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IEEE International Conference on Robotics and Automation (ICRA)

作者： Jiaxi Wu Haoran Wu Shanlin Zhong Quqin Sun Yinlin Li State Key Laboratory of Turbulence and Complex Systems Intelligent Biomimetic Design Lab College of Engineering Peking University Beijing China Department of Automation University of Science and Technology of China Heifei China State Key Laboratory of Management and Control for Complex System Institute of Automation Chinese Academy of Science Beijing China Science and Technology on Thermal Energy and Power Laboratory Wuhan China

Flat objects with negligible thicknesses like books and disks are challenging to be grasped by the robot because of the width limit of the robot's gripper, especially when they are in cluttered environments. Pre-grasp manipulation is conducive to rearranging objects on the table and moving the flat objects to the table edge, making them graspable. In this paper, we formulate this task as Parameterized Action Markov Decision Process, and a novel method based on deep reinforcement learning is proposed to address this problem by introducing sliding primitives as actions. A weight-sharing policy network is utilized to predict the sliding primitive's parameters for each object, and a Q-network is adopted to select the acted object among all the candidates on the table. Meanwhile, via integrating a curriculum learning scheme, our method can be scaled to cluttered environments with more objects. In both simulation and real-world experiments, our method surpasses the existing methods and achieves pre-grasp manipulation with higher task success rates and fewer action steps. Without fine-tuning, it can be generalized to novel shapes and household objects with more than 85% success rates in the real world. Videos and supplementary materials are available at https://***/view/pre-grasp-sliding.

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Frontiers in high entropy alloys and high entropy functional materials

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Rare Metals 2024年第10期43卷 4639-4776页

作者： Wen-Tao Zhang Xue-Qian Wang Feng-Qi Zhang Xiao-Ya Cui Bing-Bing Fan Jia-Ming Guo Zhi-Min Guo Rui Huang Wen Huang Xu-Bo Li Meng-Ru Li Yan Ma Zhi-Hua Shen Yong-Gang Sun De-Zhuang Wang Fei-Yang Wang Li-Qiang Wang Nan Wang Tian-Li Wang Wei Wang Xiao-Yang Wang Yi-Han Wang Fu-Jie Yu Yu-Zhen Yin Ling-Kun Zhang Yi Zhang Jian-Yang Zhang Qi Zhao Yu-Ping Zhao Xin-Dong Zhu Yasir Sohail Ya-Nan Chen Tao Feng Qi-Long Gao Hai-Yan He Yong-Jiang Huang Zeng-Bao Jiao Hua Ji Yao Jiang Qiang Li Xiao-Ming Li Wei-Bing Liao Huai-Jun Lin Hui Liu Qi Liu Qing-Feng Liu Wei-Di Liu Xiong-Jun Liu Yang Lu Yi-Ping Lu Wen Ma Xue-Fei Miao Jie Pan Qing Wang Hong-Hui Wu Yuan Wu Tao Yang Wei-Ming Yang Qian Yu Jin-Yu Zhang Zhi-Gang Chen Liang Mao Yang Ren Bao-Long Shen Xun-Li Wang Zhe Jia He Zhu Zhen-Duo Wu Si Lan School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Jiangsu Key Laboratory for Advanced Metallic Materials School of Materials Science and EngineeringSoutheast UniversityNanjing 211189China Department of Physics City University of Hong KongHongKong 999077China School of Materials Science and Engineering Tianjin UniversityTianjin 300072China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China Department of Mechanical Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 100872China Center of Electron Microscopy and State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Institute of Advanced Wear&Corrosion Resistance and Functional Materials Jinan UniversityGuangzhou 510632China School of Mechanics and Civil Engineering China University of Mining and TechnologyXuzhou 221116China School of Physical Sciences Great Bay UniversityDongguan 523000China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing 211816China Beijing Advanced Innovation Center for Materials Genome Engineering Research Institute for Carbon NeutralityUniversity of Science and Technology BeijingBeijing 100083China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Computing Macquarie UniversitySydney 2113Australia Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laborato

Owing to their exceptional properties,high-entropy alloys(HEAs)and high-entropy materials have emerged as promising research areas and shown diverse ***,the recent advances in the field are comprehensively reviewed,organized into five *** first section introduces the background of HEAs,covering their definition,significance,application prospects,basic properties,design principles,and *** subsequent section focuses on cutting-edge high-entropy structural materials,highlighting developments such as nanostructured alloys,grain boundary engineering,eutectic systems,cryogenic alloys,thin films,micro-nano-lattice structures,additive manufacturing,high entropy metallic glasses,nano-precipitate strengthened alloys,composition modulation,alloy fibers,and refractory *** the following section,the emphasis shifts to functional materials,exploring HEAs as catalysts,magneto-caloric materials,corrosion-resistant alloys,radiation-resistant alloys,hydrogen storage systems,and materials for ***,the review encompasses functional high-entropy materials outside the realm of alloys,including thermoelectric,quantum dots,nanooxide catalysts,energy storage materials,negative thermal expansion ceramics,and high-entropy wave absorption *** paper concludes with an outlook,discussing future directions and potential growth areas in the *** this comprehensive review,researchers,engineers,and scientists may gain valuable insights into the recent progress and opportunities for further exploration in the exciting domains of high-entropy alloys and functional materials.

关键词： High entropy alloys Cutting-edge structural materials Cutting-edgefunctional materials

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Chance-Constrained Generic Energy Storage Operations under Decision-Dependent Uncertainty

arXiv

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arXiv 2022年

作者： Qi, Ning Pinson, Pierre Almassalkhi, Mads R. Cheng, Lin Zhuang, Yingrui State Key Laboratory of Control and Simulation of Power Systems and Generation Equipment Department of Electrical Engineering Tsinghua University Beijing100084 China The Dyson School of Design Engineering Imperial College London LondonSW7 2AZ United Kingdom The Department of Electrical and Biomedical Engineering University of Vermont BurlingtonVT05405 United States

Compared with large-scale physical batteries, aggregated and coordinated generic energy storage (GES) resources provide low-cost, but uncertain, flexibility for power grid operations. While GES can be characterized by different types of uncertainty, the literature mostly focuses on decision-independent uncertainties (DIUs), such as exogenous stochastic disturbances caused by weather conditions. Instead, this manuscript focuses on newly-introduced decision-dependent uncertainties (DDUs) and considers an optimal GES dispatch that accounts for uncertain available state-of-charge (SoC) bounds that are affected by incentive signals and discomfort levels. To incorporate DDUs, we present a novel chance-constrained optimization (CCO) approach for the day-ahead economic dispatch of GES units. Two tractable methods are presented to solve the proposed CCO problem with DDUs: (i) a robust reformulation for general but incomplete distributions of DDUs, and (ii) an iterative algorithm for specific and known distributions of DDUs. Furthermore, reliability indices are introduced to verify the applicability of the proposed approach with respect to the reliability of the response of GES units. simulation-based analysis shows that the proposed methods yield conservative, but credible, GES dispatch strategies and reduced penalty cost by incorporating DDUs in the constraints and leveraging data-driven parameter identification. This results in improved availability and performance of coordinated GES units. © 2022, CC BY.

关键词： Energy storage

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A Novel YJQR-LSTM Model for Nonparametric Probabilistic Sustainable Agriculture Wind power Forecasting Based on Intelligent IoT

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IEEE Internet of Things Journal 2024年

作者： Wang, Jie Jiang, Junhui Chen, Xinlong Cai, Defu Wu, Yue Lu, Renzhi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Harbin Engineering University College of Computer Science and Technology Harbin150001 China State Grid Hubei Electric Power Research Institute Hubei Wuhan430079 China School of Technology State Key Laboratory of Efficient Production of Forest Resources Key Laboratory of National Forestry and Grassland Administration on Forestry Equipment and Automation Beijing Forestry University Beijing100083 China Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of Education Chongqing400065 China State Key Laboratory of Mechanical Transmission for Advanced Equipment Chongqing University Chongqing400044 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

Energy costs associated with the consumption of non-renewable energy sources have become an important issue in improving the international competitiveness of agriculture. Wind power, as a renewable energy source, can replace non-renewable energy sources to reduce energy costs and improve the sustainability of agricultural. However, the inherent intermittency, randomness, and volatility within weather conditions and wind speed presents a substantial challenge in accurately predicting wind power generation. This work proposes a novel YJQR-LSTM algorithm that leverages Yeo-Johnson quantile regression (YJQR) with a long short-term memory (LSTM) network for nonparametric probabilistic forecasting of wind power generation via the Intelligent Internet of Things. First, an improved YJQR model based on the YJ transformation is designed to obtain a more precise characterization of wind uncertainty, providing a more flexible probability density function for wind power generation. Then, utilizing the unique structure of the LSTM network to learn the parameters of the YJQR model, temporal features can be extracted from time series data. To mitigate the impact of outliers in the raw data on accuracy and improve computational efficiency, a novel logarithmic likelihood function is developed as the loss function utilized in the training phase. The effectiveness of the proposed algorithm is validated using a real-world dataset from five wind farms from the Global Energy Forecasting Competition. Numerical results demonstrate that the algorithm provides more accurate wind power prediction results in complex wind power data environments, which is important for making full use of wind energy and thus reducing the consumption of non-renewable energy in agriculture. © 2024 IEEE.

关键词： Wind forecasting

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Perspectives on the impacts of climate change and their adaptation

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National Science Open 2024年第1期3卷 4-5页

作者： Guohe Huang Jiapei Chen State Key Joint Laboratory of Environmental Simulation and Pollution Control China-Canada Center for EnergyEnvironment and Ecology ResearchUR-BNUSchool of EnvironmentBeijing Normal UniversityBeijing 100875China Environmental Systems Engineering Program University of ReginaReginaSaskatchewan S4S 0A2Canada

The pressing impacts of climate change and the imperative for adaptation strategies emphasize the immediate necessity for proactive global *** challenges encompass the enhancement of climate modeling,the development of sector-tailored strategies,and the implementation of adaptation *** these issues necessitates worldwide collaborations,sustainable practices,technological innovations.

关键词： climate adaptation collaboration

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Wide-Area Damping control Enhanced by Non-Invasive Data Verification Against False Data Injection Attacks

SSRN

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SSRN 2024年

作者： Sun, Zhenglong Long, Hao Li, Zewei Gao, Bo Wei, Xiaoqiang Belikov, Juri Levron, Yoash Department of Electrical Engineering Key Laboratory of Modern Power System Simulation and Control & Renewable Energy State Grid Jiashan County Power Supply Company Jiaxing China State Grid Heilongjiang Electric Power Company Limited Heilongjiang China The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion Israel Institute of Technology Haifa Israel Department of Software Science Tallinn University of Technology Tallinn Estonia Northeast Electric Power University China

The increasing interconnectivity of power systems and the integration of renewable energy sources into the grid have exacerbated the low-frequency oscillation problem, which directly impacts the stability of the power grid. The performance of wide-area damping control (WADC), which is an effective oscillation suppression technique , is susceptible to false data injection attacks (FDIA). This study proposes a non-intrusive data verification method using an adaptive variational autoencoder (AVAE) combined with a deep convolutional neural network (DCNN) to enhance WADC’s resistance to FDIA. The method integrates data dimensionality reduction of AVAE and reconstruction capabilities with DCNN’s feature extraction advantages. Itemploys adaptive thresholds to dynamically track grid data fluctuations, accurately distinguishing between normal and abnormal measurements and improving attack detection and performance discrimination. In addition, the study proposes an intelligent repair process based on latent representations for anomalous data in the repair module, using hierarchical clustering and Gaussian mixture models post-Bayesian inference to repair data. AVAE then reconstructs the repaired data, mitigating attack impacts. simulations on IEEE 39-Bus, 68-Bus, and 118-bus systems demonstrate that this method effectively detects network attacks and restores manipulated signals with minimal reconstruction errors, thus ensuring grid stability and reliability. © 2024, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Day-ahead Intra-day Economic Dispatch Methodology Accounting for the Participation of Electrolytic Aluminum Loads and Energy Storage in power System Peaking

Day-ahead Intra-day Economic Dispatch Methodology Accounting...

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power Science and Technology (ICPST), IEEE International Conference on

作者： Wei Zheng Peng Yu Zhaoyang Xu Tianzhen Fan Hengyu Liu Meng Yu Jiong Zhang Junhui Li State Grid Liaoning Electric Power Supply Co. Ltd State Grid Fushun Electric Power Supply Company Liao Ning China State Grid Liaoning Electric Power Co. Ltd State Grid Liaoning Electric Power Company Limited Power Dispatch And Control Center Liao Ning China State Grid Electric Power Research Institute Co. Ltd State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd Hu Bei China Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology (Northeast Electric Power University) Ji Lin China State Grid Liaoning Electric Power Co. Ltd State Grid Liaoning Electric Power Company Limited Electric Power Research Institute Liao Ning China

ISBN: (数字)9798350349030

ISBN: (纸本)9798350349047

Under the background of dual-carbon target and large-scale new energy access, most of the existing power system scheduling modes focus on the power source side, ignoring the potential of load-side interaction with the grid for peak shifting. In order to promote two-way coordination and interaction on both sides of the source and load sides, and to guarantee the balance of power supply and demand on different time scales and the high level of new energy consumption, it is proposed to incorporate the representative electrolysis aluminum loads on the load side into the main body of the scheduling, and to optimize the dispatching of power supply and demand through the configuration of It is proposed that the representative electrolytic aluminum load on the load side should be included in the main body of dispatch, and by configuring battery energy storage devices on the load side, joint optimal dispatch with thermal power units containing carbon capture devices should be carried out. With the optimization objective of minimizing the operating cost of the power system, a joint day-ahead-intraday economic dispatch method is developed. Finally, simulation calculations are carried out with an example to show that the proposed peaking model can effectively guide the demand response of the aluminum loads, increase the rate of new energy consumption, and reduce the operating cost of the system at the same time.

关键词： Energy consumption Costs power supplies Aluminum Optimal scheduling Wind power generation Regulation

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A Novel Engine and Battery Coupled Thermal Management Strategy for Connected Hevs Based on Switched Model Predictive control Under Low Temperature

SSRN

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SSRN 2022年

作者： Li, Kai Chen, Hong Hou, Shengyan Eriksson, Lars Gao, Jinwu State Key Laboratory of Automotive Simulation and Control Jilin University Changchun131022 China Department of Control Science and Engineering Jilin University Changchun131022 China School of Electronics and Information Engineering Tongji University Shanghai201804 China Department of Electrical Engineering Vehicular Systems Linkping University SE-581 83 Sweden

Under a low-temperature environment, electric vehicles face serious environmental adaptability problems, and efficient vehicle thermal management strategies are urgently needed. This paper presents a novel engine-battery coupled thermal management strategy for connected hybrid electric vehicles (HEVs). An improved system structure for an engine-battery coupled thermal management system (engine-battery CTMS) is designed to avoid unnecessary heat loss. The control requirements of the engine-battery CTMS include minimum engine fuel consumption, minimum power battery ageing damage and minimum system energy consumption, which constitutes a multi-objective optimal control problem in a finite time domain. Based on model predictive control (MPC) theory, a switched nonlinear MPC (NMPC) control strategy is proposed to solve the optimal control problem of the complex coupled multi-input multi-output system. To verify the effectiveness of the proposed strategy, a comparative experiment of the original system structure of the engine-battery CTMS combined with a centralized NMPC control strategy is designed. The results of the cosimulation experiment between MATLAB/Simulink and AMEsim under various driving cycles and different ambient temperatures show that the improved structure and switched control strategy confer great advantages in reducing the controller computation burden, engine fuel consumption, and power battery ageing damage. © 2022, The Authors. All rights reserved.

关键词： Model predictive control

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Dual-layer economic dispatch method for electric fused magnesia loads participating in peak shaving guided by price compensation

Dual-layer economic dispatch method for electric fused magne...

引用

4th Energy Conversion and Economics Annual Forum (ECE Forum 2024)

作者： Junhui Li Tianzhen Fan Jing Xu Wei Zheng Chupeng Xiao Chaoyang Xu Cuiping Li Key Laboratory of Modern Power System Simulation and Control & Renewable Energy Technology Ministry of Education Northeast Electric Power University Jilin People's Republic of China State Grid Electric Power Research Institute Co. State Grid Electric Power Research Institute Wuhan Efficiency Evaluation Co. Ltd. Hubei People's Republic of China State Grid Liaoning Electric Power Supply Co. State Grid Fushun Electric Power Supply Co. Ltd. Liaoning People's Republic of China

ISBN: (数字)9781837242924

To address the severe challenge of increasing fluctuations in supply and demand due to the expansion of new energy installations, this paper proposes a hierarchical optimization control strategy for electric fused magnesia loads participating in peak shav ing, guided by a price compensation mechanism. In the upper-layer system control, a price mechanism is designed to enhance the flexibility of electric fused magnesia loads in participating in peak shaving, considering the overall benefit of the system. In the lower-layer enterprise net profit optimization model, the product revenue, demand response income, operation and maintenance costs, and carbon emission costs of the electric fused magnesia enterprise are balanced to ensure the maximization of the enterprise’s net profit. Finally, numerical examples are used to verify the effectiveness of this method in improving the system's peak shaving capability.

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Adaptive Current Protection Technology for Distribution Network with Distributed power Sources Based on Local Information

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Mobile Information systems 2021年 2021卷

作者： Cui, Shijie Zeng, Peng Wang, Zhongfeng Zuo, Yue State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China State Grid Huludao Power Supply Company Huludao125000 China

Because the distributed power supply is connected to the distribution network, its structure has also changed the original traditional method. The original single power supply method was originally used. The network provided is a radial network and it is connected to the distributed power supply. Later, it became a structure with small- and medium-sized power supplies inside, and its network power supply became dual or multiple power supplies. Such a structural change affected the coordination of power distribution protection devices, which would impose current on the distribution network. Protection has a negative impact. This paper aims to study the adaptive current protection technology of distribution network with distributed power based on local information. On the basis of analyzing the characteristics of distributed power, the classification of distributed power, and the operation mode of distributed power, according to the access of IIDG, as well as different influences of relay protection of distribution network, an improved protection scheme with adaptive current quick-break protection as the main protection is proposed, so that different fault characteristics of the distribution network have different setting values, and then simulation experiments are carried out on this scheme. The experimental results show that the setting value of three-phase short-circuit fault proposed in this paper can well protect the protection range, which is larger than the traditional protection setting and self-adaptive setting. © 2021 Shijie Cui et al.

关键词： Electric power system protection

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