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Voltage Regulation Strategy for Medium and Low Voltage Distribution Network Based on Power Electronics 11

Voltage Regulation Strategy for Medium and Low Voltage Distr...

引用

11th China international conference on Electricity Distribution, CICED 2024

作者： Liang, Wenru Yang, Lulu Li, Jingru Zhao, Yuchen Yan, Jingxian Distribution Network Planning and Design Center State Grid Economic and Technological Research Institute Beijing China State Grid Economic and Technological Research Institute Beijing China

ISBN: (纸本)9798350368345

As distributed energy resources and power electronics technology undergo swift advancements, high-power equipment and nonlinear loads have been widely applied. It has greatly improved productivity but also led to multiple aspects of power quality concerns in distribution network, such as voltage deviation, harmonics, and three-phase imbalance. Control strategy with four-leg three-phase four-wire (TP-FW) parallel active power filter alongside static synchronous compensator is presented in this paper. The proposed strategy, along with the refined design methodology for key parameters, could effectively deal with power quality issues under conditions of undesirable grid voltage and asymmetric loads. The effectiveness of the proposed method is verified through simulation results in MATLAB/Simulink, which could ensure the stability of the voltage in medium and low voltage distribution network and promote the improvement of power quality. © 2024 IEEE.

关键词： Power distribution networks

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A Fault Diagnosis Method for Multilevel Inverters Based on parallel computing Architecture 3

A Fault Diagnosis Method for Multilevel Inverters Based on P...

引用

3rd IEEE international conference on Industrial Electronics for Sustainable Energy Systems, IESES 2023

作者： Yu, Yue Wang, Tianzhen Zhang, Fan Logistics Engineering College Shanghai Maritime University Shanghai China

ISBN: (纸本)9798350324754

Cascaded H-bridge multilevel inverter (CHMLI) is currently one of the most promising medium-high power grid connected inverter topologies. As the number of CHMLI power switch tubes increases, more computing resources are needed for the training and decision-making of power switch tube fault diagnosis, which aggravates the impact of faults. Therefore, it is urgent to develop a more rapid fault diagnosis computing architecture. To solve these problems, a parallel computing architecture based fault diagnosis method for multilevel inverters is proposed, which utilizes the parallel computing advantages of memristor cross arrays to improve the diagnostic efficiency of traditional methods. In this method, the output phase voltage of the inverter system is selected as the input signal of fault diagnosis. After data preprocessing, the weight storage and parallel calculation are carried out through the memristor cross arrays, and then the fault classification results are obtained through full connection. Compared with traditional methods, the proposed fault diagnosis method has advantages such as parallel computing, storage and fusion, and has shorter diagnosis time while ensuring diagnostic accuracy. Experimental results show that the proposed method is fast, effective and accurate. © 2023 IEEE.

关键词： Failure analysis

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Edge computing Scheduling Method for Related Tasks of Industrial Internet of Things 29

Edge Computing Scheduling Method for Related Tasks of Indust...

引用

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

作者： Tan, Aiping Wang, Chang Dong, Chenglong Wang, Yan Liaoning University College of Information China

ISBN: (纸本)9798350330717

The Industrial Internet of Things (IIoT) generally uses cloud computing mode to process tasks. However, in cases of excessive task volume, a high workload will lead to significant processing delays. Edge computing, due to its ability to achieve load balancing and reduce processing latency, is an effective solution. Existing research mainly focuses on how to schedule multiple tasks and multiple edge nodes effectively but lacks consideration of the correlation between tasks. In view of the shortcomings of current work, this paper defines an edge computing scheduling problem for related tasks in IIoT scenario and proves that this problem is NP-Hard. A related task scheduling method is proposed based on a Directed Optimization Genetic Algorithm(DO-GA). Finally, an experimental environment is set up to perform a performance comparison analysis of the proposed algorithm. Experimental results show that, in terms of convergence, scheduling success rate and stability, the method proposed in this paper outperforms traditional Genetic Algorithm(GA) and Particle Swarm Optimization(PSO). © 2023 IEEE.

关键词： Edge computing IIoT Task Scheduling

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AeneasHDC: An Automatic Framework for Deploying Hyperdimensional computing Models on FPGAs

AeneasHDC: An Automatic Framework for Deploying Hyperdimensi...

引用

international Joint conference on Neural Networks (IJCNN)

作者： Angioli, Marco Jamili, Saeid Barbirotta, Marcello Cheikh, Abdallah Mastrandrea, Antonio Menichelli, Francesco Rosato, Antonello Olivieri, Mauro Sapienza Univ Rome Dept Informat Engn Elect & Telecommun Rome Italy

ISBN: (纸本)9798350359329;9798350359312

Hyperdimensional computing (HDC) is a bioinspired learning paradigm, that models neural pattern activities using high-dimensional distributed representations. HDC leverages parallel and simple vector arithmetic operations to combine and compare different concepts, enabling cognitive and reasoning tasks. The computational efficiency and parallelism of this approach make it particularly suited for hardware implementations, especially as a lightweight, energy-efficient solution for performing learning tasks on resource-constrained edge devices. The HDC pipeline, including encoding, training, and comparison stages, has been extensively explored with various approaches in the literature. However, while these techniques are mainly oriented to improve the model accuracy, their influence on hardware parameters remains largely unexplored. This work presents AeneasHDC, an automatic and open-source platform for the streamlined deployment of HDC models in both software and hardware for classification, regression and clustering tasks. AeneasHDC supports an extensive range of techniques commonly adopted in literature, automates the design of flexible hardware accelerators for HDC, and empowers users to easily assess the impact of different design choices on model accuracy, memory usage, execution time, power consumption, and area requirements.

关键词： Hyperdimensional computing Automatic Hardware Acceleration FPGA Classification Clustering Regression Edge AI

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1st international conference on Renewable Energy, Green computing, and Sustainable Development, REGS 2023

1st International Conference on Renewable Energy, Green Comp...

引用

1st international conference on Renewable Energy, Green computing, and Sustainable Development, REGS 2023

ISBN: (纸本)9783031586064

The proceedings contain 15 papers. The special focus in this conference is on Renewable Energy, Green computing, and Sustainable Development. The topics include: Power Distribution System Power Quality Enhancement with Custom Power Devices Utilizing Machine Learning Techniques;diabetes Prediction Using Logistic Regression;Performance Analysis of Low Voltage Ride Through Techniques of DFIG Connected to grid Using Soft computing Techniques;passive Islanding Detection and Load Shedding Techniques in Micro grids: A Brief Review;Rapid Convergence of New FP Iterative Algorithm;a Case Study: Design Based Model of Electric Vehicle;Asymmetrical Current Source Multilevel Inverter with Multicarrier PWM Strategies;optimizing the Technological Efficiency of Hybrid Photovoltaic Systems to Fulfill the Energy Requirements of Emergency Shelters for Refugees of the Ukrainian War;a Manual Charging Adaptive Energy Efficient Bike;review of Optimization Tools Used for Design of distributed Renewable Energy Resources;Optimal Power Tracking for grid-Connected Doubly Fed Induction Generator (DFIG) Wind Turbines Using OPO Algorithm;image Inpainting for Object Removal Application using Improved Patch Priority and Exemplar Patch Selection;Reversible Logic Toffoli Gate Priority Encoder for Effective Nano-Scale Application in QCA Paradigm.

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A distributed Technique of Optimization Problems Solving Based on Efficient Workload Assignment 27th

A Distributed Technique of Optimization Problems Solving Ba...

引用

27th international conference on distributed Computer and Communication Networks, DCCN 2024

作者： Klimenko, Anna Institute of IT and Security Technologies Russian State University for Humanities Moscow Russia

ISBN: (纸本)9783031808524

Currently, the efficiency improvement of complex technical systems is an urgent scientific task, which in numerous cases is formalized via optimization problem and solved by some well-known optimization methods. Metaheuristics are used intensively in this field due to their possibility to generate acceptable solutions in restricted time periods. However, it is known that with the decrease of performing time the solution accuracy of metaheuristics degrades, as well as algorithm’s convergence depends on its exploitation/exploration features and preliminary set parameters. parallel metaheuristics implementations are used to improve algorithms performance. In this paper a distributed technique of computationally-hard optimization problems solving based on efficient workload assignment is presented and described. The novelty of the technique proposed is that the additional procedure of metaheuristics instances forming and distribution is added, which creates metaheuristic instances of a various computational complexity and assigns them to the most suited computing resources to perform parallel independent runs within the operation time restriction. The workload generated by metaheuristics blocks is distributed efficiently by means of metaheuristics portfolio usage. The latter makes it possible to improve the metaheuristics instances distribution and computational complexities forming so as to put the largest metaheuristic block to the node with the highest performance with the iterative improvement of the distribution, considering the full set of computing resources constraints and criteria. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optimization algorithms

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Cost-Effective Dynamic Alliance Pricing Mechanism Based on distributed Edge-Intelligence 21

Cost-Effective Dynamic Alliance Pricing Mechanism Based on D...

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21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Cao, Zhihan Zheng, Xi Guo, Jianxiong Jia, Weijia Xu, Yang Wang, Tian Beijing Normal University Zhuhai China Fudan University Shanghai China Macquarie University Australia Hunan University Changsha China

ISBN: (纸本)9798350329223

In Internet of Thing (ToT) system based on edge intelligence, pay-for-use demand has become a consensus, and the pricing of ToT services has attracted the attention of academia and industry. The pricing method based on non-cooperative game allows edge service providers to compete fairly, effectively preventing edge nodes from malicious bidding. However, in this scenario, a task can only be assigned to one edge vendor. Because only one winner can profit each time, it is easy to create a large number of edge service providers who cannot profit for a long time due to insufficient incentives. To this end, a dynamic alliance pricing mechanism based on distributed edge-intelligence is proposed. Edge service providers can freely choose to form an edge dynamic alliance, which not only retains the independence of edge nodes, but also makes full use of the advantages of mutual cooperation between nodes. Tn order to prevent abnormal behaviors of edge nodes, we set up necessary restrictive rules. At the same time, we designed a privacy-enhanced joint pricing prediction algorithm to screen the alliance's candidate solutions to improve pricing efficiency and edge benefit. The experimental results show that, compared with the traditional game method, the performance of the mechanism we proposed improves the utilization rate of edge resources by 32.76%-61.37%. Meanwhile, the prediction accuracy was improved by 16.47%38.86%, and the average prediction time was reduced by 42.81 %-65.57%. © 2023 IEEE.

关键词： Profitability

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Research on Dynamic Protection Technology of Network Security Spatial Data Assets Based on Neural Networks 5

Research on Dynamic Protection Technology of Network Securit...

引用

5th international conference on Information Science, parallel and distributed Systems, ISPDS 2024

作者： Meng, Liang Meng, Chunzhi Zeng, Mingfei Chen, Lina Information Communication Branch of Guangxi Grid Company Nanning China

ISBN: (纸本)9798350352719

With the rapid development of network technology, the security of data assets in cyberspace has become crucial. This article proposes a dynamic security detection system based on neural networks to address the shortcomings of existing protection mechanisms. This system simulates biological neural networks and integrates key functions such as identity authentication, anomaly detection, and penetration testing, aiming to achieve real-time protection of data assets. The study elaborated on the system design and explored its potential in improving network security performance. In response to the challenges of model training resource consumption, missing data labels, and vulnerability to attacks, this study proposes optimization strategies such as data augmentation, label mapping, and adversarial training, which significantly improve the robustness and accuracy of the model. Experimental verification shows that the proposed technical solution has significant advantages in anomaly detection and robustness. This study provides a new perspective for the field of network security and lays the foundation for future research and practice. © 2024 IEEE.

关键词： Data asset protection Dynamic security detection Network security Neural network architecture

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Efficient Large Scale Reverse-time Migration Imaging Computation based on distributed Spark Cluster with GPUs 21

Efficient Large Scale Reverse-time Migration Imaging Computa...

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作者： Wangzhang, Suhui Yang, Ruizhang Gu, Rong Li, Bo Liu, Dingjin Wang, Zhaokang Nanjing University State Key Laboratory for Novel Software Technology China Sinopec Geophysical Research Institute China Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology China

ISBN: (纸本)9798350329223

The reverse-time migration (RTM) imaging algorithm, which is used for complex underground structure analysis, is known as one of the current mainstream method of high-precision seismic imaging. Nowadays, as the fast development of seismic data acquisition technology, there exists large amount of seismic data. Thus, the requirement for the efficient computation of the reverse-time migration (RTM) imaging algorithms over large-scale data have been increased greatly. To address this problem, in this paper, we propose a distributed reverse-time migration imaging algorithm based on the widely-used Spark platform first. Based on that, we design and implement a RTM computation solution based on Spark and GPU with several optimizations, including pre-shuffle&caching and load balancing. We also analyze the fault tolerance capability of the proposed distributed algorithm. We evaluate the proposed methods on the typical three-dimensional wavefild benchmark dataset. Experimental results show that the performance of the Spark based reverse-time migration imaging algorithm can scale up near linearly with computing nodes. In addition, the GPU+Spark based version has significant performance improvement on execution efficiency over the Spark-based algorithm. Moreover, the proposed optimization strategies also take effect. In addition, the method proposed in this paper has been applied in the real-world environment of one of the top oil exploration companies world wide, and has achieved significant improvement under the premise of the algorithm correctness guarantee. © 2023 IEEE.

关键词： Graphics processing unit

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Intelligent Islanding Classification with MLPNN for Hybrid distributed Energy Generations in Microgrid System

Intelligent Islanding Classification with MLPNN for Hybrid D...

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2023 IEEE international conference on Intelligent and Innovative Technologies in computing, Electrical and Electronics, ICIITCEE 2023

作者： Saifi, Izhar Ahmad Haque, Ahteshamul Amir, Mohammad Bharath Kurukuru, V.S. Jamia Millia Islamia Department of Electrical Engineering India Silicon Austria Labs Power Electronics Research Division Austria

ISBN: (纸本)9781665462631

Due to the rise in penetration of distributed energy resources (DERs) and energy storage systems (ESSs) into the microgrid (μG) system, the abrupt disconnections of DERs and ESSs might have an effect on the stability and dependability of the entire μG system. It is important to take into account the low-voltage ride-through (LVRT) capabilities to keep the μG system operating steadily. In order to quickly identify grid abnormalities and categorize islanding scenarios, this paper attempts to develop an islanding detection technique. The procedure involves generation of data samples corresponding to different grid abnormalities and training them with a machine learning based classifier. To achieve this process, a grid-connected (GC) three-phase μG system is simulated and compiled in Typhoon HIL application software to get distinct data sets for different grid fault conditions. Additionally, the data is trained using the multilayered perceptron neural network (MLPNN) classifier to create the islanding classification process. The trained classifier has 99.5% effectiveness and can classify data at a rate of about 170000 observations per second. © 2023 IEEE.

关键词： Classification (of information)

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