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Distributed Collaborative Optimization of a Multi-Region Integrated Energy system Based on Edge Computing Unit

FRONTIERS IN ENERGY RESEARCH 2022年 10卷

作者： Wang, Mengxue Zhao, Haoran Tian, Hang Wu, Qiuwei Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China Department of Electrical Engineering Technical University of Denmark Lyngby Denmark

The coordinated optimization scheduling of the integrated energy systems is vital in multi-energy complementarity and hierarchical utilization. However, the centralized optimization is inferior to the distributed optimization of the large-scale multiregion integrated energy system (MRIES) in data processing capacity and information security. This study proposes a distributed computing architecture based on the edge computing unit (ECU), which takes the energy hub as the main body and sets the partitioning principle and method of MRIES. The ECU can finally realize the whole-system collaborative optimization of MRIES, which contains electrical, natural gas, and district heating networks through internal autonomous optimization and boundary information interaction with the cloud computing center. At the same time, an improved nested algorithm based on the consensus-alternating direction method of multipliers is proposed, which ensures the convergence of the mixed-integer linear program and effectively improves the convergence speed. Combining the advantages of the model and algorithm provides a theoretical and algorithmic support for the optimization research of the MRIES.

关键词： edge computing unit multiregional integrated energy system distributed collaborative optimization energy hub energy conservation

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A multiple-time-scale control strategy for burden distribution and blast supply of a BF

A multiple-time-scale control strategy for burden distributi...

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Chinese control and decision Conference, CCDC

作者： Yunpeng Guo Jianqi An Guoyu Zhao Min Wu Qingyi Wang School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China

The operations of blast furnaces (BFs) are very vital for the long-term stability of the iron making process. The burden distribution and blast supply are the two major operation systems of BFs. At present, the researches are lack effective adjustment methods for the BF operations combined with burden distribution and blast supply. The burden distribution affects the iron making process on a long time scale, while the blast supply affects the iron making process on a short time scale. This paper presents a multi-time sampling-data adjustment strategy for the BF operations aiming at optimizing GUR on multiple time scales. First, this paper analyzes the relationship between the gas utilization ratio (GUR) and the burden distribution, the blast supply on multiple time scales. Then, this paper establishes a prediction model of GUR on the long time scale and the short time scale based on autoregressive integrated moving average (ARIMA). Next, this paper provides a control strategy of burden distribution and a control strategy of blast supply by a prediction model based on support vector regression (SVR). Finally, this paper makes experiments and applies this method in a real-world BF. The analysis of the results shows the control strategy of the BF operations provides a good guide on making a suitable decision for burden distribution and blast supply.

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Gesture Recognition of sEMG Based on Res-LSTM 17th

Gesture Recognition of sEMG Based on Res-LSTM

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17th International Conference on intelligent Robotics and Applications, ICIRA 2024

作者： Zhao, Yujia Zou, Chunlong Yun, Juntong Jiang, Du Huang, Li Liu, Ying Jiang, Guozhang Xie, Yuanmin Key Laboratory of Metallurgical Equipment and Control Technology of Ministry of Education Wuhan University of Science and Technology Wuhan430081 China College of Mechanical Engineering Hubei University of Automotive Technology Shiyan442000 China Research Center for Biomimetic Robot and Intelligent Measurement and Control Wuhan University of Science and Technology Wuhan430081 China Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering Wuhan University of Science and Technology Wuhan430081 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-Time Industrial System Wuhan University of Science and Technology Wuhan430081 China College of Computer Science and Technology Wuhan University of Science and Technology Wuhan430081 China School of Mechanical Engineering Hubei Engineering University Xiaogan432000 China

ISBN: (纸本)9789819607761

sEMG (surface electromyography) signal control of bionic prostheses has been widely studied over the past few years. In particular, sparse sEMG signals are rapidly developing in the field of gesture recognition for their convenience, noninvasiveness, and ease of access. However, compared with high-density EMG signals, sparse EMG signals lack rich feature information, which in turn affects gesture recognition accuracy. In order to reduce the loss of feature information of sparse EMG signals in the spatio-temporal dimension, this paper proposes a hybrid neural network Res-LSTM combining residual network and long short-term memory network. The ordinary convolutional blocks in the CNN network are replaced with residual blocks, and the final fully connected layer is removed and a constant mapping layer is added to adequate extraction of spatial feature information of the data. The Res layer’s output is utilized as the input for the long and short-term memory (LSTM) network, which further extracts the features of the data in the temporal dimension, and finally completes the categorization output through a fully connected layer. The average accuracy of 91.11% for gesture-motion classification was verified by training on a homebrew dataset;and tested on a public dataset, NinaPro DB1, with an average accuracy of 91.03%. The experimental findings indicate that the proposed Res-LSTM network framework contributes to solving the problem of lack of feature information of sparse sEMG signals and improves the pattern recognition accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Gesture recognition

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Impact assessment for large-scale off-grid of renewable energy on weakly interconnected systems

Impact assessment for large-scale off-grid of renewable ener...

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China International Electrical and Energy Conference (CIEEC)

作者： Li Li Changlong Qin Xin Dong Rongfei Qiao Jialun Yang State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

Under the background of increasingly serious off-grid problems of renewable energy, this paper analyzes the impact of large-scale off-grid of renewable energy and voltage stability in the weakly interconnected system. First, an improved index is proposed based on Simpson formula for assessing transient voltage stability and achieving early warning of instability risk. Then, based on the simulation of a real-life UHV (Ultra-high voltage) weakly interconnected system, two typical cascading failure scenarios are reasonably anticipated. These cascading failures contain a series of renewable energy off-grid and blocking of DC transmission caused by different initial failures, whose voltage stability is evaluated by the proposed index. Besides, the nature of cascading failure evolution is analyzed. Finally, accuracy and efficient of the proposed index are verified.

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ColorDynamic: Generalizable, Scalable, Real-time, End-to-end Local Planner for Unstructured and Dynamic Environments

arXiv

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arXiv 2025年

作者： Xin, Jinghao Liang, Zhichao Zhang, Zihuan Wang, Peng Li, Ning Department of Automation Shanghai Jiao Tong University Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

Deep Reinforcement Learning (DRL) has demonstrated potential in addressing robotic local planning problems, yet its efficacy remains constrained in highly unstructured and dynamic environments. To address these challenges, this study proposes the ColorDynamic framework. First, an end-to-end DRL formulation is established, which maps raw sensor data directly to control commands, thereby ensuring compatibility with unstructured environments. Under this formulation, a novel network, Transqer, is introduced. The Transqer enables online DRL learning from temporal transitions, substantially enhancing decision-making in dynamic scenarios. To facilitate scalable training of Transqer with diverse data, an efficient simulation platform E-Sparrow, along with a data augmentation technique leveraging symmetric invariance, are developed. Comparative evaluations against state-of-the-art methods, alongside assessments of generalizability, scalability, and real-time performance, were conducted to validate the effectiveness of ColorDynamic. Results indicate that our approach achieves a success rate exceeding 90% while exhibiting real-time capacity (1.2–1.3 ms per planning). Additionally, ablation studies were performed to corroborate the contributions of individual components. Building on this, the OkayPlan-ColorDynamic (OPCD) navigation system is presented, with simulated and real-world experiments demonstrating its superiority and applicability in complex scenarios. The codebase and experimental demonstrations have been open-sourced on our website1 to facilitate reproducibility and further research. Copyright © 2025, The Authors. All rights reserved.

关键词： decision making

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Synchronization Stability Analysis of Wind Farms Integrated MMC-HVDC system

Synchronization Stability Analysis of Wind Farms Integrated ...

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International Conference on Power system Technology (POWERCON)

作者： Li Li Changlong Qin Changtao Kan Jialun Yang Guanqun Li State Grid Jinan Power Supply Company Jinan China Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China

High-voltage direct current (HVDC) based on modular multilevel converter (MMC) is an efficient way to integrate wind farms. However, following short-circuit fault on transmission line, wind turbines (WTs) may lose synchronization with sending-end MMC. As a result, delivery and consumption of wind power are under risk. This paper investigates the mechanism of synchronous instability of wind farm integrated MMC-HVDC system under the severe grid faults. First, a synchronization stability model of the whole is stablished, the existing of stable equilibrium points (SEPs) and the damping characteristic of the whole system are analyzed. Then, a synchronization stability enhancement strategy is proposed based on the coordination of WT and MMC. Last, the effectiveness of proposed strategy is verified by time-domain simulation.

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Quantitative analysis for coupling between frequency dynamic and power angle oscillation 4

Quantitative analysis for coupling between frequency dynamic...

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4th IEEE International Conference on Power, intelligent Computing and systems, ICPICS 2022

作者： Liu, Fusuo Wang, Chao Ma, Ruicong Li, Wei Cao, Yongji Chang, Haijun Zhang, Hengxu Gao, Zhimin State Key Laboratory of Smart Grid Protection and Control NARI Group Corporation State Grid Electric Power Research Institute Nanjing China NARI Group Corporation State Grid Electric Power Research Institute Nanjing China Key Laboratory of Power System Intelligent Dispatch and Control of the Ministry of Education Shandong University Jinan China

ISBN: (数字)9781665467735

ISBN: (纸本)9781665467735

With the increasing penetration of power electronic devices, the behaviors of the power system become more and more complex. This paper analyzes the coupling characteristics between the frequency dynamics and power angle oscillation. The mechanism of frequency response and power angle oscillation is derived. The evaluation indexes are proposed to quantify the frequency response, power angle oscillation, and coupling characteristics. The coupling degree of the frequency dynamics and power angle oscillation is depicted by the Pearson correlation coefficient. The impacts of power angle oscillation on the frequency response are attained through time-domain simulation and theoretical analysis. The results show that the proposed evaluation indexes are effective to quantify the coupling characteristics. Additionally, there is positive correlation between the frequency dynamics and power angle oscillation. © 2022 IEEE.

关键词： Frequency response

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Preventive control Method of Improving Transient Voltage Stability for Power system Based on the Attention of CNN

Preventive Control Method of Improving Transient Voltage Sta...

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Sustainable Power and Energy Conference (iSPEC)

作者： Zhendong Li Jiongcheng Yan Yutian Liu Li Li Hanbing Qu Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education Shandong University Jinan China State Grid Jinan Power Supply Company Jinan China

The widespread integration of renewable generation and the changes in load structure bring significant challenges to the transient voltage stability of large-scale power systems. In order to achieve fast decision of preventive control for improving transient voltage stability, this paper proposes a preventive control method based on the attention of Convolutional Neural Network (CNN). An optimization decision model is established, which not only considers multiple traditional constraints but also introduces the model attention indicator based on Gradient-weighted Class Activation Mapping (Grad-CAM), enabling a more targeted improvement of transient voltage stability. The proposed indicator is capable of extracting the knowledge learned by CNN-based transient voltage assessment model, and utilizing it to guide the optimization decision-making of preventive control. Simulation results of provincial grid of eastern China demonstrate that the optimized control scheme can effectively enhance the transient voltage stability of power systems.

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Automated Assembly by Two-Fingered Microhand for Fabrication of Soft Magnetic Microrobots

Automated Assembly by Two-Fingered Microhand for Fabrication...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Yue Zhao Xiaoming Liu Ruixi Wang Dan Liu Masaru Kojima Qiang Huang Tatsuo Arai Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Advanced Innovation Center for Intelligent Robots and Systems Beijing China School of Mechatronical Engineering Beijing Institute of Technology Beijing China Department of Intelligent Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Materials Engineering Science Osaka University Osaka Japan Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micro-assembly is an emerging method to fabricate microrobots with multiple modules or particles. However, there is always a lack of a flexible and efficient method to freely create the desired magnetic soft microrobots. In this paper, an automated assembly system based on a two-fingered microhand is presented for fabricating magnetic soft microrobots. Our proposed system can automatically pick and place components to assemble microrobots with a two-fingered micromanipulator, and orient these components through an external magnetic field. The automated assembly has the advantages of high accuracy, high speed, and high success rate. It can endow magnetic microrobots with flexible material selection, arbitrary geometry design, and programable magnetization profile. We can make full use of this system to fabricate multiple magnetic soft microrobots. The experiment results demonstrate that this system can efficiently fabricate microrobots with excellent mechanical properties, which have application potential in robotics, biomedical engineering, and environmental governance.

关键词： Three-dimensional displays Shape Micromanipulators Magnetization Manuals Three-dimensional printing Soft magnetic materials

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Distributed Neighbor Selection on Matrix-weighted Networks

Distributed Neighbor Selection on Matrix-weighted Networks

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Youth Academic Annual Conference of Chinese Association of Automation (YAC)

作者： Haoyu Wei Lulu Pan Haibin Shao Dewei Li Department of Automation and Key Laboratory of System Control and Information Processing Ministry of Education of China and Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University Shanghai China

This paper explores distributed neighbor selection in matrix-weighted networks, where a matrix-valued weight captures the interdependencies among agents’ states. We demonstrate the feasibility of preserving and, in certain cases, improving the convergence rate of matrix-weighted networks by having agents exclusively engage with a subset of their neighbors. Leveraging the inherent monotonicity of Laplacian matrix eigenvectors, we introduce a novel neighbor selection criterion that reduces network connections while enhancing convergence rates. Existing neighbor selection strategies often require acquiring information from all nodes before making decisions. In order to propose a distributed neighbor selection strategy for matrix-weighted networks, we establish a numerical relationship between Laplacian eigenvectors and a locally measurable metric. In essence, this paper extends prior findings from scalar-weighted networks to matrix-weighted networks.

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