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检索条件"任意字段=3rd Euro-NF Conference on Network Control and Optimization"

共 1643 条记录，以下是171-180 订阅

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Bayesian Optimized LSBoost Model for the DC Fault Location in a MT-VSC-HVDC network 3

Bayesian Optimized LSBoost Model for the DC Fault Location i...

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

作者： Pragati, Abha Mishra, Manohar Panigrahi, Rasmi Ranjan Gadanayak, D.A. Department of Ee Odisha India Department of Eee Odisha India Gandhi Engineering College Department of Ee Odisha Bhubaneswar India Department of Eee Odisha India

ISBN: (纸本)9798350387193

The proliferation of direct current (DC) networks within utility systems presents novel challenges for fault localization., distinct from traditional AC grids. Unlike AC systems., DC grids exhibit unique transient behaviors., rendering conventional fault restoration techniques less effective. Precise fault localization is crucial for minimizing costs and ensuring swift power restoration in DC networks., particularly in multi-terminal high-voltage direct current systems utilizing voltage source converters. This study proposes a novel approach for identifying high-resistance grounding defects in HVDC transmission lines., a task known for its complexity. By leveraging Bayesian optimization., we develop a tailored Least Squares Boosting model optimized for DC fault localization in MT - HVDC systems. Extensive experimentation demonstrates our method' s efficacy., achieving a Mean Squared Error (MSE) of 0.1784 and Root-MSE of 0.4224. These results signify improved accuracy and efficiency in fault localization., contributing to the reliability and robustness of DC grid operations., and facilitating effective maintenance and restoration strategies. © 2024 IEEE.

关键词： Bayesian networks

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Research on Reactive Power optimization of Active Distribution network Based on Deep Reinforcement Learning 3

Research on Reactive Power Optimization of Active Distributi...

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3rd Power System and Green Energy conference, PSGEC 2023

作者： Cai, Changchun Liu, Jingna Wang, Yuanyuan Li, Yuanjia Fan, Jinghao He, Jie Hohai University Jiangsu Key Laboratory of Power Transmission & Distribution Equipment Technology Changzhou China College of Information Sciences and Engineering Hohai University Changzhou China Dongying Power Supply Company State Grid Shandong Electric Power Company Dongying China

ISBN: (纸本)9798350340099

The installed proportion of renewable energy is constantly increasing. Due to the intermittency and volatility of renewable energy output, its connection to the power grid can easily affect voltage, leading to voltage violations and safety issues in grid operation. At the same time, considering fast regulation characteristics of Static Var Generator (SVG), a method utilizing deep reinforcement learning is introduced for controlling active distribution network reactive power and voltage. This method converts reactive power and voltage control problem of the power grid under renewable energy access conditions into a Markov Decision Process (MDP), and then constructs a reactive power and voltage control model under renewable energy access. Based on the actual operating environment requirements of the power grid, constraint conditions are proposed, and the model is solved using Deep Deterministic Policy Gradient (DDPG) algorithm. Finally, simulations were performed for improved 33 node instances. The effectiveness of the reactive power and voltage optimization control strategy given in this paper is verified by comparing the reactive power and voltage optimization control effects under different levels of renewable energy access conditions. © 2023 IEEE.

关键词： Reinforcement learning

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719457

The proceedings contain 35 papers. The special focus in this conference is on Computational Electronics for Wireless Communications. The topics include: Design of a Four-Port MIMO Antenna for C-, X-, and Ku-Band Applications;Exploration of Diverse Techniques for Design and optimization of QCA-Based Multiplexer Circuits;melanoma Detection Via Deep Convolutional Neural network;Pattern Diversity mm-Wave Rectangular Ring Shaped MIMO Antenna for 5G Communication and Internet of Things Applications;Flexible Two/Eight-Port MIMO Antenna Configuration for LTE B41/WLAN/Wi-Fi 6E and C-Band Applications;Improvement in Machining Ability of Tungsten Carbide Tool Insert Through Microwave Hybrid Heating During Turning of EN 24 Steel;optimizing Airfare Pricing: A Data-Driven Approach for Affordable Travel Planning;advancements in Emotion Recognition: Systematic Review and Research Roadmap;epoc-Based Electroencephalography Signals Analysis of Different Stress Levels;Two-Element Tilted Rectangular MIMO UWB Antenna for Underwater Communications;deep Learning-Based Maize Crop Disease Detection and Remedial Recommendation System;design of Splash Plate Dielectric Hat Feed for Ka-Band Tracking Radar Applications;soft Computing Paradigms for Load Balancing in Cloud Computing;strategies for Effective network Congestion control: Insights from Parameter-Based Analysis;BICC: Optimizing Sensor network Performance Via an Efficient Bioinspired Iterative Approach with Congestion control;Leakage Power Reduction and Stability Analysis of 5 nm Node GAA CNTFET SRAMs;bandwidth Enhancement and Improvement of Directivity of a Composite Conical Structure in the Presence of a Circular Loop Antenna;development of Compact Dual Polarized Corrugated Feed Horn Antenna with Low Cross-Polarization for Doppler Weather Radar Applications in C-Band;A Review on Recent Breakthroughs and Accomplishments in the Development of FeFET.

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Adaptive Convolutional Neural network for Predicting Steering Angle and Acceleration on Autonomous Driving Scenario 3rd

Adaptive Convolutional Neural Network for Predicting Steerin...

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3rd International conference on optimization, Learning Algorithms and Applications (OL2A)

作者： Vasiljevic, Ive Music, Josip Mendes, Joa Lima, Jose Fac Elect Engn Mech Engn & Naval Architecture Rudera Boskovica 32 Split 21000 Croatia Polytech Inst Braganca Campus Santa Apolonia P-5300253 Braganca Portugal Inst Politecn Braganca Res Ctr Digitalizat & Intelligent Robot CeDRI P-5300253 Braganca Portugal Inst Politecn Braganca Lab Associado Sustentabilidade & Tecnol Reg Monta P-5300253 Braganca Portugal

ISBN: (纸本)9783031530357;9783031530364

This paper introduces a novel approach to autonomous vehicle control using an end-to-end learning framework. While existing solutions in the field often rely on computationally expensive architectures, our proposed lightweight model achieves comparable efficiency. We leveraged the Car Learning to Act (CARLA) simulator to generate training data by recording sensor inputs and corresponding control actions during simulated driving. The Mean Squared Error (MSE) loss function served as a performance metric during model training. Our end-to-end learning architecture demonstrates promising results in predicting steering angle and throttle, offering a practical and accessible solution for autonomous driving. Results of the experiment showed that our proposed network is approximate to 5.4 times lighter than Nvidia's PilotNet and had a slightly lower testing loss. We showed that our network is offering a balance between performance and computational efficiency. By eliminating the need for handcrafted feature engineering, our approach simplifies the control process and reduces computational demands. Experimental evaluation on a testing map showcases the model's effectiveness in real-world scenarios whilst being competitive with other existing models.

关键词： Autonomous Vehicles End-to-end Learning CARLA Simulator Convolutional Neural network Deep Learning

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A power supply recovery algorithm for large area power failure in distribution network based on artificial neural network 3

A power supply recovery algorithm for large area power failu...

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3rd IEEE International conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Jin, Yan Jinman, Luo Siqi, Ye Yuqing, Li Dongguan Power Supply Bureau of Guangdong Power Grid Corporation Dongguan523000 China

ISBN: (纸本)9798350398410

The distribution network directly serves the vast number of users. The distribution network covers every corner of the city and the countryside, and undertakes the function of supplying electricity to small and medium-sized customers. The purpose of this paper is to study the power supply recovery algorithm for large-scale power outages in distribution networks based on artificial neural networks. On the basis of analyzing the characteristics of power distribution network and large-area power outage in distribution network, a method of hot backup and artificial neural network is proposed to solve the problem of power supply recovery from large-area power outage in distribution network method of positioning. Secondly, the distribution network reconstruction model based on CMAC is introduced. In view of the shortcomings of the CMAC algorithm, the spurious technology is introduced to the random mapping method of the associated empty golden section. The CMAC network can quickly and accurately give the switch state when the network loss is the smallest according to different load conditions in the system. It avoids the complicated search optimization in the traditional heuristic method, especially suitable for real-time control. © 2023 IEEE.

关键词： Outages

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A Novel Compound Fault Recovery and control Techniques in Electric Distribution network 3

A Novel Compound Fault Recovery and Control Techniques in El...

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2022 3rd International conference on Electrical Technology and Automatic control, ICETAC 2022

作者： Mao, Wandeng Yuan, Shaoguang Tian, Yangyang Guo, Zhimin Liu, Hao Wang, Lei State Grid Henan Electric Power Research Institute Zhengzhou450052 China

When the distribution network is affected by natural disasters or deliberately damaged, failure may cause a large area power outage, which will seriously impact the national economy, social stability, people's normal life, etc. This paper proposes a composite fault recovery method for the distribution networks based on the maximum-flow method. A service flow model, an information link recovery model, and a multi-objective optimization model are established to effectively solve communication power's composite fault recovery problem in the distribution network. © Published under licence by IOP Publishing Ltd.

关键词： Multiobjective optimization

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ChatGPT Participates in Traffic control as a Traffic Manager Assistant 3

ChatGPT Participates in Traffic Control as a Traffic Manager...

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3rd IEEE International conference on Digital Twins and Parallel Intelligence, DTPI 2023

作者： Tang, Yiqing Dai, Xingyuan Lv, Yisheng University of Chinese Academy of Sciences School of Artificial Intelligence Beijing China Chinese Academy of Sciences Institution of Automation Beijing China

ISBN: (纸本)9798350318470

This paper uses ChatGPT to assist traffic manager in traffic signal control and verifies the possibility of applying ChatGPT in traffic control scenarios. We believe ChatGPT has four capabilities, including: knowledge acquisition, data analysis, decision support, and programming interface support. Proper use of these capabilities can assist traffic manager in their traffic strategy optimization efforts. We constructed a simulation environment and used ChatGPT to assist human traffic manager in performing signal optimization efforts. The process of evaluating the application potential of ChatGPT consists of four steps. First, we conducted a test on traffic related issues to test ChatGPT's knowledge acquisition ability. Second, the road network structure topology analysis test was conducted to help traffic manager quickly understand the road structure. Third, we performed traffic flow characterization and corresponding optimization suggestions to help traffic manager determine optimization policies. Finally, the policy deployment code modification is done by interacting with ChatGPT through natural language, which can improve the efficiency of traffic policy deployment compared with manual programming. The simulation results show that ChatGPT can help traffic managers to complete traffic policy optimization faster. Also, the traffic control optimization under this process improves the average passing speed of vehicles compared with that before the policy change. © 2023 IEEE.

关键词： Roads and streets

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

3rd International Conference on Computational Electronics fo...

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3rd International conference on Computational Electronics for Wireless Communications, ICCWC 2023

ISBN: (纸本)9789819719426

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Optimal Scheduling Method of Virtual Power Plant Based on Model Predictive control 3

Optimal Scheduling Method of Virtual Power Plant Based on Mo...

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3rd International conference on Energy, Power and Electrical Engineering, EPEE 2023

作者： Ye, Hong Huang, Honghui He, Yan Xu, Min Yang, Yantian Qiao, Yilin Sci-tech Information Branch Jinhua Bada Group Co. Ltd Jinhua China Development Planning Department State Grid Zhejiang Electric Power Co. LTD. Jinhua Power Supply Company Jinhua China None State Grid Shanghai Electric Power Company Economic and Technological Research Institute Shanghai China School of Electrical Engineering Zhejiang University Hangzhou China

ISBN: (纸本)9798350318180

In the environment of high proportion of new energy power system, one of the key points of virtual power plant dispatching is to consider the optimal control of distributed new energy prediction error. In this paper, a multi-time scale rolling optimization scheduling method considering the prediction error of distributed resources is proposed. Firstly, the long and short term neural network prediction method is used to predict the distributed photovoltaic output and the load rate of the communication base station, and then the bidding strategy is formulated on this basis. After the bidding, the decision sequence of each optimization variable within the day is constantly updated by the model based rolling prediction, and the particle swarm optimization algorithm is used to optimize the solution. Example analysis shows that the proposed method can reduce the dispatching cost by 21.47% compared with the theoretical optimal dispatching method, and is suitable for the optimization control of virtual power plant with a high proportion of uncontrollable power or load access. © 2023 IEEE.

关键词： Double time scale High uncertainty Model predictive control Virtual power plant

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Nonlinear programming based liner shipping route optimization under ECA restriction 3

Nonlinear programming based liner shipping route optimizatio...

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3rd International conference on Artificial Intelligence and Computer Engineering, ICAICE 2022

作者： Wang, Qiulan Co. Ltd. Shanghai200082 China

ISBN: (纸本)9781510663473

This study proposes an innovate nonlinear programming model to optimize the shipping cost under the restriction of Emission control Area (ECA). The arctic shipping route entitles the most potentially route from Asia to euro to significantly reduce the shipping cost. Therefore, aiming to optimal the existing shipping cost, the optimized model is provided and finds that the feeder ships have better performance on economic cost whether sailing in ECA accordingly with the carbon tax less than 190 USD/tons. The proposed optimization method can well reduce the shipping cost and get better emission performance when choosing the arctic shipping route. And the results also can improve the shipping company's revenues and maintain environmental sustainability. © 2023 SPIE.

关键词： Emission control

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