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Electromagnetic Transient Modeling and Real-time Simulation Based on state-space Equations

Electromagnetic Transient Modeling and Real-time Simulation ...

引用

New Energy and Power Engineering (ICNEPE), International Conference on

作者： Tianyu Guo Binjiang Hu Hui Luo Liang Tu Yanjun Zhao YuFeng Hu State Key Laboratory of HVDC Electric Power Research Institute China Southern Power Grid Guangdong Provincial Key Laboratory of Intelligent Operation and Control for New Energy Power System CSG Key Laboratory for Power System Simulation Electric Power Research Institute China Southern Power Grid China China Southern Power Grid Co. Ltd China

ISBN: (数字)9798331516086

ISBN: (纸本)9798331516093

Due to the different output characteristics of different components of the power system, the simulation results of its output deviate from the actual situation. Therefore, the electromagnetic transient modeling and real-time simulation based on the state space equation are proposed. From the perspective of rectifier, DC transmission line, inverter and control system, the composition of the state space model of the complete power system and the output characteristics of each part in the operation process are analyzed. In the test results, the design method can achieve a stable fit between the real-time simulation results of voltage waveform containing two VSC modules and the actual situation.

关键词： Simulation systems operation Power system dynamics Power system stability Aerospace electronics Mathematical models Real-time systems Smart grids Nonlinear dynamical systems Transient analysis

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Distributed Power Restoration Method Based on Joint Control of DGs

Distributed Power Restoration Method Based on Joint Control ...

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International Conference on Power system Technology (POWERCON)

作者： Ruimei Wang Wei Cong Yi Tang Anning Wang Yongjie Zhang Qianshuo Gu Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Shandong Electric Power Company Jinan China

As the scale of power system continues to expand, the proportion of distributed generators (DGs) in the smart distribution network has been increasing year by year. To address the problem of power restoration after DGs access, distributed power restoration method based on joint control of photovoltaic unit and pv/battery unit is proposed. Analyzing the control modes of photovoltaic unit and pv/battery unit during distributed power restoration, available capacity and adjustment range are determined based on control principles and operation constraints. Based on this method, the network is gradually converted into Thevenin equivalent circuit, and power restoration calculations are performed while considering the operating characteristics of photovoltaic unit and pv/battery unit. At the same time, power output adjustment is carried out based on voltage and frequency constraints to avoid overvoltage or frequency problems during the power restoration process. Using IEEE 33-node system as an example, then the feasibility of this method is verified.

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Research on Artificial Intelligence Industrial Big Data Platform for Industrial Internet Applications 4

Research on Artificial Intelligence Industrial Big Data Plat...

引用

4th IEEE International Conference on Electronics Technology, ICET 2021

作者： Li, Tan Song, Wei-Ning Zhou, Jun-Hua East China University of Technology School of Software Nanchang China Beijing Institute of Electronic System Engineering State Key Laboratory of Intelligent Manufacturing System Technology Beijing China

ISBN: (纸本)9781728176734

With the development of industry, enterprises have put forward higher requirements for real-time data collection and analysis. According to this, a distributed industrial big data platform for Industrial Internet applications is designed, which can effectively support the industrial field big data collection and storage. The system contains a distributed database, real-time database and offline big data platform to implement the collection and storage of all data in the system. Thus, the industrial big data platform architecture and related data hierarchical processing flow are proposed, which can effectively meet the requirements of the multi-source data and real-time processing of smart factory. The proposed data platform technical architecture will have important reference value for the realization of smart manufacturing and smart factory. © 2021 IEEE.

关键词： Big data

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Underwater Image Enhancement Based on Light Attenuation Prior and Dual-Image Multi-Scale Fusion

Underwater Image Enhancement Based on Light Attenuation Prio...

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作者： Liushanchuan He Yongjie Yan Chengtao Cai School of Intelligent System Science and Engineering Harbin Engineering University State Key Laboratory of Air Traffic Management System and Technology

The focus of this work is solving the degradation of underwater images. Different from images taken on land,underwater images suffer from color bias and fuzzy details due to absorption and scattering. In order to solve the above problems of underwater images, we propose a novel method called LAPF, which includes two stages of restoration and dual-image multi-scale fusion. Restoration based on underwater light attenuation prior can initially restore the details of the underwater image by building a scene depth estimation model. Coupled with the use of image fusion, obtain the contrast-enhanced picture and color-corrected picture as fusion inputs, and calculate the normalized weight maps to add detail. Carrying out multi-scale decomposition and fusion of these two parts, successfully further enhance the contrast while realizing the color correction. Experimental results on a variety of underwater images show the power of the method proposed in this paper, which achieves good results in both subjective visual perception and objective quality evaluation parameters.

关键词： Underwater image Color correction Contrast enhancement Underwater light attenuation prior Image fusion

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Acoustically Driven Micropipette for Hydrodynamic Manipulation of Mouse Oocytes

Acoustically Driven Micropipette for Hydrodynamic Manipulati...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Zhaofeng Zuo Xiaoming Liu Zhuo Chen Yuyang Li Xiaoqing Tang Dan Liu Qiang Huang Tatsuo Arai Beijing Advanced Innovation Center for Intelligent Robots and Systems and School of Mechatronical Engineering Key Laboratory of Biomimetic Robots and Systems Ministry of Education State Key Laboratory of Intelligent Control and Decision of Complex System Beijing Institute of Technology Beijing China Center for Neuroscience and Biomedical Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Micromanipulation techniques that can achieve controlled fine operations at the micro scale play an important role in biomedical fields including embryo engineering, gene engineering, drug screening, and cell analysis. However, micromanipulation of biological micro-objects, such as cells and micro tissues, suffers from mechanical damage and low efficiency. Several techniques have been introduced to manipulate cells more easily, but most of them are restricted by expensive devices, limited work area, and potential damage to cellular structure. Here we develop a hydrodynamic manipulation method to rotate and transport mouse oocytes, which utilizes acoustic waves and micropipette to generate acoustic radiation force and excite microstreaming. This method can accomplish rotational and translational operations precisely and controllably. We tested the process of trapping, rotation, and transportation of the mouse oocytes, and measured rotational and translational speed with a range of applied voltage. The method was able to shorten the cost time of delivery and posture adjustment before oocyte injection. Our study provides an easy-to-use technique for oocyte manipulation without contact, and it has the potential to be universally applied in many cellular studies.

关键词： Visualization Voltage measurement Three-dimensional displays Force Transportation Hydrodynamics Biology

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Attribute Analysis and Modeling Research on Color Harmony of Three-Color Combination

Attribute Analysis and Modeling Research on Color Harmony of...

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Culture-Oriented Science and Technology (CoST), International Conference on

作者： Jiapeng Pu Shuang Wang Hui Ren State Key Laboratory of Media Convergence and Communication Communication University of China Key Laboratory of Acoustic Visual Technology and Intelligent Control System Communication University of China Beijing Key Laboratory of Modern Entertainment Technology Communication University of China Beijing China

Color harmony is the result of objective color matching and human subjective feeling. This paper aims to study the subjective and objective factors of color harmony and carry out mathematical models between them. Firstly, we constructed a color data set commonly used three-color combinations. Secondly, we analyzed the subjective and objective factors affecting color harmony, extracted the 36-dimensional objective physical features, and divided subjective factors into direct psychological factors and indirect psychological factors. Then, based on color harmony and its subjective factors, a total of 33 subjects evaluated 150 three-color combinations on a five-level scale. Finally, the mathematical models of color harmony were constructed by machine learning algorithms. The research shows that color harmony is not only affected by objective factors, but also by subjective factors, and the influence of subjective factors is greater. In addition, the multiple linear regression algorithm plays best on color harmony predicting, which can better explain the color harmony mechanism.

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Graph-to-Text Generation Combining Directed and Undirected Structural Information in Knowledge Graphs*

Graph-to-Text Generation Combining Directed and Undirected S...

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Natural Language Processing (ICNLP), International Conference on

作者： Hongda Gong Shimin Shan Hongkui Wei Software School Dalian University of Technology Dalian University of Technology DaLian China State Key Laboratory of Intelligent Manufacturing System Technology Beijing Institute of Electronic System Engineering Beijing China

Graph-to-text generation task is transforms knowledge graphs into natural language. In current research, pretrained language models(PLMs) have shown better performance than structured graph encoders in the generation task. Currently, PLMs serialise knowledge graphs mostly by transforming them into undirected graph structures. The advantage of an undirected graph structure is that it provides a more comprehensive representation of the information in knowledge graph, but it is difficult to capture the dependencies between entities, so the information represented may not be accurate. Therefore, We use four types of positional embedding to obtain both the directed and undirected structure of the knowledge graph, so that we can more fully represent the information in knowledge graph, and the dependencies between entities. We then add a semantic aggregation module to the Transformer layer of PLMs, which is used to obtain a more comprehensive representation of the information in knowledge graph, as well as to capture the dependencies between entities. Thus, our approach combines the advantages of both directed and undirected structural information. In addition, our new approach is more capable of capturing generic knowledge and can show better results with small samples of data.

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Flapping-Wing Robot Achieves Multi-Domain Locomotion Using Transformable Wheel Mechanism

Flapping-Wing Robot Achieves Multi-Domain Locomotion Using T...

引用

IEEE International Conference on robotics and Biomimetics

作者： Xiangxiao Chai Shihua Wang Erzhen Pan Wenfu Xu School of Mechanical Engineering and Automation Harbin Institute of Technology (Shenzhen) Shenzhen China Key University Laboratory of Mechanism & Machine Theory and Intelligent Unmanned Systems of Guangdong Shenzhen China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

The difficulty of moving from aquatic to aerial environment is one of the major challenges limiting the development of trans-medium multi-domain locomotion vehicles. In this paper, the concept of the transformable wheel, a feature commonly found in autonomous land vehicles (AL V s), is introduced into the design. Additionally, a flapping-wing robot equipped with multi-domain locomotion capability, which can not only realize aerial flight and surface navigation on water, but also ascend from water to land for subsequent takeoff is presented. The paper first introduces the system design of the robot and proposes the corresponding multi-domain locomotion strategy. Subsequently, the design of the transformable wheel mechanism is described in detail and the force analysis for the water-exit is conducted. The feasibility of the proposed strategy is experimentally validated, confirming the prototype's multi-domain locomotion performance finally. This research explores the feasibility of introducing the transformable wheel mechanism into trans-medium multi-domain vehicle platforms and provides a design example.

关键词： Legged locomotion Torque Limiting Navigation Propellers Roads Wheels Prototypes Motors system analysis and design

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A Missing Data Imputation Method for Electricity Consumption Data Based on TCN-Attention with Mask Tokens

A Missing Data Imputation Method for Electricity Consumption...

引用

Consumer Electronics and Computer Engineering (ICCECE), 2021 IEEE International Conference on

作者： Yifeng Mao Ming Yang Peng Li Zhujian Ou Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China State Grid Nantong Power Supply Company State Grid Jiangsu Electric Power CO. LTD Nantong China

With the popularization of smart meters, power companies can collect massive amounts of data from users for non-invasive load detection, electricity theft detection, etc. However, due to faults in smart meters and abnormal communication, the dataset often has missing values, making it difficult for data-driven methods to be widely applied. In this paper, we propose an unsupervised learning method based on TCN-Attention to fill in customers' electricity consumption data. Firstly, we extract electricity consumption features through TCN. In order to improve the feature extraction capability, we introduce a channel attention mechanism to TCN. The attention mechanism makes TCN focus on the effective features in the data by assigning weights. Meanwhile, we use mask tokens to cover a certain proportion of data, forcing the proposed model to learn potential feature representations of the data during the training process. Finally, the features are restored to data through feedforward neural networks. An example is given to verify the effectiveness of the proposed method.

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Distributed Photovoltaic Power Forecasting Method Based on AP Clustering and Transfer Learning

Distributed Photovoltaic Power Forecasting Method Based on A...

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IEEE/IAS Industrial and Commercial Power system Asia (I&CPS Asia)

作者： Baolong Yang Ming Yang Qiangsheng Bu Chuanqi Wang Yuanhe Zhang Menglin Li Fei Luo Key Laboratory of Power System Intelligent Dispatch and Control Shandong University Jinan China Research Institute State Grid Jiangsu Electric Power CO. LTD. Nanjing China

ISBN: (数字)9798350352290

ISBN: (纸本)9798350352306

Highly accurate photovoltaic (PV) power forecast technology can provide a scientific basis for grid planning to ensure safe, economical, and efficient grid operation. The investment cost for a specialized power prediction system for each PV plant is high. Consequently, a distributed PV (DPV) power forecast method based on affinity propagation (AP) clustering and transfer learning is proposed in this paper. First, the dynamic time warping (DTW) algorithm determines the similarity of power curves between DPV plants; on this basis, multiple DPV power plants in the area to be forecasted are divided into station clusters by using AP clustering; and the PV power plants in each station cluster are divided into source and target domain by transfer learning, combined with long-short term memory (LSTM) network, the power forecasting model is established, and enabling transfer of power models from source to target domain. Finally, data from 20 DPV plants in China are used to demonstrate the effectiveness of the approach. Compared to separate modeling, the proposed method reduces the average training time by 58% while maintaining the forecasting accuracy.

关键词： Photovoltaic systems Training Accuracy Costs Heuristic algorithms Transfer learning Weather forecasting Predictive models Forecasting Long short term memory

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