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2021 China automation Congress, CAC 2021

作者： Yang, Nan Li, Xinhang Huang, Yupeng Xiao, Menghao Wang, Zhe Song, Xuri Li, Lei Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781665426473

Due to the expansion of new energy sources, the complexity and difficulty of power grid dispatching are further increased, especially simultaneously considering security, economic and environmental factors. Existing power grid dispatching methods, such as the bisection method and proportional control, are not competent for the multi-objective complex dispatching. This paper proposes a hierarchical multi-object deep deterministic policy gradient (HMO-DDPG) algorithm to dispatch the smart grid with new energy sources. In this algorithm, the lower Decision Layer uses multi-agent architecture to make decisions for every generator unit, while the upper Optimization Layer evaluates the security, economic and environmental factors of the decisions in the whole power grid. Besides, a double evaluation mechanism deployed in the two layers is proposed to make the decision more reasonable and comprehensive. Moreover, a topology analysis method is used to avoid the islanding problem during smart grid dispatching. Experiments carried out on the power grid simulator provided by China Electric Power research Institute show HMO-DDPG can enable the power grid to operate safely for over 80% days of a year without exceeding the thermal stability limit of all branches. Moreover, the economic cost is 1.80% less, and the utilization rate of new energy is 70.48% higher than that of the traditional dispatching methods on average. © 2021 IEEE

关键词： Smart power grids

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Application of Container Image Repository technology in Automatic Operation and Maintenance of the dispatching and Control Cloud 2nd

Application of Container Image Repository Technology in Auto...

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2nd Big Data Analytics for Cyber-Physical system in Smart City, BDCPS 2020

作者： Tao, Lei Huang, Yunhao Liu, Dong Ma, Xinxin Wu, Shuzhou Cui, Can Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology State Key Laboratory of Power Grid Security and Energy Saving China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9789813345737

With the continuous development of the container technology, the scale of the dispatching and control cloud built by it is expanding continuously. At the same time, the application portability of cloud platform based on virtualization is poor, the man-made operation and maintenance mode of the dispatching and control cloud is inefficient and the error rate is high. It is difficult to guarantee the safe and high-quality operation of power grid and the lean and efficient operation of the dispatching management. Using container technology to containerize cloud platform applications and relying on the Continuous Integration and Continuous Deployment (CI/CD) platform which can achieve efficient and accurate maintenance of the dispatching and control cloud. In this paper, the CI/CD platform which consists of the Jenkins,Kubernetes and Docker is introduced to complete the operation and maintenance tasks of the dispatching and control cloud,such as automatically agile construction, automatic publishing. Finally, through the analysis of the dispatching and control cloud, it is verified that the results show that the platform has a certain effect in automation construction, publishing efficiency, gray publishing update, operation and maintenance quality. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Integration

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A Fault Screening Method for N-2 Contingency With Line Outage Distribution Factor

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Dianwang Jishu/Power system technology 2023年 47卷 45-50页

作者： Yu, Yijun Wang, Miao Sun, Bo Geng, Lin Song, Zhibin Zhang, Xiangyu Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Haidian District Beijing100192 China North China Branch of State Grid Corporation of China Xicheng District Beijing100053 China State Grid Shanxi Economic Research Institute Shanxi Province Taiyuan030002 China

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What induced the trend shift of mixed-layer depths in the Antarctic Circumpolar Current region in the mid-1980s?

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Acta Oceanologica Sinica 2024年第1期43卷 11-21页

作者： Shan Liu Jingzhi Su Huijun Wang Cuijuan Sui Key Laboratory of Marine Hazards Forecasting National Marine Environmental Forecasting CenterMinistry of Natural ResourcesBeijing 100081China Center for Earth System Modeling and Prediction of CMA Beijing 100081China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters Nanjing University of Information Science&TechnologyNanjing 210044China Nansen-Zhu International Research Center Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China

An obvious trend shift in the annual mean and winter mixed layer depth(MLD)in the Antarctic Circumpolar Current(ACC)region was detected during the 1960–2021 *** trends stopped in mid-1980s,followed by a period of weak *** MLD deepening trend difference between the two periods were mainly distributed in the western areas in the Drake Passage,the areas north to Victoria Land and Wilkes Land,and the central parts of the South Indian *** newly formed ocean current shear due to the meridional shift of the ACC flow axis between the two periods is the dominant driver for the MLD trends shift distributed in the western areas in the Drake Passage and the central parts of the South Indian *** saltier trends in the regions north to Victoria Land and Wilkes Land could be responsible for the strengthening mixing processes in this region.

关键词： mixed layer depth trend shift Antarctic Circumpolar Current(ACC) flow axis

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Maintenance Scheduling Optimization for AC-DC Grid Based on Component Status under N-1 Contingencies

Maintenance Scheduling Optimization for AC-DC Grid Based on ...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Zhi Cai Jiali Zhang Bin Han Yueshuang Bao Xinyuan Liu Huiping Zheng Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China State Grid Shanxi Electric Power Supply Company Taiyuan China

The joint optimization technology of maintenance scheduling with power outage with long-term unit commitment is a powerful support to the safe operation of AC-DC transmission grid. With respect to the splitting of single-ended component that may happen under the simultaneous action of branch maintenance and N-1 contingencies, this paper adds the in-service/out-of-service status variables of branch, generator, load and DC converter after maintenance and N-1 contingencies on the basis of the 0-1 decision variable of maintenance scheduling and unit commitment, and determines the association relationships between the variables above-mentioned, based on which the optimization model of the AC-DC grid maintenance scheduling is established, and linearizes the 0-1 status variables and non-linear constraints therein, to generate the mixed-integer programming model that is easy to be solved, to realize overall optimization of maintenance and power generation. Simulation is conducted by taking the improved IEEE 39-bus system as an example. The result shows that the proposed model and the algorithm can deal effectively with the load-generation imbalance caused by the component status change, enhance the reasonability and solvability of the maintenance scheduling based on N-1 contingencies, thus to upgrade the security of maintenance scheduling.

关键词： Analytical models systems operation Contingency management Maintenance engineering Generators Scheduling Inverters

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research on Dynamic Resource Scheduling technology of dispatching and Control Cloud Platform Based on Container 2nd

Research on Dynamic Resource Scheduling Technology of Dispat...

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2nd Big Data Analytics for Cyber-Physical system in Smart City, BDCPS 2020

作者： Liu, Dong Huang, Yunhao Wang, Jiaqi Xia, Wenyue Li, Dapeng Feng, Qiong Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology State Key Laboratory of Power Grid Security and Energy Saving China Electric Power Research Institute Haidian District Beijing100192 China

ISBN: (纸本)9789813345737

With the continuous advancement of dispatching and control cloud, traditional platform systems and virtualization technologies have been well supported in resource scheduling, flexible scaling, rapid migration deployment, the future availability and maintainability of these business systems will face enormous challenges. The rapid development of Docker container technology has solved the shortcomings of traditional PaaS platform based on virtual machine. In this paper, Docker container technology is used to build PaaS platform services of dispatching and control cloud. Through the analysis and verification of the flexible scaling, resource scheduling, container response and other performance aspects of the container, the experiment shows that the Docker-based PaaS platform has a good effect in the rational scheduling, rapid response and flexible scaling of resources. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Containers

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Graphene-based dual-function acoustic transducers for machine learning-assisted human-robot interfaces

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InfoMat 2023年第2期5卷 66-79页

作者： Hao Sun Xin Gao Liang-Yan Guo Lu-Qi Tao Zi Hao Guo Yangshi Shao Tianrui Cui Yi Yang Xiong Pu Tian-Ling Ren Institute of Microelectronics and Beijing National Research Center for Information Science and Technology(BNRist) Tsinghua UniversityBeijingPeople's Republic of China CAS Center for Excellence in Nanoscience Beijing Key Laboratory of Micro-nano Energy and SensorBeijing Institute of Nanoenergy and NanosystemsChinese Academy of SciencesBeijingPeople's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingPeople's Republic of China School of Computer Science&Technology Beijing Institute of TechnologyBeijingPeople's Republic of China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijingPeople's Republic of China

Human–robot interface(HRI)electronics are critical for realizing robotic ***,we report graphene-based dual-function acoustic transducers for machine learning-assisted human–robot interfaces(GHRI).The GHRI functions both an artificial ear through the triboelectric acoustic sensing mechanism and an artificial mouth through the thermoacoustic sound emission *** success of the integrated device is also attributed to the multifunctional laser-induced graphene,as either triboelectric materials,electrodes,or thermoacoustic *** systematically optimizing the structure parameters,the GHRI achieves high sensitivity(4500 mV Pa^(–1))and operating durability(1000000 cycles and 60 days),capable of recognizing speech identities,emotions,content,and other information in the human *** the assistance of machine learning,30 speech categories are trained by a convolutional neural network,and the accuracy reaches 99.66%and 96.63%in training datasets and test ***,GHRI is used for artificial intelligence communication based on recognized speech *** work shows broad prospects for the development of robotic intelligence.

关键词： human-robot interface laser-induced graphene machine learning thermoacoustic triboelectric nanogenerator

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research on Ship Target Recognition in Optical Remote Sensing Image Based on Local Feature Matching 2

Research on Ship Target Recognition in Optical Remote Sensin...

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2nd IEEE International Conference on Information technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Li, Lai Ma, Xinxin Liu, Xiaohui Unit 91039 of the PLA Beijing China China Beijing Key Lab of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China

ISBN: (纸本)9781665428767

In modern naval warfare, ship target is an important combat force on the battlefield of naval warfare. Different types of ship target have differences in combat mode and combat positioning. In order to accurately grasp the enemy combat ship movement, accurately analyze the enemy combat intention and firepower coverage in the naval battle field, the accurate identification of the ship target becomes a key problem. In this paper, a ship target type recognition method based on SIFT feature and Harris corner feature matching is proposed in view of the close size and similar appearance of different types of ship targets. © 2021 IEEE.

关键词： Optical remote sensing

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research on Centralized Optimization Clearing Method for Interregional Spot Market

Research on Centralized Optimization Clearing Method for Int...

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2022 Power system and Green Energy Conference, PSGEC 2022

作者： Han, Bin Hu, Chenxu Yuan, Xue Yu, Mingkai Wang, Jianxiao Zhang, Lu Chang, Naichao Mu, Yongzheng Beijing Key Laboratory of Research and System Evaluation of Power Dispatching Automation Technology China Electric Power Research Institute Beijing China School of Electrical and Electronic Engineering North China Electric Power University Beijing China College of Information and Electrical Engineering China Agricultural University Beijing China National Power Dispatch Control Center Beijing China

ISBN: (数字)9781665499934

ISBN: (纸本)9781665499934

The paper presents an in-depth analysis of the clearing process of the interregional renewable energy spot market currently in operation and the trading path construction method, and explains the issues that need to be considered for the future development of this market. On this basis, a centralized and efficient interregional spot market clearing model is proposed, which makes full use of the current trading path construction method, considering the same optimization objective of price difference matching method and security constraints of interregional and regional power grids. With high scalability, this model can meet the requirements of power grid safe operation and further improve the enforceability of market clearing results. It can provide theoretic support for the development of interregional spot market and the effective coordination between cross regional and regional spot market in the future. A case study of a 5-region system is adopted to verify the correctness and effectiveness of the proposed model. © 2022 IEEE.

关键词： Electric power transmission networks

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Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO

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Advances in Atmospheric Sciences 2024年第1期41卷 141-154页

作者： Tingyu WANG Ping HUANG Center for Monsoon System Research Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD) Nanjing University of Information Science&TechnologyNanjing 210044China State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China University of Chinese Academy of Sciences Beijing 100049China

The application of deep learning is fast developing in climate prediction,in which El Ni?o–Southern Oscillation(ENSO),as the most dominant disaster-causing climate event,is a key *** studies have shown that deep learning methods possess a certain level of superiority in predicting ENSO *** present study develops a deep learning model for predicting the spatial pattern of sea surface temperature anomalies(SSTAs)in the equatorial Pacific by training a convolutional neural network(CNN)model with historical simulations from CMIP6 *** with dynamical models,the CNN model has higher skill in predicting the SSTAs in the equatorial western-central Pacific,but not in the eastern *** CNN model can successfully capture the small-scale precursors in the initial SSTAs for the development of central Pacific ENSO to distinguish the spatial mode up to a lead time of seven months.A fusion model combining the predictions of the CNN model and the dynamical models achieves higher skill than each of them for both central and eastern Pacific ENSO.

关键词： ENSO diversity deep learning ENSO prediction dynamical forecast system

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