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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Mohammad Hamidieh Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

This paper aims to explore the significance of accurately defining the geometry of needle electrodes in gas discharge plasma finite-element simulations and evaluate the sensitivity and responsiveness of simulation outputs to different needle electrode shapes. Using a hydrodynamic (drift-diffusion) model and COMSOL Multiphysics software, a comprehensive numerical analysis is performed to investigate the influence of six needle geometries (hyperbolic, elliptic, and circle-with-tangents) with an equal radius of curvature on negative air discharge plasma characteristics. The study aims to establish a comparative understanding of how the defined geometries relate to the behavior of the discharge plasma. Various aspects, including the properties of Trichel pulses, the spatiotemporal evolution of charged species, and the electrical field distribution, are explored.

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A Test System for Transmission Expansion Planning Studies Meeting the Operation Requirements Under Normal Condition as Well as all Single Contingencies

A Test System for Transmission Expansion Planning Studies Me...

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Annual North-American Power Symposium

作者： Bhuban Dhamala Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

This paper presents a 17-bus 500 kV test system for transmission expansion planning (TEP) studies. An actual 500 kV transmission line geometry was used for the overhead lines of this system. Although many test systems have been introduced for different types of power system analysis, those especially for TEP studies at a transmission voltage level, not distribution voltage level, are few. To the best of our knowledge, the introduced test systems for TEP studies, either those combined with electricity market problems or those used to connect a new load or generation to an existing power grid, consider the studies under only normal condition. However, for TEP studies it is needed that a test system meets voltage drop and line loading limits criteria under normal condition as well as all single contingencies, and in this regard, addressing the latter, all single contingencies, is challenging. This paper addresses this technical gap, introducing a 17-bus test system at a transmission voltage level, 500 kV, that meets requirements under normal condition as well as all single contingencies. In addition to presenting all details of this new test system, load flow results under normal condition as well as the worst single contigency are presented. For studies on the TEP, this test system can be an invaluable resource.

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Geometric Graph Filters and Neural Networks: Limit Properties and Discriminability Trade-offs

arXiv

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arXiv 2023年

作者： Wang, Zhiyang Ruiz, Luana Ribeiro, Alejandro Department of Electrical and Systems Engineering University of Pennsylvania PA United States Computer Science & Artificial Intelligence Laboratory MIT MA United States

This paper studies the relationship between a graph neural network (GNN) and a manifold neural network (MNN) when the graph is constructed from a set of points sampled from the manifold, thus encoding geometric information. We consider convolutional MNNs and GNNs where the manifold and the graph convolutions are respectively defined in terms of the Laplace-Beltrami operator and the graph Laplacian. Using the appropriate kernels, we analyze both dense and moderately sparse graphs. We prove non-asymptotic error bounds showing that convolutional filters and neural networks on these graphs converge to convolutional filters and neural networks on the continuous manifold. As a byproduct of this analysis, we observe an important trade-off between the discriminability of graph filters and their ability to approximate the desired behavior of manifold filters. We then discuss how this trade-off is ameliorated in neural networks due to the frequency mixing property of nonlinearities. We further derive a transferability corollary for geometric graphs sampled from the same manifold. We validate our results numerically on a navigation control problem and a point cloud classification task. © 2023, CC0.

关键词： Graph neural networks

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Influence of Solid Dielectric Permittivity on Positive Streamer Characteristics in a Liquid-Solid Composite Dielectric

Influence of Solid Dielectric Permittivity on Positive Strea...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

This paper presents a numerical investigation of positive streamers in a liquid-solid composite dielectric system, specifically in the context of wet-mate DC connectors. The study focuses on the influence of different materials and relative permittivity values of the solid dielectric on streamer behavior. The study employs a non-dimensionalized electric field-dependent molecular ionization streamer model to describe the initiation and propagation of streamers within a needle-sphere electrode system. A 2D-axisymmetric COMSOL model is utilized, where a solid tube-like dielectric is placed near the needle tip in an electrode system filled with transformer oil. The effects of varying relative permittivity values on the electric field distribution, streamer propagation velocity, ionization and attachment rates, and spatiotemporal evolution of charged species (electrons, positive ions, and negative ions) are studied. By analyzing these aspects, the paper aims to enhance the understanding of streamer dynamics and provide valuable information for optimizing the design and performance of equipment utilizing liquid-solid composite dielectric systems.

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Modular Multilevel DC-DC Converter with Inherent Bipolar Operation Capability for Resilient Bipolar MVDC Grids

Modular Multilevel DC-DC Converter with Inherent Bipolar Ope...

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作者： Zhao, Shanshan Chen, Yu Cui, Shenghui Hu, Jingxin State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology China Seoul National University Department of Electrical and Computer Engineering Korea Republic of Institute for Power Generation and Storage Systems E. on Energy Research Center RWTH Aachen University Germany

Bipolar medium-voltage dc (MVDC) distribution systems are of great interests nowadays due to its high availability and reliability. A bipolar MVDC grid can be established by the installation of power-balancers or directly by converters with inherent bipolar operation capability to avoid the extra cost and volume. Therefore, this paper proposes a modular multilevel dc-dc converter (MMDC) with inherent bipolar operation capability for the interconnection of bipolar MVDC grids and LVDC girds. Considering that the MMDC is normally only capable of operating with monopole MVDC distribution systems due to the symmetric structure and operation scheme, a center-Tapped high-frequency interface transformer is employed on the MV side of the MMDC. Based on the concept of the flux dc-bias cancellation, a dedicated operation method is proposed, whereby the power flows of the two MVDC poles can be regulated independently with a simple control scheme, and no penalty of increased current rating is imposed on the semiconductor devices or the interface transformer compared to the conventional MMDC with monopole operation. Additionally, the MMDC can realize single-pole operation in case one pole is faulty and deliver at least 50% of the rated power capacity, which significantly enhance the reliability and availability of the power delivery. The validity of the proposed bipolar operation scheme of the MMDC has been verified by both simulations and experiments with a down-scale prototype. © 2017 CPSS.

关键词： Poles

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Wavelet Convolutional Neural Network for Low-Resolution Brain MRI Images

Wavelet Convolutional Neural Network for Low-Resolution Brai...

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IEEE International Symposium on Biomedical Imaging

作者： Soo Min Oh Yifan Li Jo Woon Chong Wireless Information and Network Sciences Laboratory Massachusetts Institute of Technology Cambridge MA USA Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Statistics Texas A&M University College Station TX USA Department of Electrical and Computer Engineering Texas Tech University Lubbock TX USA Department of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Low-resolution images inherently contain less information, making effective feature extraction more challenging and posing difficulties for training neural networks. However, if neural networks can be trained successfully on low-resolution images, this could significantly reduce memory storage requirements and computational costs. In this study, we address the limitations of low-resolution brain MRI images by enhancing the available information through wavelet transform techniques. Specifically, we leverage the high-frequency coefficients obtained from wavelet transforms and the Hurst exponent to improve feature representation and optimize model training for convolutional neural networks (CNN), which is referred to as the wavelet CNN (WCNN). We demonstrate that WCNN outperforms standard CNNs in multi-class classification tasks, distinguishing among four categories of brain MRI images such as normal, glioma, meningioma, and pituitary tumor images. This approach highlights the potential to achieve high classification accuracy even with low-resolution data, ultimately reducing the memory and computational resources required for data processing and model training.

关键词： Wavelet transforms Training Accuracy Magnetic resonance imaging Computational modeling Brain modeling Feature extraction Data models Convolutional neural networks Tumors

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A New Method for Calculating Electric Field Intensity on Subconductors in Unconventional High Voltage, High Power Density Transmission Lines

A New Method for Calculating Electric Field Intensity on Sub...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Mushfiqul Abedin Khan Mona Ghassemi Department of Electrical and Computer Engineering Zero Emission Realization of Optimized Energy Systems (ZEROES) Laboratory The University of Texas at Dallas Richardson TX USA

The electric field intensity on subconductors is the most important parameter to consider when electrically designing (extra) high voltage, (E)HV, transmission lines. In this regard, it is not possible to use existing formulae for calculating electric field intensity on subconductors, which are for subconductors placed symmetrically on bundle circles, for recently introduced unconventional high power density lines where subconductors can be located in unsymmetrical or at any location in the space. Further, in existing formulas, nearby (other phases) and image conductors are ignored, and a uniform field is assumed despite the fact that electric fields are distorted in the presence of other subconductors in a bundle. None of the mentioned assumptions are valid for unconventional high power density lines, so it is necessary to develop an innovative method for calculating electric field on subconductors on these new lines. In our method, $n_{m}$ line charges for each subconductor are employed to set the electric potentials of $n_{m}$ points at the periphery of that subconductor equal to the applied voltage. The line charges are uniformly distributed around a hypothetical cylinder with a radius of $r/2$ , where $r$ is the radius of that subconductor. The challenge is choosing a proper $n_{m}$ , leading to an accurate result.

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Unconventional High Surge Impedance Loading (HSIL) Lines and Transmission Expansion Planning

Unconventional High Surge Impedance Loading (HSIL) Lines and...

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Annual North-American Power Symposium

Transmission expansion planning (TEP) is crucial for maintaining the reliable and efficient operation of the power systems, particularly in the face of increasing electricity demand and the integration of renewable energy sources. This paper aims to investigate the application of unconventional high surge impedance loading (HSIL) lines in TEP and presents a comparative analysis of their outcomes against conventional line-based TEP approaches. Starting with a 17-bus 500 kV test system, which can operate well under normal operating condition as well as all single contingency conditions, the objective is to connect a new load located in a new bus, bus #18, to the existing test system via two approaches: using conventional lines and incorporating unconventional HSIL lines. By comparing the number of lines required for the conventional and unconventional approaches, maintaining identical conductor weight per circuit, the effectiveness of unconventional HSIL lines in TEP is evaluated where using only two unconventional HSIL lines is sufficient to connect 1250 MW load demand at bus 18 while three transmission lines are required when using the conventional line. Finally, a thorough economic analysis has been conducted on both TEP scenarios, revealing that implementing unconventional HSIL lines leads to remarkable cost savings and thus can be considered a promising option for TEP studies.

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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ERASE-Net: Efficient Segmentation Networks for Automotive Radar Signals

ERASE-Net: Efficient Segmentation Networks for Automotive Ra...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Shihong Fang Haoran Zhu Devansh Bisla Anna Choromanska Satish Ravindran Dongyin Ren Ryan Wu Department of Electrical and Computer Engineering Learning Systems Laboratory New York University NY USA NXP Semiconductors San Jose CA USA

Among various sensors for assisted and autonomous driving systems, automotive radar has been considered as a robust and low-cost solution even in adverse weather or lighting conditions. With the recent development of radar technologies and open-sourced annotated data sets, semantic segmentation with radar signals has become very promising. However, existing methods are either computationally expensive or discard significant amounts of valuable information from raw 3D radar signals by reducing them to 2D planes via averaging. In this work, we introduce ERASE-Net, an Efficient RAdar SEgmentation Network to segment the raw radar signals semantically. The core of our approach is the novel detect-then-segment method for raw radar signals. It first detects the center point of each object, then extracts a compact radar signal representation, and finally performs semantic segmentation. We show that our method can achieve superior performance on radar semantic segmentation task compared to the state-of-the-art (SOTA) technique. Furthermore, our approach requires up to 20×less computational resources. Finally, we show that the proposed ERASE-Net can be compressed by 40% without significant loss in performance, significantly more than the SOTA network, which makes it a more promising candidate for practical automotive applications.

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