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International Conference on Magnetics (INTERMAG)

作者： Wenxia Sima Yonglai Liu Potao Sun Yuan Zhou Daixiao Peng Ming Yang State Key Laboratory of Power Transmission Equipment System Security and New Technology Chongqing University Chongqing China Electric Power Research Institute of Guangdong Power Grid Co.Ltd. Guangzhou Guang Dong China Department of Electrical and Computer Engineering New York University Brooklyn NY USA

The inrush current is one of the most common low-frequency transients, and is crucial for transformer studies and design. Core materials have an impact on the inrush current of transformers due to their different magnetizing characteristics. However, how their differences affect inrush currents and the influence level need to be further studied. In this paper, the effect of core materials, namely, the grain-oriented silicon steel, grain non-oriented silicon steel, amorphous alloy and nanocrystalline alloy, on inrush currents of transformers is investigated. Experiments are carried out to compare the inrush current peak values of these four transformers. An improved π model considering the detailed saturation process of the core is proposed for inrush current simulations on the ATP-EMTP platform. The simulated and experimental waveforms show good agreement from the peak value and wave-shape point of view. Results show that the amorphous alloy and nanocrystalline alloy transformers generate much higher inrush currents than those of the non-oriented silicon steel and oriented silicon steel transformers, and the reason is discussed in detail.

关键词： Circuit faults Surges Transformer cores Current transformers Saturation magnetization Surge protection

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Corrigendum: Ethanol-controlled peroxidation in liquid-anode discharges (2019 J. Phys. D: Appl. Phys. 52 425205)

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Journal of Physics D: Applied Physics 2021年第22期54卷

作者： Qiang Chen Jiao Lin Xinyi He Hailong Hu Junshuai Li Qing Xiong Bingyan Chen Zhengyong Song Hai Liu Qing Huo Liu Kostya (Ken) Ostrikov Institute of Electromagnetics and Acoustics Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Department of Electronic Science Xiamen University Xiamen 361005 People's Republic of China College of Physics and Information Engineering Fuzhou University Fuzhou 350116 People's Republic of China Key Laboratory of Special Function Materials and Structure Design of the Ministry of Education and School of Physical Science and Technology Lanzhou University Lanzhou 730000 People's Republic of China State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing University Chongqing 400044 People's Republic of China Department of Mathematics and Physics Hohai University Changzhou 213022 People's Republic of China School of Civil Engineering Guangzhou University Guangzhou 510006 People's Republic of China Department of Electrical and Computer Engineering Duke University Durham NC 27708 United States of America Institute for Future Environments and School of Chemistry Physics and Mechanical Engineering Queensland University of Technology Brisbane QLD 4000 Australia CSIRO-QUT Joint Sustainable Processes and Devices Laboratory Commonwealth Scientific and Industrial Research Organization PO Box 218 Lindfield NSW 2070 Australia

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Preventive Soot Blowing Strategy Based on State of Health Prediction for Coal-fired power Plant Boiler

Preventive Soot Blowing Strategy Based on State of Health Pr...

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Chinese Control Conference (CCC)

作者： Yuanhao Shi Jie Wen Xiaoqiong Pang Jianfang Jia Fangshu Cui Jianchao Zeng Jingcheng Wang North University of China School of Electrical and Control Engineering Taiyuan China North University of China School of Computer Science and Technology Taiyuan China Department of Automation and Key Laboratory of System Control and Information Processing Shanghai Jiao Tong University Shanghai China

This paper seeks the optimization of soot-blowing operations for heat transfer surfaces in coal- fired power plant boiler. A preventive soot blowing strategy based on state of health prediction for heat transfer surface was proposed. The average state of health of heat transfer surface was forecasted as the threshold to draft preventive soot blowing strategy. According to the theory of renewal process, an optimization model with the system prediction interval and the threshold of preventive soot blowing operation as the optimization variables and minimize average energy losses as the target function was established. By using Particle Swarm Optimization (PSO), the optimal preventive cycle and soot-blowing threshold were obtained, and the long-run average cost rate was the lowest. The model is validated with experiment data of a 300MW coal-fired power plant boiler and the parameters of prediction model are obtained. The results can verify the feasibility of proposed strategy. It can be used as the guide for the optimization of soot blowing in coal-fired power plant to improve the energy conservation level.

关键词： Thermal engineering Radio frequency Coal Combustion Heat engines Heat transfer

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Design of hybrid damping controller based on multi-target gravitational search optimization algorithm in a multi-machine power system with high penetration of PV park

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Journal of Central South University 2016年第5期23卷 1163-1175页

作者： Y.Hashemi H.Shayeghi M.Moradzadeh A.Safari Technical Engineering Department University of Mohaghegh Ardabili Centre of Excellence for Power System Automation and Operation Department of Electrical EngineeringIran University of Science and Technology Centre for Engineering Innovation Department of Electrical and Computer EngineeringUniversity of Windsor Electrical Energy Laboratory Department of Electrical EnergySystems and AutomationGhent University Department of Electrical Engineering Azarbaijan Shahid Madani University

A mathematical approach was proposed to investigate the impact of high penetration of large-scale photovoltaic park(LPP) on small-signal stability of a power network and design of hybrid controller for these units.A systematic procedure was performed to obtain the complete model of a multi-machine power network including *** damping of oscillations focusing on inter-area oscillatory modes,a hybrid controller for LPP was *** performance of the suggested controller was tested using a 16-machine 5-area *** results indicate that the proposed hybrid controller for LPP provides sufficient damping to the low-frequency modes of power system for a wide range of operating *** method presented in this work effectively indentifies the impact of increased PV penetration and its controller on dynamic performance of multi-machine power network containing *** results demonstrate that the model presented can be used in designing of essential controllers for LPP.

关键词： hybrid controller multi-objective optimization large-scale photovoltaic park small-signal scrutiny

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Cascaded PI Controller Tuning for power Plant Superheated Steam Temperature based on Multi-Objective Optimization

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IFAC-PapersOnLine 2017年第1期50卷 3227-3231页

作者： Fu H. Pan L. Xue Y.L. Sun L. Li D.H. Lee K.Y. Wu Z.L. He T. Zheng S. Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Southeast University Nanjing 210096 China State Key Lab of Power System Department of Thermal Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Baylor University Waco TX 76798 United States Department of Automation Fuzhou University Fuzhou 350000 China

To improve the superheated steam temperature (SST) control performance, this paper considers the conflicting objectives in SST control, multi-objective optimization (MOO) algorithm is employed in this paper. Firstly, the inner-loop PI controller is optimized by considering the objectives of set-point tracking and disturbance rejection, with and without constraints on robustness level. It is found that robustness constraint is essential in the optimization. Secondly, the regulation performance of the outer-loop PI controller is addressed by optimizing the disturbance rejection and robustness simultaneously. The parameter are determined based on the simulation comparison between the non-dominated solutions. The simulation results demonstrate a promising prospect of promoting the proposed strategy to more industrial practices. © 2017

关键词： Disturbance rejection Pareto optimization power plant control Robustness Superheated steam temperature

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Machine discovery of partial differential equations from spatiotemporal data

arXiv

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal system Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

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CLASSIFICATION BASED ON CAPSULE NETWORK WITH HYPERSPECTRAL IMAGE

CLASSIFICATION BASED ON CAPSULE NETWORK WITH HYPERSPECTRAL I...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Yi Me Zezhong Zheng Zhengqiang Guo Fan Mou Fangrong Zhou Rui Kong Ankai Hou Mingcang Zhu Yong He Juan Ren Huaixin Chen Zhigang Liu Guoqing Zhou Jiang Li Yunnan Electric Power Research Institute Yunnan Power Grid Co. Ltd. Kunming Yunnan PRC School of Resources and Environment University of Electronic Science and Technology of China Chengdu Sichuan 611731 Department of Natural Resources of Sichuan Province Chengdu Sichuan PRC Sichuan Research Institute for Eco-system Restoration & Geo-disaster Prevention Chengdu Sichuan PRC State Key Laboratory of Remote Sensing Science Jointly Sponsored by Beijing Normal University and the Institute of Remote Sensing and Digital Earth of Chinese Academy of Sciences Beijing PRC Guangxi Key Laboratory for Spatial Information and Geomatics Guilin University of Technology Guilin Guangxi Department of Electrical and Computer Engineering Old Dominion University Norfolk VA USA

Hyperspectral image is usually composed of hundreds of bands rich of spatial and spectral information. And this is an advantage for the common remotely sensed data. Thus, the classification of hyperspectral image could be of great value. However, the dimensionality of hyperspectral image may lead to the curse of dimensionality phenomenon when it is directly used for land use classification or other applications, making it difficult to be utilized effectively. In this paper, we presented a novel classification framework with capsule network based on the spectral and spatial information of hyperspectral images. At first, we use principal components analysis (PCA) to reduce the dimensionalities of hyperspectral image. Then, we use the capsule network to classify hyperspectral image. Our experimental result showed the novel classification framework is more efficient than other six popular methods. Therefore, the capsule network method is robust for hyperspectral image classification.

关键词： Hyperspectral imaging Training Image classification Principal component analysis Logistics

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Stability and Performance Analysis of Saturated systems Using an Enhanced Max Quadratic Lyapunov Function

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IFAC-PapersOnLine 2017年第1期50卷 11847-11852页

作者： Li Y. Lin Z. Li N. Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai 200240 China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville VA 22904-4743 United States

This paper revisits the problem of estimating the domain of attraction and the nonlinear L2 gain of a saturated system with an algebraic loop. A max quadratic Lyapunov function, the maximum function of a group of quadratic Lyapunov functions, is proposed for use in the stability and performance analysis for a saturated system. The max quadratic Lyapunov function involves a partitioning of the state space. Exploiting the special properties of regions of the state space, we propose in this paper an enhanced max quadratic Lyapunov function, which results from adding a term that characterizes the regions of the state space to the max quadratic Lyapunov function. The matrix associated with the enhanced max quadratic Lyapunov function is not required to be positive definite, and thus a set of less conservative stability and performance conditions are established, from which a larger estimate of the domain of attraction and a tighter estimate of the nonlinear L2 gain can be obtained. Simulation results indicate the effectiveness and superiority of the proposed method. © 2017

关键词： Domain of attraction enhanced max quadratic Lyapunov functions input saturation L2 gain

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Policy Optimization for Content Push via Energy Harvesting Small Cells in Heterogeneous Networks

Policy Optimization for Content Push via Energy Harvesting S...

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作者： Gong, Jie Zhou, Sheng Zhou, Zhenyu Niu, Zhisheng School of Data and Computer Science Sun Yat-sen University Guangzhou510006 China Tsinghua National Laboratory for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing102206 China

Motivated by the rapid development of energy harvesting technology and content-aware communication in access networks, this paper considers the push mechanism design in small-cell base stations (SBSs) powered by renewable energy. A user request can be satisfied by either push or unicast from the SBS. If the SBS cannot handle the request, the user is blocked by the SBS and is served by the macro-cell BS instead, which typically consumes more energy. We aim to minimize the ratio of user requests blocked by the SBS to total number of user requests. With finite battery capacity, Markov decision process-based problem is formulated, and the optimal policy is found by dynamic programming (DP). Two threshold-based policies are proposed: the push-only threshold-based policy and the energy-efficient threshold-based policy, and the closed-form blocking probabilities with infinite battery capacity are derived. Numerical results show that the proposed policies outperform the conventional non-push policy if the content popularity changes slowly or the content request generating rate is high, and can achieve the performance of the greedy optimal threshold-based policy. In addition, the performance gap between the threshold-based policies and the DP optimal policy is small when the energy arrival rate is low or the request generating rate is high. © 2016 IEEE.

关键词： Heterogeneous networks

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Breast Cancer Diagnosis Using an Unsupervised Feature Extraction Algorithm Based on Deep Learning

Breast Cancer Diagnosis Using an Unsupervised Feature Extrac...

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第37届中国控制会议

作者： Yawen Xiao Jun Wu Zongli Lin Xiaodong Zhao Department of Automation Shanghai Jiao Tong Universityand Key Laboratory of System Control and Information Processing of Ministry of Education The Center for Bioinformatics and Computational Biology Shanghai Key Laboratory of Regulatory Biologythe Institute of Biomedical Sciences and School of Life SciencesEast China Normal University Charles L.Brown Department of Electrical and Computer Engineering University of Virginia School of Biomedical Engineering Shanghai Jiao Tong University

Breast cancer is the most often detected cancer in women. At the same time, it is one of the most curable types of cancer if diagnosed early. With the development of the detection technology, a growing amount of clinical data and highdimensional features can be used for breast cancer diagnosis. The high-dimensional data contributes to advances in the diagnostic technology, but also incurs a large amount of computational redundancy. Thus, extracting important information and reducing the feature dimension is critical to effective prediction and an accurate treatment ***, the previous works for breast cancer diagnosis are mainly based on labeled data that is difficult to obtain. To address this issue, in this paper, we demonstrate a new scheme, which integrates a deep learning based unsupervised feature extraction algorithm, the stacked auto-encoders, with a support vector machine model（SAE-SVM）, for breast cancer diagnosis. The stacked auto-encoders with the greedy layerwise pre-training and an improved momentum update algorithm is applied to capture essential information and extract necessary features of the original data. Then, a support vector machine model is employed to classify the samples with new features into malignant or benign tumors. The proposed method was tested on the Wisconsin Diagnostic Breast Cancer data set. The performance is evaluated using various measures and compared with the previously published results. The comparison results show that the proposed SAE-SVM method improves the accuracy to 98.25% and outperforms the other methods. The deep learning based unsupervised feature extraction significantly improves the performance of classification and provides a promising approach to breast cancer diagnosis.

关键词： Deep learning auto-encoder feature extraction breast cancer diagnosis unlabeled data

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