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PSO Optimized Observer Based Load Frequency controller with Reduced Particle Dimension 3

PSO Optimized Observer Based Load Frequency Controller with ...

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3rd IEEE International conference on Power Electronics, Intelligent control and Energy Systems, ICPEICES 2024

作者： Srivastava, Geeta Ray, Anjan Kumar National Institute of Technology Sikkim Department Electrical and Electronics Engineering Ravangla India

ISBN: (纸本)9798350387193

In order to maintain the stability and reliability of a power system, it is essential to keep the frequency of the system within prescribed bounds. In this work, a PSO optimized observer based load frequency controller is proposed with reduced particle dimension. Output of each subsystem having dynamics is considered as the prospective state of the system. Unavailability of measured output leads to the requiremnt of design of an observer. Unlike the controller gains as the dimensional elements of the particles, the prospective poles of the system are considered as the components of each particle leading to the design of the controller. Such choice immediately removes the burden of ensuring system stability at each iteration for all particles. Furthermore, without using explicit observer gains, a set of offsets are used for complete design of an observer based controller while keeping both the controller and the observer poles of the system within a prescribed boundary. Simulation results and comparative study with existing methods demonstrate the better performance of the proposed approach. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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Coordinated method for islanding division and component restoration in active distribution networks aimed at enhanced resilience 3

Coordinated method for islanding division and component rest...

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2024 3rd International conference on New Energy, Energy Storage and Power Engineering, NESP 2024

作者： Wang, Cunyou Xu, Hang Chang, Xinli Li, Cancan School of Automation and Electrical Engineering University of Jinan Shandong Jinan250022 China Dispatch Control Center State Grid Weihai Electric Power Company Shandong Weihai264620 China Ltd Shandong Huizhi Electric Power Technology Company Shandong Jinan250000 China

Due to the uncertainties associated with distributed generation output and load power consumption, distribution networks encounter challenges in component restoration and resilience enhancement. This paper proposes a three-layer min-max-min robust optimization model designed for coordinated optimization of islanding division and component restoration in the distribution network. Firstly, constraint models for distributed energy storage, distributed generation, load power consumption, line reconfiguration, component restoration, islanding division, and line power flow are established. Secondly, to maximize the resilience of the distribution network, a robust optimization model is performed with the objective function of minimizing the total load shedding during the component restoration phase. Finally, validated on the IEEE 33-node distribution system, the proposed method demonstrates that it can significantly enhance the resilience of the distribution network, enabling more precise and effective optimization scheduling. © Published under licence by IOP Publishing Ltd.

关键词： Power distribution networks

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Intelligent Detection of Partial Discharge Based on Deep Integration Modeling with Multi-BP Neural networks 3

Intelligent Detection of Partial Discharge Based on Deep Int...

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3rd International Joint conference on Information and Communication Engineering (JCICE)

作者： Qi, Haotian Yang, Yifei Tan, Minjia Jiangsu Univ Sci & Technol Sch Automat Zhenjiang Jiangsu Peoples R China Jiangsu Univ Sci & Technol Ocean Coll Zhenjiang Jiangsu Peoples R China

ISBN: (纸本)9798350388732;9798350388725

Low-voltage switchgear is responsible for power collection and distribution, system monitoring and protection, control and protection of electrical equipment in power plants, which is one of the important guarantees for the operation safety of power plants. Accurately identifying the type of Partial Discharge (PD) faults in Low-Voltage switchgear is of great significance for understanding its real-time operating status and operation and maintenance. The degree of power plant intelligence is improving with the continuous development of fault detection technology. Therefore, to meet the demand for low-voltage switchgear PD detection intelligence, this paper first develops a deep integration model for intelligent detection of PD diagnosis in low-voltage switchgear with the multi-BP neural networks. The multi-BP neural networks are established by the traditional BP neural network which using the Particle Swarm optimization (PSO) to optimize the parameters. Then, this paper constructs a deep integration model to analyze the feature vectors of Phase-Resolved Partial Discharge (PRPD) of typical PD phenomenon, so as to identify the type of PD in switchgear. Finally, the proposed model is experimentally verified by an online Ultra-High Frequency (UHF) PD measurement system for PRPD, and the verification results prove that the accuracy of the proposed model in identifying the type of discharge reaches 96.25%, which is superiority over traditional CNN neural networks and single BP neural network models.

关键词： low-voltage switchgear partial discharge (PD) fault diagnosis BP neural network integration model

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3rd International conference on Signal Processing and Machine Learning, COnf-SPML 2023

3rd International Conference on Signal Processing and Machin...

引用

3rd International conference on Signal Processing and Machine Learning, COnf-SPML 2023

The proceedings contain 64 papers. The topics discussed include: diffusion models-based data augmentation for the cell cycle phase classification;data augmentation by using GANs and image transformation in facial emotion classification;researches advanced in fine-grained image classification based on deep learning;performance analysis of attention mechanism and teacher forcing ratio based on machine translation;collaborative obstacle avoidance algorithm and simulation of swarm robots with limited viewing angle;the advance of underdetermined linear regression optimization based on implicit bias;bird audio recognition based on convolutional neural network;the configuration optimization of the plants in vertical farming by 3D modeling;robot navigation based on multi-sensor fusion;a study on the model predictive control based on convolutional neural network;channel capacity on visible light communication;and the gap between theoretical computational theories and application models.

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Traffic Flow optimization Using CNN and GCN for Adaptive Signal control 3

Traffic Flow Optimization Using CNN and GCN for Adaptive Sig...

引用

3rd IEEE Asian conference on Innovation in Technology, ASIANCON 2023

作者： Kolte, Sanket Ingle, Anup Kshirsagar, Ketki Gawande, P.G. Kumbhar, Kalpana A. Kawchale, Rupa S. Vishwakarma Institute of Information Technology Department of Electronics and Telecommunication Pune India

ISBN: (纸本)9798350302288

The number of vehicles is increasing every day, and hence traffic congestion, especially in intersections, is becoming a major issue. This paper presents a novel approach for traffic analysis and signal prediction using a combination of deep learning techniques.3000 images were utilized to train on CNN (Convolutional Neural network) for spatial feature extraction using the Yolov5 algorithm. Then those features were given to the GCN (Graph Convolutional network) as input by creating a DGL graph. The findings from this research provide valuable insights into improving traffic flow efficiency and can be applied in various sectors such as logistics, transportation, and urban planning. © 2023 IEEE.

关键词： Message passing

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Edge Computing Resource Optimal Dispatch in Distribution network Considering the Power Tasks Uncertainty 3

Edge Computing Resource Optimal Dispatch in Distribution Net...

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3rd International conference on Energy, Power and Electrical Technology, ICEPET 2024

作者： Cai, Tiantian Chen, Junjian Li, Junye Liang, Xuran Li, Xiaohua Cai, Zexiang Hu, Ming Yang, Bin Power Grid Research Institute Co. Ltd. Technology RandD Center China Southern Power Grid Digital Guangzhou China South China University of Technology School of Electric Power Engineering Guangzhou China

ISBN: (纸本)9798350352658

With the increasing penetration rate of new energy in the power grid, the tasks scenarios faced by the power system are becoming increasingly complex. This complexity leads to the spatiotemporal uncertainty about resource requirements of terminal task for edge computing. Traditional resource scheduling schemes are configured with constant values or over-provisioned in each power business scenario, resulting in low resource utilization. A data-driven model to optimize scheduling resources, which can be for edge computing terminals in distribution network, is proposed to meet the uncertain resource requirements. Firstly, a power tasks resource demand model considering source-load uncertainty is established, and the boundary conditions of adjustable resources are established by utilizing the adjustability of power tasks with different delay and priority requirements. Secondly, considering the multiple costs consumed in the process of power tasks scheduling, a two-stage optimization model is established with the comprehensive computing resource balance and operation cost as the objective function. Finally, the simulation results show that the proposed model has a positive effect on improving the balance and economy of distribution network edge computing resources compared with the traditional optimization model. © 2024 IEEE.

关键词： Power distribution networks

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Adaptive SOM neural network algorithm 3

Adaptive SOM neural network algorithm

引用

3rd International conference on Artificial Intelligence, Automation, and High-Performance Computing, AIAHPC 2023

作者： Wang, Xin Yan, Taishan School of Information Science and Engineering Hunan Institute of Science and Technology Hunan Yueyang China Hunan Institute of Science and Technology School of Information Science and Engineering China Key Laboratory of Hunan Province on Intelligent Control and Optimization of Complex Industrial Logistics System Hunan Yueyang China

ISBN: (数字)9781510666580

ISBN: (纸本)9781510666573

In order to improve the performance of traditional SOM model, an adaptive SOM neural network algorithm is proposed. By introducing the error square sum solution formula, the clustering error of the model sub-data set is calculated by using the method of cutting the data set, and the change trend of the clustering error is quickly obtained. Then the learning rate of the SOM network is adaptively adjusted according to this, and the convergence speed is accelerated on the basis of ensuring that the network is fully trained, thereby improving the performance of the SOM network. © 2023 SPIE.

关键词： Errors

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A Robust optimization Model for Optimal Charging and Discharging Coordination of Plug-in Electric Vehicle in Microgrid 3

A Robust Optimization Model for Optimal Charging and Dischar...

引用

3rd IEEE International conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Bilal, Mohd Mohammad, Arshad Bakhsh, Farhad Ilahi Snd College of Engineering and Research Center Nashik India Aligarh Muslim University Aligarh India Srinagar India

ISBN: (纸本)9798350304732

The necessity for developing an alternative ecofriendly transport network has been emphasized by the harmful environmental effects of employing fossil fuel-powered vehicles. Plug-in Electric Vehicle (PEVs) are introduced to reduce the steadily rising energy consumption and carbon emissions of urban transport. However, the potential advantages of electrification as a particular and dependable source may be diminished due to limitations on the electrical grid caused by the rising demand for mobility. A power grid may have severe interruption due to the green energy sources' such as Photovoltaic (PV) arrays reliance on the weather to produce electricity. In the meanwhile, unregulated PEV charging could result in a load surge on the electrical system. In this regard, this article investigates the charging and discharging of PEVs with the goal of minimizing charging costs. The simulation results for the considered scenario suggest that the given optimization strategies can satisfy the required objective. © 2023 IEEE.

关键词： Electric vehicles Microgrid optimization Scheduling Solar photovoltaic

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optimization of Clustering, and QOS Routing in Wireless Multicast Ad-Hoc networks with Topology control 3

Optimization of Clustering, and QOS Routing in Wireless Mult...

引用

3rd International conference on Smart Generation Computing, Communication and networking, SMART GENCON 2023

作者： Ghumman, Sukhman Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350319125

In this study, we provide a novel approach to multicast wireless ad hoc network routing with cluster QoS optimization. The notion of topology compression is used to quantify the effect of multicast heterogeneity on scheduling by use of a compressed multiplex topology matrix. Topology matrices are defined by first identifying all feasible multicast pathways, including network-coded paths. In a conflict-free environment provided by careful scheduling, a selection of these pathways with optimal rates is selected to optimize feasible throughput and capacity per sum of transmission powers along the route. As a result, this optimization yields not one but two improvements: one in the form of performance enhancement due to planning on a condensed network topology, and another in the form of mixed route selection optimizations that strike a fine balance between multicast variety and network coding. As an example, we provide the numerical findings for a basic wireless butterfly network. If throughput is the only metric of importance, then either a network coded design or simple routing may be optimal, depending on the specifics of the system. However, a collection of simple routing pathways is optimal if throughput is prioritized above power consumption. In this study, we examine the problem of energy-efficient topology management in ad hoc wireless connections. We create an Energy Effective Topology controller method that incorporates a pertinent QoS Routing system in order to identify a network architecture that has maximum network lifespan fulfill QoS criteria, which includes traffic needs, hop bound, and bandwidth. The combined analytical along with numerical findings show that, when compared to algorithms that are solely concerned with reducing energy consumption, this optimal method can achieve the optimum network lifespan, which is a genuine period of time and more important and reasonable than merely reducing energy consumption. © 2023 IEEE.

关键词： and Topology control optimization of Clustering QOS Routing Wireless Multicast Ad-Hoc networks

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2023 3rd International conference on Electrical Engineering and control Science, IC2ECS 2023

2023 3rd International Conference on Electrical Engineering ...

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3rd International conference on Electrical Engineering and control Science, IC2ECS 2023

ISBN: (纸本)9798350382426

The proceedings contain 322 papers. The topics discussed include: electromagnetic design of 10kV high voltage induction motor based on Ansoft Maxwell;efficiency promotion strategy for three-level ANPC-DAB-based DC-DC converter with wide-range multi-objective optimization method;nonlinear data classification of power Internet of Things considering transient and steady state;analysis of early warning indicators for high voltage power cable tunnel fire based on experimental data;real-time evaluation model construction of GIS conductor thermal state based on PSO-BP neural network;research and application of CPS-based automatic circulation system for the entire silk production process;and transmission line relay protection fault-tolerant information sharing and joint analysis method.

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