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检索条件"任意字段=4th IEEE International Conference on Distributed Computing in Sensor Systems"

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Performance Assessment of PV-SH-BES System with Mode Transfer Capabilities 4

Performance Assessment of PV-SH-BES System with Mode Transfe...

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4th ieee international conference on computing, Power and Communication Technologies, GUCON 2021

作者： Tyagi, Shalvi Singh, Bhim Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi110016 India

ISBN: (纸本)9781728199511

Synchronization of AC sources is a challenging task due to the variation in voltage, frequency and jump in phase angle at the instant of mode transfer. Conventional SRF-PLL (Synchronous Reference Frame-Phase Locked Loop) fails to extract the frequency and phase at unbalanced and distorted voltages conditions. therefore, a frequency adaptive filter (FAF) is used to estimate the phase angles of the voltages of the grid and the hydro generator, which are harmonics-free. the microgrid is found capable of driving in both islanded mode and grid connected mode. Moreover, this work also utilizes a block sparse normalized proportionate least mean M estimate based (BS-NPLME) adaptive filter to extract the fundamental element of the load currents and the hydro generator currents in both the modes to generate the switching pulses. Besides, to improve the reliability of the system, a battery energy storage (BES) is integrated to the microgrid (MG) along with solar PV panel to harness the solar energy. An increment conductance (InC) maximum power point algorithm (MPPA) is used to extract the peak power. Furthermore, the ancillary functions delivered by the power conditioning unit, are achieved as suppression of harmonics and power factor correction of the grid. the reactive power is compensated by power conditioning unit (PCU). this system is simulated in MATLAB platform and obtained results are presented to demonstrate these functions of MG. © 2021 ieee.

关键词： distributed power generation

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Comparative Study of Enhanced Power Quality for Lump vs. distributed FACTS Devices 4

Comparative Study of Enhanced Power Quality for Lump vs. Dis...

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4th international conference on Recent Developments in Control, Automation and Power Engineering, RDCAPE 2021

作者： Anand, Vikash Shekhar, Ravi SRM Institute Of Science Technology Dept. Of Electrical And Electronics Engg. Ghaziabad India Amity School Of Engineering Technology Dept. Of Electrical Engg. Patna India

ISBN: (纸本)9781665414296

Advancement in FACTS technology have proven to be very useful in improving the quality and performance of power systems. Among FACTS devices, UPFC is well known as versatile device which control all the three parameters viz. Voltage, Angle and Line Impedance concurrently for uninterrupted power operation. Recent innovating idea gives birth to innovative FACTS device-distributed Power Flow Controller. It is modified form of UPFC employed distributed FACTS concept. Underneath background, this paper offering a prospective study of UPFC and DPFC to reduce voltage sag, current swell and harmonic deduction of susceptible load through a control model base on D-Q axis method and PI controller in SIMULINK environs. the simulated results authenticate facility of DPFC in rectifying the grade of power remarkably. © 2021 ieee.

关键词： Flexible AC transmission systems

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Regional distributed Photovoltaic Ultra-Short-Term Prediction Method Based on Multi-task Learning 4

Regional Distributed Photovoltaic Ultra-Short-Term Predictio...

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4th ieee China international Electrical and Energy conference, CIEEC 2021

作者： Wang, Bo Cui, Wei Wang, Cong Xu, Hang China Electric Power Research Institute State Key Laboratory of Operation and Control of Renewable Energy Storage Systems Beijing China Northwest Branch of State Grid Corporation of China Xian China University of Jinan School of Electrical and Engineering Jinan China

ISBN: (纸本)9781728171494

Accurate and fast photovoltaic prediction is of great significance to the economy and safety of smart grid. At present, the existing prediction methods do not consider the correlation between different photovoltaic power stations in the region. In this paper, a regionally distributed photovoltaic ultra-short-term prediction method based on multi-task learning is proposed. Firstly, the LSTM model for photovoltaic prediction is designed, and then the multitask learning is realized through the network parameter sharing mechanism. Due to the full consideration of the correlation between different tasks, the proposed method improves the prediction accuracy. At the same time, the structure of multi-input and multi- output improves training efficiency. An example is given to demonstrate the effectiveness of the proposed method. © 2021 ieee.

关键词： Long short-term memory

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Deep Reinforcement Learning and distributed Solution for Local Network based Optimization of Network Slicing in 5G Communications

Deep Reinforcement Learning and Distributed Solution for Loc...

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distributed computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Naziya Hussain Anoop Singh Taki Alrwbaye Yogesh Ramaswamy L. Ramesh Department of Computer Science and Engineering Mukesh Patel School of Technology Management & Engineering SVKM's NMIMS Deemed to be University Shirpur India Group of IPS Academy Indore India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics and Communication Engineering M.Kumarasamy College of Engineering Karur India

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Nowadays, the advent of 5G networks has introduced the concept of network slicing to allow the multiple independent networks for coexisting on a shared physical infrastructure. However, the existing Mixed-Integer Linear Programming (MILP) solution suffered inefficient resource utilization which has led to reduced network performance and increased latency. Hence, this research proposes Deep Reinforcement Learning and distributed Solution for Local Network (DRL-DSLN) based optimization of network slicing in 5G for efficient resource allocation and low latency. the proposed DRL-DSLN framework increases the network performance where it learns from interactions with adapting to changing conditions and makes real time decisions. Initially, the network slicing architecture is introduced to maintain a high performance of network operation by supporting various services. Here, each network slice has a four specific Key Performance Indicators (KPIs) and these are identified to optimize slicing process. Finally, DRL-DSLN are employed to solve the inter-slice allocation and intra-slice allocation problems respectively. From the results, the proposed DRL-DSLN achieved better results in terms of accuracy (98.27%), precision (97.31%), recall (92.94%), and F1-score (97.53%) when compared to existing Harris Hawks Optimization with Convolutional Neural Network (HHO-CNN) model.

关键词： 5G mobile communication Network slicing Key performance indicator Telecommunication traffic Deep reinforcement learning Real-time systems Mixed integer linear programming Resource management Software defined networking Optimization

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An Extensive Examination of Different Phases Available in Enhancing the Power of WSN

An Extensive Examination of Different Phases Available in En...

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Advance computing and Innovative Technologies in Engineering (ICACITE), international conference on

作者： Rupali Gill Rakesh Kumar Shivani Pant A. Deepika Ramy Riad Al-Fatlawy Sanjeevkumar Angadi Centre of Research Impact and Outreach Chitkara University Rajpura Punjab India Department of Computer Engineering & Applications GLA University Mathura Department of Computer Science engineering Uttaranchal Institute of Technology Uttaranchal University Dehradun India Department of CSE(AI&ML) CMR College of Engineering & Technology The Islamic university Najaf Iraq Computer Science and Engineering Nutan College of Engineering and Research Talegaon Dabhade Pune Maharashtra India

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

In this whole test, we delve into the evolution and transformative ability of Wireless sensor Networks (WSNs), emphasizing the pivotal position of graphical statistics in elucidating the technological improvements and stressful conditions interior this region. By harnessing the energy of the ieee 802.15.4 general, this research provides an in-intensity analysis of WSNs’ operational efficiencies, energy consumption styles, and network resilience through meticulously designed graphs and information visualizations. these graphical representations now not only offer a easy and concise overview of WSN universal performance metrics but additionally spotlight the giant enhancements completed thru the implementation of a singular WSN framework. the examiner’s findings underscore the vital importance of empirical statistics visualization in advancing our knowledge of WSN technologies, offering valuable insights into the optimization strategies that may result in extra power-inexperienced, reliable, and scalable sensor networks. this research contributes to the continued communicate in the subject, paving the manner for future enhancements and programs of WSNs across several sectors.

关键词： ieee 802.15 Standard Wireless communication Measurement Knowledge engineering Wireless sensor networks Visualization Energy consumption

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MeLoN: distributed Deep Learning meets the Big Data Platform 2

MeLoN: Distributed Deep Learning meets the Big Data Platform

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2nd ieee international conference on Autonomic computing and Self-Organizing systems (ACSOS)

作者： Kang, Dae-Cheol Heo, Seoungbeom Jang, Hyeounji Lee, Hyeock-Jin Cho, Minkyoung Kim, Jik-Soo Myongji Univ Dept Comp Engn Yongin South Korea

ISBN: (纸本)9781665443937

Recent advancements in Artificial Intelligence have brought "Deep Learning" frameworks to be a cornerstone for the 4th Industrial Revolution along with "Big Data" platform technologies such as Apache Hadoop. However, efficient processing of deep learning applications has become challenging as the overall sizes of data and model increase rapidly. To address this problem, we can leverage big data platforms that have successfully provided stable storage and data processing capability during the past decade. In this paper, we present design and implementation of MeLoN (Multi-tenant dEep Learning framework On yarN) that can effectively run distributed deep learning applications on top of the big data platform Hadoop. MeLoN takes expected GPU memory usages of a deep learning application as an input parameter, and employs a GPU over-provisioning policy that can improve the overall resource utilization. Evaluation results show that MeLoN can improve the overall system throughput for concurrently running multiple deep learning applications in a Hadoop cluster. MeLoN can bring many interesting research issues related to profiling of expected GPU memory usages of deep learning applications, storage optimizations for deep learning processing, supporting complex deep learning related jobs based on queuing systems which can ultimately contribute to a new data processing framework in the YARN-based Hadoop ecosystem.

关键词： Hadoop YARN Deep Learning GPU Scheduling

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Impact of distributed Generations on Loss Allocation in Grid-connected and Grid-isolated Distribution systems

Impact of Distributed Generations on Loss Allocation in Grid...

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Sustainable Energy and Future Electric Transportation (SEFET), international conference on

作者： Sandeep Kumar Das Supriya Sarkar Electrical Engineering Department National Institute of Technology Durgapur Durgapur India

ISBN: (数字)9798350383997

ISBN: (纸本)9798350384000

Nowadays, in the deregulated environment of power systems, it is crucial to fairly allocate active power loss occurring in the complex distribution network having distributed generations, solar PVs with batteries and EV charging stations to the network users according to their contribution toward loss. the network users (i.e. consumer and owner of distributed generation / distributed generation and EV charging station / solar PVs with batteries / solar PVs with batteries and EV charging station) associated with a node are either charged or benefitted jointly, not separately, according to exact and quadratic loss allocation methods. thus, to separate the allocated loss of the node among the network users, ‘separate distributed generation exact loss allocation’ and ‘separate distributed generation quadratic loss allocation’ methods are proposed in the work. A dummy node, connected to the node, is created to separate the distributed generation from the load virtually. then, exact and quadratic loss allocation methods are applied to the modified network to find the loss allocation of individual network users separately, considering variations in generations and loads. the effectiveness of the loss allocation methods is tested on 33-node radial grid-connected and grid- isolated networks.

关键词： Power demand Transportation Distribution networks Electric vehicle charging distributed power generation Batteries Power systems Resource management

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distributed Dynamic Coverage Based on Labeled Multi-Bernoulli Filter and Anti-flocking 4

Distributed Dynamic Coverage Based on Labeled Multi-Bernoull...

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4th ieee international conference on Electronics Technology, ICET 2021

作者： Zhao, Yuyang Wang, Gang Qu, Yangshen Xue, Rui University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu China University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Beihang University School of Electronics and Information Engineering Beijing China

ISBN: (纸本)9781728176734

In mobile sensor networks, the anti-flocking filter can cover an area of interest to search and detect stationary targets, but it cannot track the states of moving targets. In this work, we couple a labeled multi-Bernoulli filter to the anti-flocking algorithm to deal with both the area coverage requirement and the multiple moving target tracking problem. the mobile nodes can operate in either search mode or tracking mode. the labeled multi-Bernoulli filter is used to label the target tracks when in tracking mode and the anti-flocking algorithm is used to provide maximal area coverage in search mode. When a mobile node is in search mode, the selfishness characteristic will steer this node toward less visited areas where the most likely targets will appear. When an emerging target is detected, the mobile node then switches to tracking mode and estimates the position of the target based on it maintaining this track over time. Simulation results demonstrate the feasibility of the proposed algorithm. © 2021 ieee.

关键词： sensor networks

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Optimal Planning of distributed Photovoltaic in Integrated Electricity and Heat systems 4

Optimal Planning of Distributed Photovoltaic in Integrated E...

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4th international conference on Energy, Electrical and Power Engineering, CEEPE 2021

作者： Gu, Jiting Zhao, Lebing Zhu, Chao State Grid Zhejiang Economic Research Institute Finance and Planning Department Hangzhou China Zhejiang University College of Electrical Engineering Hangzhou China

ISBN: (纸本)9781665433631

In order to tackle the energy crisis, plenty of researches have focused on the integration of renewable energy, such as the wind power and the distributed photovoltaic (DPV). However, the penetration of DPVs in the individual power system is restricted due to the operational limits. Hence, the integrated electricity and heat system (IEHS) has been developed to improve the system flexibility and enlarge the penetration of DPV. this paper presents an optimal planning method of DPV in IEHS, which formulates the optimization model of DPV planning according to the modeling of the multi-energy networks. In order to reduce the curtailment of DPVs, the penalty cost of DPVs is integrated to the objective of the proposed planning model. Numerical simulation results demonstrate the effectiveness of the proposed planning method. © 2021 ieee.

关键词： Wind power

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6th ieee international Workshop on Wireless Communications and Networking in Extreme Environments (WCNEE) Message from the Workshop Chairs

Proceedings - 18th Annual International Conference on Distri...

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Proceedings - 18th Annual international conference on distributed computing in sensor systems, DCOSS 2022 2022年 XX页

作者： Balasubramaniam, Sasitharan Demirors, Emrecan Guan, Zhangyu Sklivanitis, George University of Nebraska-Lincoln United States Northeastern University United States University at Buffalo United States Florida Atlantic University United States

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