desired AI-led distributed and ubiquitous intelligence in real computing systems. We then need additional effort to establish and deliver the convergence of Edge AI and IoT in formatting the future Intelligent IoT. The Intelligent IoT is envisioned to involve numerous autonomous & distributed computing and AI-driven entities capable of understanding their internal status (context), the status of their environment and peers (collaborative context) and take timely optimized actions to efficiently serve modern applications, like tactile internet and augmented AI-led gaming.

关键词： Edge AI Intelligent Edge Collaborative Context Aware Applications distributed AI Edge AI Infrastructure Modelling Computational Intelligence

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并行智能优化算法研究进展

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控制理论与应用 2023年第1期40卷 1-11页

作者：张国王锐雷洪涛张涛王凌国防科技大学系统工程学院湖南长沙410073 多能源系统智慧互联技术湖南省重点实验室湖南长沙410073 清华大学自动化系北京100084

基于种群迭代搜索的智能优化算法在农业、交通、工业等很多领域都取得了广泛的应用.但是该类算法迭代寻优的特点使其求解效率通常较低,很难应用到大规模、高维或实时性要求较高的复杂优化问题中.随并行分布式技术的发展,国内外很多学者... 详细信息

基于种群迭代搜索的智能优化算法在农业、交通、工业等很多领域都取得了广泛的应用.但是该类算法迭代寻优的特点使其求解效率通常较低,很难应用到大规模、高维或实时性要求较高的复杂优化问题中.随并行分布式技术的发展,国内外很多学者开始着手研究智能优化算法的并行化.本文首要介绍了并行智能优化算法的基本概念;其次从协同机制、并行模型以及硬件结构3个维度综述了几类常见的并行智能优化算法,详细分析阐述了它们优点及不足;最后对并行智能优化算法的未来研究进行了展望.

关键词：大规模优化智能优化算法并行计算并行优化算法

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Performance optimization of Learning Trees modules in the ECL repository

Performance optimization of Learning Trees modules in the EC...

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Communication, Control and Intelligent systems (CCIS)

作者： Shashank B Sudershan K S Shobha G S. Manonmani Roger Dev Dept. of Computer Science RV College of Engineering Bangalore India LexisNexis Risk Solutions USA

ISBN: (数字)9798331528201

ISBN: (纸本)9798331528218

Multi-Node computation, also known as distributed computing, is a paradigm that allows for the efficient utilization of multiple interconnected nodes or machines to perform complex computational tasks. By dividing the workload and distributing it across multiple nodes, multi-node computation enables parallel processing, scalability, and improved performance for a wide range of applications. HPCC systems® from LexisNexis Risk Solutions is a proven, open source solution for big data insights that can be implemented by businesses of all sizes. HPCC systems offers a consistent data-centric programming language, two processing platforms and a single, complete end-to-end architecture for efficient processing. ECL is the Enterprise Control Language designed specifically for huge data projects using the HPCC systems platform. It is extremely scalable and a powerful declarative data-processing language with native multi-processing capabilities and used for HPCC systems. Some core ECL functions, like the ones handling Learning Tree algorithms of Machine Learning Bundle are recursive in nature and hence high computational time is needed. This paper thus presents a solution to achieve optimized processing of ML learning tree algorithms in ECL through embedding python libraries. The proposed approach was tested on standard datasets and a significant decrease in computation time. For real Estate Valuation Regression dataset the computation time was reduced from 11.371 seconds to 0.886 seconds using the proposed method showing 1324% improvement with no impact on the accuracy. Random Forests are also implemented using the proposed Decision Tree method allowing the users to specify the number of decision trees to be used in the forest.

关键词： Machine learning algorithms Scalability Big Data parallel processing Computational efficiency Decision trees Random forests Standards Optimization Python

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An integrated control algorithm of power distribution for islanded microgrid based on improved virtual synchronous generator

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IET RENEWABLE POWER GENERATION 2021年第12期15卷 2674-2685页

作者： Zhang, Liang Zheng, Hao Wan, Tao Shi, Donghan Lyu, Ling Cai, Guowei Northeast Elect Power Univ Minist Educ Key Lab Modern Power Syst Simulat & Control & Ren Jilin 132012 Jilin Peoples R China State Grid Shandong Elect Power Co Jining Power Supply Co Jining Peoples R China State Grid Informat & Telecommun Grp Co Ltd Beijing Peoples R China Nanyang Technol Univ Sch Elect & Elect Engn Singapore Singapore

Virtual synchronous generator technology can effectively improve the anti-interference characteristics of the system frequency and bus voltage in the microgrid, and solve the problems of insufficient damping and low inertia of the system. However, in an islanded microgrid with multiple distributed generation, the difference in line impedance will cause local voltage deviations, which in turn leads to a series of problems, such as reducing power distribution accuracy and increasing bus voltage drop. Therefore, for the island-type microgrid multi-inverter distributed power generation parallel system, in order to solve the problem of low power distribution accuracy and large frequency oscillation caused by system parameters in virtual synchronous generator control, an improved virtual synchronous generator control algorithm based on adaptive droop coefficient is proposed in this paper, which not only eliminates the resistance component of the line impedance, makes the system impedance characteristic present a purely inductive nature, but also realises real-time adjustment of active and reactive power. While maintaining the stability of the bus voltage and system frequency, it maintains high power distribution accuracy and improves the dynamic performance and operational stability of the power grid system.

关键词： Synchronous machines Control of electric power systems distributed power generation Power system control Distribution networks

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Modeling Datasets in distributed Monitoring systems 11

Modeling Datasets in Distributed Monitoring Systems

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11th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing systems - Technology and Applications (IDAACS)

作者： Bekeneva, Yana A. St Petersburg Electrotech Univ LETI Ul Prof Popova 5 St Petersburg 197376 Russia

ISBN: (纸本)9781665426053

Automated detection of abnormal events in processes in real time is an urgent and important task. To identify various kinds of anomalies, various Data Mining methods are often used. Often, to obtain high accuracy of the analysis, preliminary training is required using a large amount of data and representative samples. However, it is not always possible to obtain a representative sample of various kinds of anomalies from real sources. This paper proposes an approach to modeling datasets generated by distributed heterogeneous monitoring systems.

关键词： monitoring systems vehicle movement generation datasets modeling system anomalies data mining

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Heterogeneous Data Processing in All-Digital Phased Array Radar

Heterogeneous Data Processing in All-Digital Phased Array Ra...

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IEEE International Symposium on Phased Array systems and Technology

作者： Skyler Garner Mark Yeary Advanced Radar Research Center University of Oklahoma Norman OK United States

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

All-digital phased array radars are an emerging technology that require new data processing techniques which are scalable, rely on open-architecture software components, and do not create bottlenecks for the real-time operation. Achieving a solution for these issues relies on heterogeneous processing and distributed software development on CPUs and FPGAs, whereby the overarching algorithm processing chain is distributed in a way that begins at the ADC of each element on an array and concludes in actionable data products. An important aspect is not only the speed of the algorithms, but the throughput of the packetized data transfer. These packets are not only routed, but they are also acted upon in a network of computing services. Here, we explore an open-source resource manager, known as Kubernetes, which connects the packetized data with computing resources on the servers, i.e., moving data in and out of server RAM to complete algorithms such as creating range-Doppler maps, as our results section shows.

关键词： Phased arrays Array signal processing Software algorithms Signal processing algorithms Radar parallel processing Throughput Arrays Servers Software development management

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