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Multiagent Power Flow Control for Plug-and-Play Battery Energy Storage systems in DC Microgrids 58

Multiagent Power Flow Control for Plug-and-Play Battery Ener...

引用

58th international Universities Power Engineering conference, UPEC 2023

作者： Al-Saadi, Mudhafar Short, Michael School of Computing Engineering and Digital Technologies Teesside University Middelisborough United Kingdom

ISBN: (纸本)9798350316834

Multiagent reinforcement learning has proven remarkably effective at finding near-optimal solutions to complex non-linear control problems when compared to classical schemes. Such problems typically arise when considering power management problems related to advanced power distribution applications, such as micro/smart grids, smart buildings, electric vehicles, and vehicle-to-grid applications. The achievement of balanced synchronized charge/discharge of energy storage systems in real-time is often a critical factor in fulfilling optimized power flow and enhancing battery health and usable lifetime. It is also critical to reducing power losses, supporting energy/power balance, and integration of renewable/intermittent energy. This paper proposes a control adaptation to optimize the power flow of battery energy systems in a DC autonomous microgrid. Multiagent neighbor-to-neighbor information related to the variation in the load participation and measured state of charge is locally exploited to optimize the balance of power storage. The results confirm accurate synchronization of the charge/discharge and enhanced balanced output voltage under an excessive continuous load variation. In addition, for different expectations of real operation, regarding battery capacities, initial states of charge, environmental impacts, and degradation. Furthermore, the independence of the microgrid operation from the number of battery energy storage systems is verified through plug-and-play insertions and removals. © 2023 ieee.

关键词： Electric load flow

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A Heuristic Approach for Resolution of Issues in Power System applications in SCADA/ EMS System at National Load Despatch Centre 10

A Heuristic Approach for Resolution of Issues in Power Syste...

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10th ieee international conference on Power systems, ICPS 2023

作者： Saxena, S.C. Singh, Pramod Shukla, Rohit Kumar, Deepak Singh, Bhavneet Grid Controller of India Limited New Delhi India

ISBN: (纸本)9798350318739

Supervisory Control and Data Acquisition & Energy Management systems (SCADA/EMS) System are extensively used at load despatch Centres worldwide for real time grid operation in Power System. These systems are equipped with several Power System applications (PSA) features as required for operators to make informed decisions. These applications are generic to a great extent and are part of the overall functionality of the SCADA/EMS system. State Estimation (SE), Contingency Analysis (CA), Optimal power flow (OPF), etc. are some of the widely and commonly used applications in SCADA/EMS system. The smooth running of these applications is of prime importance to ensure safe and economic grid operation. Besides providing situational awareness to system operators it also gives meaningful full information about violations, vulnerabilities and operational issues which may result in potential problems in real time grid operation. Solution and results obtained from these applications primarily depends on SCADA data and correct modeling of the power system network. The source of SCADA data may be directly reporting from sub stations or from other control Centres through Inter Control Centre Protocol (ICCP) data. Further regular tuning of key parameters also plays an important role in the successful running of PSA application. This paper provides the issues and their mitigation in heuristic manner which are encountered during the running of these applications at national level i.e. National Load Despatch Centre (NLDC) in India. © 2023 ieee.

关键词： Energy management systems

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Quantum Machine Learning for Network Intrusion Detection systems, a Systematic Literature Review 2

Quantum Machine Learning for Network Intrusion Detection Sys...

引用

2nd ieee international conference on AI in Cybersecurity, ICAIC 2023

作者： Nicesio, Otavio Kiyatake Leal, Adriano Galindo Gava, Vagner Luiz Institute for Technological Research Artificial Intelligence and Analytics Department São Paulo Brazil

ISBN: (纸本)9798350396966

Quantum computing presents potential advantages over classical computing in terms of computational complexity. Therefore, it is expected for quantum machine learning applications to have improvements in capacity and learning efficiency over classical machine learning methods. This paper aims to present a Systematic Literature Review of articles published between 2017 and 2022, identifying, analyzing, and comparing different proposals of quantum machine learning applications for network intrusion detection systems (IDS). This study focused on identifying papers that implemented quantum machine learning algorithms in the context of intrusion detection systems. The main algorithms found were variational hybrid quantum-classical, with models based on quantum support vector machines and quantum neural networks. Benefits compared to classical models were observed and described, such as reduced training time and improved classification accuracy for attacking traffic. © 2023 ieee.

关键词： Support vector machines

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In-Situ Techniques on GPU-Accelerated Data-Intensive applications 19

In-Situ Techniques on GPU-Accelerated Data-Intensive Applica...

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

作者： Ju, Yi Li, Mingshuai Perez, Adalberto Bellentani, Laura Jansson, Niclas Markidis, Stefano Schlatter, Philipp Laure, Erwin Max Planck Computing and Data Facility Germany Kth Royal Institute of Technology Sweden Cineca Italy Technical University of Munich Germany Friedrich-Alexander-Universität Erlangen-Nürnberg Germany

ISBN: (纸本)9798350322231

The computational power of High-Performance computing (HPC) systems is constantly increasing, however, their input/output (IO) performance grows relatively slowly, and their storage capacity is also limited. This unbalance presents significant challenges for applications such as Molecular Dynamics (MD) and Computational Fluid Dynamics (CFD), which generate massive amounts of data for further visualization or analysis. At the same time, checkpointing is crucial for long runs on HPC clusters, due to limited walltimes and/or failures of system components, and typically requires the storage of large amount of data. Thus, restricted IO performance and storage capacity can lead to bottlenecks for the performance of full application workflows (as compared to computational kernels without IO). In-situ techniques, where data is further processed while still in memory rather to write it out over the I/O subsystem, can help to tackle these problems. In contrast to traditional post-processing methods, in-situ techniques can reduce or avoid the need to write or read data via the IO subsystem. They offer a promising approach for applications aiming to leverage the full power of large scale HPC systems. In-situ techniques can also be applied to hybrid computational nodes on HPC systems consisting of graphics processing units (GPUs) and central processing units (CPUs). On one node, the GPUs would have significant performance advantages over the CPUs. Therefore, current approaches for GPU-accelerated applications often focus on maximizing GPU usage, leaving CPUs underutilized. In-situ tasks using CPUs to perform data analysis or preprocess data concurrently to the running simulation, offer a possibility to improve this underutilization. © 2023 ieee.

关键词： Graphics processing unit

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Revolutionizing Education through Digital Twin Technology: Key Techniques and applications 2

Revolutionizing Education through Digital Twin Technology: K...

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2nd ieee international conference on Emerging Research in Computational Science, ICERCS 2024

作者： Valarmathie, P. Guruvarshni, G. Rajalakshmi Engineering College Department of Information Technology Chennai India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Digital Twin technology, which was first employed in industry, is now providing exciting opportunities for education. Imagine kids learning through interaction with virtual representations of real-world systems experimenting, making mistakes, and comprehending complex processes in a secure environment. However, today's Digital Twin educational applications often fall lacking when it comes of flexibility and differentiation. The study explores the key techniques used in the digital twin technology in education, includes real-time Data Integration, AI-Driver Feedback Mechanisms, Simulation and Interactive Learning, Personalization, Performance analytics and Learning Insights. The essential aspects of the framework, such as the data collection for gathering raw data, the data processing for preparing, the data transformation for formatting data for the Virtual Learning Environment (VLE), dashboard and data Visualization to provide feedback and insights for educators. Our platform is designed to be adaptable and scalable, making it easy to fit into various educational settings and adapt to different learning goals. This survey aims to make these powerful learning tools accessible to more students, enhancing their education in meaningful ways. © 2024 ieee.

关键词： Federated learning

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Deep Learning Segmentation Models Evaluation for Deforestation Monitoring embedded systems 4

Deep Learning Segmentation Models Evaluation for Deforestati...

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4th international conference on Intelligent Cybernetics Technology and applications, ICICyTA 2024

作者： Careli, Álvaro S. Boas, Evandro C. Vilas Teixeira, Eduardo H. Silva, Elaine C. C. Aquino, Guilherme P. Figueiredo, Felipe A. P. National Institute of Telecommunication Inatel Cyber Security and IoT Laboratory Cs&i Lab. Santa Rita do Sapucaí Brazil National Institute of Telecommunication Inatel IoT Research Group Laboratory Santa Rita do Sapucaí Brazil National Institute of Telecommunication Inatel Wireless and Artificial Intelligence Laboratory Wai Lab. Santa Rita do Sapucaí Brazil

ISBN: (纸本)9798331506490

This work evaluates deep learning segmentation models to propose a deforestation monitoring embedded system. The approach stands for environmental monitoring using remote sensing imagery, edge computing, and a deep learning segmentation model. Thus, the performance of you only look once architecture version 8 (YOLOv8) and Mask Region-based convolutional neural networks (Mask R-CNN) embedded in Raspberry Pi Model 4 regarding Intersection over Union (IoU), mean Average Precision (mAP), and time per image processing metrics is compared. The models are combined with a pixel-based algorithm that analyzes the temporal segmented images to define their forest area percentage for deforestation monitoring and detection. The results demonstrate YOLOv8x model achieved an IoU of 0.762, with a time per image of 0.4777 seconds, while Mask R-CNN R101 FPN 3x obtained an IoU of 0.763, with a time per image of 0.2669 seconds. The average times for YOLOv8 ranged from 0.0434 to 0.4777 seconds, and for Mask R-CNN from 0.1969 to 0.2669 seconds. Finally, this work proposes evaluating the model's performance when working with generative AI models Dall-e, Craiyon, and Tess-AI to create a synthetic dataset to augment the initial one with synthetic samples and improve the model's training with a large dataset. The Dall-e has been shown to outperform the others regarding the IoU metric, which was suggested to augment datasets with synthetic samples. © 2024 ieee.

关键词： Deforestation Environmental Monitoring Mask R-CNN Segmentation YOLOv8

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Exploring Segnet Architectures for iGPU embedded Devices 15

Exploring Segnet Architectures for iGPU Embedded Devices

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15th international Joint conference on Computational Intelligence, IJCCI 2023

作者： Chaudron, Jean-Baptiste Mascarenas-Gonzalez, Alfonso ISAE-SUPAERO Université de Toulouse France

ISBN: (纸本)9789897586743

Image segmentation is an important topic in computer vision which encompasses a variety of techniques to divide image into multiple areas or sub-regions in order to extract meaningful information. Artificial Neural Networks (ANNs), biologically inspired algorithms, are nowadays widely used to perform such tasks and popular models are usually based on encoder-decoder architectures. Segnet was one of the first proposed model of this kind in the literature and, despite its efficiency, it has several drawbacks for embedded systems especially due to the huge amount of arithmetic operations and memory used in the original version. However, its simple sequential based architecture offers interesting properties for optimization and real-time analysis. In this paper, we deeply investigate how to tune and adapt original Segnet architecture to allow efficient run-time execution on embedded targets equipped with an iGPU. We propose our own implementation design which is experimented and validated on iGPU embedded devices for two state of the art datasets from Unmanned Aerial Vehicle (UAV) applications. © 2023 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： embedded systems

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Scene-level Tracking and Reconstruction without Object Priors

Scene-level Tracking and Reconstruction without Object Prior...

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ieee/RSJ international conference on Intelligent Robots and systems (IROS)

作者： Chang, Haonan Boularias, Abdeslam Rutgers State Univ Dept Comp Sci New Brunswick NJ 08901 USA

ISBN: (数字)9781665479271

ISBN: (纸本)9781665479271

We present the first real-time system capable of tracking and reconstructing, individually, every visible object in a given scene, without any form of prior on the rigidness of the objects, texture existence, or object category. In contrast with previous methods such as Co-Fusion and MaskFusion that first segment the scene into individual objects and then process each object independently, the proposed method dynamically segments the non-rigid scene as part of the tracking and reconstruction process. When new measurements indicate topology change, reconstructed models are updated in real-time to reflect that change. Our proposed system can provide the live geometry and deformation of all visible objects in a novel scene in real-time, which makes it possible to be integrated seamlessly into numerous existing robotics applications that rely on object models for grasping and manipulation. The capabilities of the proposed system are demonstrated in challenging scenes that contain multiple rigid and non-rigid objects. Supplementary material, including video, can be found at https://***/changhaonan/STAR-no-prior.

关键词： Geometry Deformable models Visualization Stars Grasping Streaming media real-time systems

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks 23

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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23rd ieee international conference on Machine Learning and applications, ICMLA 2024

作者： Poole, Benjamin Ratnakumar, Rajan Madurasinghe, Dulip Tharaka Kummerle, Christian Venayagamoorthy, Ganesh Kumar Lee, Minwoo University of North Carolina Department of Computer Science Charlotte United States University of Pretoria Department of Electrical Electronic and Computer Engineering South Africa Clemson University Real-Time Power and Intelligent Systems Laboratory United States

ISBN: (纸本)9798350374889

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases. © 2024 ieee.

关键词： Undirected graphs

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Programming Autonomous Machines Special Session Paper 22

Programming Autonomous Machines Special Session Paper

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22nd ACM SIGBED international conference on embedded Software (EMSOFT) as part of the embedded systems Week (ESWEEK)

作者： Liu, Shaoshan Li, Xiaoming Geng, Tongsheng Zuckerman, Stephane Gaudiot, Jean-Luc PerceptIn Santa Clara CA 95054 USA Univ Delaware Newark DE USA Univ Calif Irvine Irvine CA USA CY Cergy Paris Univ ENSEA Lab ETIS UMR 8051CNRS F-95000 Cergy France

ISBN: (数字)9781665472982

ISBN: (纸本)9781665472982

One key technical challenge in the age of autonomous machines is the programming of autonomous machines, which demands the synergy across multiple domains, including fundamental computer science, computer architecture, and robotics, and requires expertise from both academia and industry. This paper discusses the programming theory and practices tied to producing real-life autonomous machines, and covers aspects from high-level concepts down to low-level code generation in the context of specific functional requirements, performance expectation, and implementation constraints of autonomous machines.

关键词： Programming Languages Compiler Autonomous Machine computing Runtime systems

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