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IEEE Texas Power and Energy Conference (TPEC)

作者： Anoy Saha 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

Designing power cables that provide high power and low system mass is one of the major goals in achieving the future all-electric wide-body aircraft. Radiative and convective heat transfers from a cable's surface to the surrounding air determine how much current is permitted to flow through it. At a cruising altitude of 12.2 km (18.8 kPa) for wide-body aircraft, the limited heat transfer by convection poses thermal issues for the design of aircraft cables. These thermal challenges are exacerbated for bipolar electric power systems (EPS), which are usually made up of two power lines next to each other. The cable's surface area affects both convective and radiative heat transfers. Changing the shape of the cable is one technique to improve heat transfers and compensate for the reduced convective heat transfer caused by low air pressure. In comparison to cylindrical and cuboid cables, the rectangular geometry design gives a bigger contact area with the surrounding atmosphere for the same cross-section area, hence it is anticipated that the heat transfer would rise and as a result, the cable's maximum power-carrying capability will be higher. The purpose of this paper is to design ±5 kV bipolar MVDC power cables with rectangular geometry to raise the maximum current carrying capacity of the cable and analyze its performance with bipolar cylindrical and cuboid geometries.

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A Review of Insulation Challenges and Mitigation Strategies in (U)WBG Power Modules Packaging

A Review of Insulation Challenges and Mitigation Strategies ...

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IEEE Texas Power and Energy Conference (TPEC)

作者： Pujan Adhikari 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

In the ever-growing landscape of electrical power demand, the future of power electronics module packaging lies in the realm of wide-bandgap (WBG) materials, including silicon carbide (SiC), gallium nitride (GaN), and cutting-edge ultra WBG (UWBG) materials like diamond, aluminum nitride (AlN), and hexagonal-boron nitride (h-BN). These materials offer superior properties to traditional silicon-based devices, promising higher power density, reduced weight, and increased operating temperature, voltage, and frequency. However, pushing the boundaries for power electronics modules presents challenges in insulation systems as the encapsulation material and the ceramic substrate may not withstand the functional parameters, potentially leading to unfavorable conditions like high field stress and partial discharge (PD), ultimately resulting in insulation failure. This paper presents a thorough analysis of the characteristics of the electrical insulation materials used in power electronics devices based on the research in WBG packaging conducted in recent years. The significance of maximum electric field stress at triple points (TPs) is examined. Furthermore, the paper reviews the strategies and techniques employed to mitigate the challenges related to maximum field stress and PDs in both encapsulation and substrate materials. It is concluded that the mitigation strategies are promising in improving insulation systems for packaging, but the studies lack their implementation under actual operating conditions of WBG power modules.

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A Test System Meeting Requirements Under Normal and Contingency Conditions at Multiple Loadings for Transmission Expansion Planning

A Test System Meeting Requirements Under Normal and Continge...

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Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

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

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

This paper introduces a high-voltage test system tailored specifically for transmission expansion planning (TEP). The network incorporates long transmission lines, and the line series impedance and shunt admittance for each line are computed utilizing the equivalent $\pi$ circuit model for long transmission lines to account for the distributed nature of line parameters. The proposed test system offers technically feasible load flow solutions under normal and all single contingency conditions for three distinct loading scenarios: peak, dominant, and light loading conditions. As the real power system is subject to various loading scenarios and should be effectively operable under all conditions, this test system accurately replicates the properties of real power systems. The test system introduced in this paper can be a valuable resource for TEP research.

关键词： Technical requirements Power transmission lines Loading Contingency management High-voltage techniques Planning Power system reliability

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Robust Feature Selection With Weight Cost Maximin Optimization

Robust Feature Selection With Weight Cost Maximin Optimizati...

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IEEE Workshop on Machine Learning for Signal Processing

作者： Mohammad Amin Omidi Babak Seyfe Shahrokh Valaee Department of Electrical Engineering Information Theoretic Learning Systems Laboratory (ITLS) Shahed University Tehran Iran Department of Electrical and Computer Engineering University of Toronto Toronto Canada

Recently, applying data-independent random feature extraction methods has been promising for time-series classifications. However, a large portion of the features generated by these methods is redundant. To address this problem, we propose a robust feature selection method that applies a maximin optimization to a weight cost function that makes a trade-off between dimension reduction and preserving the classification accuracy. It can be shown that the weight cost is discrete midpoint convex with respect to the sparsity parameter, and at the same time is concave with respect to the regularization parameter. Based on these findings, we propose an efficient implementation for feature selection with maximin optimization. Our empirical results show that for large classification tasks with a small sample size per class, our feature selection method removes more redundancies than the state-of-the-art methods, while preserving the classification accuracy.

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Digital Twin for Solar Photovoltaic Power Estimations Based on an Ensemble of Recurrent Neural Networks

Digital Twin for Solar Photovoltaic Power Estimations Based ...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Michael Walters Ganesh K. Venayagamoorthy Holcombe Department of Electrical and Computer Engineering Real-Time Power and Intelligent Systems Laboratory Clemson University Clemson SC U.S.A

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

Planning, managing, and maintaining solar photovoltaic (PV) plants is becoming increasingly challenging as a result of their increasing implementation world-wide. Solar PV power generation estimations provide a source of knowledge and certainty, assisting system operators in day-to-day responsibilities. Digital twins (DTs) replicate physical entities within a virtual setting, providing a real-time platform to perform solar PV power generation estimations, further enhancing situational awareness and operational efficiency. In this paper, a DT is developed and implemented for Clemson University's 1 MW solar PV plant located in South Carolina, USA to perform solar PV power generation estimations. An ensemble of Elman recurrent neural networks (ERNNs) is utilized in the DT for solar PV power generation estimations, replicating PV plant behaviors and characteristics. The ERNN ensemble utilizes data collected at the PV plant site, i.e. generated power, solar irradiance and ambient temperature. The DT's performance is evaluated based on different weather conditions and ERNN ensemble's output selection methods. Typical results are presented to show the effectiveness of the neural network ensemble based DT for solar PV power generation estimations.

关键词： Photovoltaic systems Recurrent neural networks Accuracy Heuristic algorithms Power system dynamics Estimation Artificial neural networks

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Design of Distributed Nonlinear Observers Without Using Global Inputs

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IEEE Transactions on Automatic control 2025年

作者： Cao, Ganghui Wang, Jinzhi Polycarpou, Marios M. Peking University State Key Laboratory for Turbulence and Complex Systems Department of Mechanics and Engineering Science College of Engineering Beijing100871 China University of Cyprus KIOS Research and Innovation Center of Excellence and the Department of Electrical and Computer Engineering Nicosia1678 Cyprus

This paper investigates the design of distributed observers for a class of nonlinear systems. The designed distributed observers reside in a network of sensor nodes. The communication links in the network enable each sensor node to estimate all states of the nonlinear system, even though each one can only collect a small portion of the system's outputs. However, all inputs of the system are still needed at each node to carry out a correct state estimation. For this problem, the paper further proposes a distributed nonlinear observer without using global inputs, in which each sensor node only needs partial input information of the nonlinear system. © 1963-2012 IEEE.

关键词： Nonlinear simulations

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A Multi-Player Potential Game Approach for Sensor Network Localization with Noisy Measurements

A Multi-Player Potential Game Approach for Sensor Network Lo...

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IEEE Conference on Decision and control

作者： Gehui Xu Guanpu Chen Baris Fidan Yiguang Hong Hongsheng Qi Thomas Parisini Karl H. Johansson Department of Electrical and Electronic Engineering Imperial College London London UK Division of Decision and Control Systems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Department of Mechanical and Mechatronics Engineering University of Waterloo Waterloo ON Canada Department of Control Science and Engineering Tongji University shanghai China Shanghai Research Institute for Intelligent Autonomous Systems Shanghai China Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Beijing China School of Mathematical Sciences University of Chinese Academy of Sciences Beijing China Department of Electronic Systems Aalborg University Denmark Department of Engineering and Architecture University of Trieste Italy

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Sensor network localization (SNL) is a challenging problem due to its inherent non-convexity and the effects of noise in inter-node ranging measurements and anchor node position. We formulate a non-convex SNL problem as a multiplayer non-convex potential game and investigate the existence and uniqueness of a Nash equilibrium (NE) in both the ideal setting without measurement noise and the practical setting with measurement noise. We first show that the NE exists and is unique in the noiseless case, and corresponds to the precise network localization. Then, we study the SNL for the case with errors affecting the anchor node position and the inter-node distance measurements. Specifically, we establish that in case these errors are sufficiently small, the NE exists and is unique. It is shown that the NE is an approximate solution to the SNL problem, and that the position errors can be quantified accordingly. Based on these findings, we apply the results to case studies involving only inter-node distance measurement errors and only anchor position information inaccuracies.

关键词： Location awareness Noise Games Position measurement Nash equilibrium Distance measurement Noise measurement

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Effect of Forced Heat Convection on Heat Transfer for Bipolar MVDC Power Cables in Envisaged Wide-Body All-Electric Aircraft

Effect of Forced Heat Convection on Heat Transfer for Bipola...

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Proceedings of the IEEE Dallas Circuits and systems Workshop (DCAS)

作者： Md Asifur Rahman Anoy Saha 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

ISBN: (数字)9798350349535

ISBN: (纸本)9798350349542

Since transportation is one of the primary sources of greenhouse gas (GHG) emissions, its electrification is essential for achieving the net-zero GHG emissions target. As a part of transportation electrification, all-electric aircraft (AEA) have emerged as a viable approach to achieve net-zero emissions in aviation. Electric power systems (EPS) with high power density and low system mass are required for future wide-body AEA. Power cables, as one of the key components of the aircraft EPS, must be constructed to maximize the overall power density of the EPS. Because of the limited heat transfer by convection at cruising altitudes of wide-body AEA, the design of power cables faces significant thermal challenges. These difficulties are worsened using bipolar MVDC EPSs, which are generally composed of two power cables, negative and positive poles, adjacent to one another. Heat transfers that occur through convection and radiation are influenced by the cable's surface area. To offset the decrease in convective heat transfer brought on by the low air pressure, one method is to apply forced heat convection. Increasing the velocity of forced air in a cable duct, either the surface temperature of the cable can be reduced, or the core diameter can be reduced along with the cable weight maintaining the same temperature obtained from natural heat convection. This paper examines the impact of forced heat convection on a conventional cylindrical bipolar cable system. The cable system is equipped with our novel designed ARC-SCT-MMEI insulation system delineated in the paper, and its performance is compared to that of natural heat convection.

关键词： Power system measurements Power cables Density measurement Net zero Ducts Power cable insulation Transportation

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Medical Image Fusion in the NSST Domain with AR-Improved PA-PCNN 2022

Medical Image Fusion in the NSST Domain with AR-Improved PA-...

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4th International Conference on Intelligent Medicine and Image Processing, IMIP 2022

作者： Pei, Gaole Shi, T.A.O. Li, Shixing College of Electrical Engineering North China University of Science and Technology China Tianjin Key Laboratory for Control Theory and Applications in Complicated Systems School of Electrical and Electronic Engineering Tianjin University of Technology China Department of Radiology Tianjin Beichen Hospital China

ISBN: (纸本)9781450396202

To present a medical image fusion method based on the non-subsampled shearlet transform (NSST) and improved parametric adaptive pulse-coupled neural network (PA-PCNN) for CT and MRI, which makes the fused images clearer and contains more information. The matched brain CT and MR-T2 are multi-scale decomposed by NSST, and the two source images are decomposed into low-frequency sub-bands and high-frequency sub-bands. The low-frequency fusion coefficients are obtained by fusing the low-frequency sub-band coefficients using the weighted sum-modified-Laplacian (WSML) low-frequency fusion rule. Meanwhile, the high-frequency sub-band coefficients are fused using the PA-PCNN model modified by the area ratio (AR) principle to obtain the high-frequency fusion coefficients. Finally, the low-frequency fusion coefficients and the high-frequency fusion coefficients are inverse transformed by NSST to obtain the final fused image. Compared to viewing CT or MR images alone, the fused images obviously display the relationship between soft mass and hard tissues. The fused image contains both the information of CT and MR. After fusion by the NSST-PA-AR-PCNN algorithm proposed in this paper, the details of the fused images are richer and the image quality is better than that of the previous fusion algorithms. This improved medical image fusion method of CT and MR could result that the final fused images are of higher quality to help detect anatomical structures and associated lesions in the brain. © 2022 ACM.

关键词： Magnetic resonance imaging

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Multi-View Radar Autoencoder for Self-Supervised Automotive Radar Representation Learning

Multi-View Radar Autoencoder for Self-Supervised Automotive ...

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IEEE Symposium on Intelligent Vehicle

作者： Haoran Zhu Haoze He Anna Choromanska Satish Ravindran Binbin Shi Lihui Chen Department of Electrical and Computer Engineering Learning Systems Laboratory New York University NY USA NXP Semiconductors San Jose CA USA

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

Automotive radar has been extensively utilized in cars for many years as an essential sensor, primarily due to its robustness in extreme weather conditions, its capacity to measure Doppler information in the surrounding environment, and its cost-effectiveness. Recently, developments in radar technologies and the availability of open-source radar data sets have attracted more attention to radars and using them for perception tasks in deep learning based autonomous driving. However, annotating radar data for large-scale autonomous driving perception tasks is challenging, i.e., it is difficult for humans to label this data and often requires a semi-automatic approach that involves projecting labels from other sensors, such as cameras and LiDARs. The lack of high-quality labeled data has limited the performance of radar perception models. In this paper, we propose MVRAE, a Multi-View Radar AutoEncoder, which employs self-supervised learning to learn meaningful representations from multi-view radar data without any labels. Our approach is based on the intuition that a good representation for multi-view radar data, which includes range-angle, range-Doppler, and angle-Doppler views, should enable the reconstruction of one view solely from the representations of the other two views. Experimental results demonstrate that our proposed self-supervised method, that can be used as a pre-training step for autonomous driving task, allows the model to learn meaningful representations from unlabeled radar data and achieves enhanced label efficiency for downstream tasks, such as radar semantic segmentation. To the best of our knowledge, MVRAE is the first work that employs self-supervised learning and conducts systematic experiments with multi-view radar data.

关键词： Representation learning Systematics Semantic segmentation Radar Self-supervised learning Data models Doppler radar

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