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IEEE Green Energy and systems Conference (IGESC)

作者： Pujan Adhikari Easir Arafat 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)9798350379617

ISBN: (纸本)9798350379624

The integration of (ultra) wide bandgap (UWBG) semiconductor technologies, such as silicon carbide and diamond, is crucial for the advancement of zero-emission vehicles (ZEVs), all-electric aircraft (AEA), and renewable energy systems. These technologies operate at higher temperatures, frequencies, and voltages, necessitating the development of novel materials that can endure these demanding conditions. Specifically, the polymeric encapsulation materials currently in use, such as silicone gel and epoxy resin, must be replaced with materials possessing superior properties in every aspect. This paper evaluates the suitability of dibenzyltoluene (DBT), a dielectric liquid, by assessing its thermal conductivity, viscosity, and dielectric breakdown strength under AC voltage. The effects of increased temperature on these properties are examined. Compared to room temperature, DBT exhibits minimal decline in both breakdown strength and thermal conductivity, which are crucial characteristics for insulating media in power modules. When these properties are compared to those of silicone gel, the superior performance of DBT becomes evident. This paper also briefly highlights the challenges to be considered when selecting a dielectric liquid, using the issues related to DBT as a reference.

关键词： Encapsulation Viscosity Temperature Photonic band gap Multichip modules High-voltage techniques Conductivity Thermal conductivity Power electronics Dielectric liquids

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Switching Overvoltage Analysis in Conventional and Unconventional Transmission Lines

Switching Overvoltage Analysis in Conventional and Unconvent...

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IEEE Kansas Power and Energy Conference

作者： Easir Arafat 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)9798350372403

ISBN: (纸本)9798350372410

Electromagnetic transients are a challenge to manage in power systems. Among these concerns are switching overvoltages, which require meticulous attention from power system designers. Evaluating this transient phenomenon becomes imperative when expanding the grid with new installations. This paper delves into the switching overvoltage (SOV) analysis, focusing on a novel proposed unconventional line. A comprehensive comparison with an existing conventional line is conducted utilizing various line models in EMTP-RV. Our investigation explores the nuanced behaviors revealed by frequency-dependent (FD) and wide-band (WB) models, shedding light on distinctive transient responses. The paper will examine the waveform characteristics using a straightforward and computationally efficient example.

关键词： Power transmission lines Computational modeling Loading Switches Power systems Impedance Surges

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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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Impact of High-Frequency Repetitive Bipolar Square Wave Voltages on the Lifetime of Turn-to-Turn Insulation

Impact of High-Frequency Repetitive Bipolar Square Wave Volt...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Md Asifur Rahman Easir Arafat Farzana Islam 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)9798350366679

ISBN: (纸本)9798350366686

Emerging power electronic drives and converters are planned to operate at high voltages with high switching frequencies (up to a few hundred kHz), which will affect the reliability of insulation systems of windings of electrical machines. To evaluate the insulation quality under such high-frequency pulses, lifetime test is a prominent way. For simplification of test procedure, turn-to-turn insulation samples are approved by IEC 60034-18-42. In past studies, the impact of switching frequencies has been investigated under lower frequencies (up to 20 kHz), which cannot address the challenges of next-generation wide bandgap (WBG)-based power converters. In this study, the lifetimes of turn-to-turn insulations under high switching frequencies (50 kHz) are tested at four different rise times (50 ns, 100 ns, 150 ns, 200 ns), and the impact of switching frequency on the lifetime of the insulation is analyzed.

关键词： Insulation Time-frequency analysis Power system measurements Photonic band gap Switching frequency Windings High-voltage techniques Reliability engineering Power electronics Next generation networking

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Symbiotic Control of Uncertain Dynamical systems: Harnessing Synergy Between Fixed-Gain Control and Adaptive Learning Architectures

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IEEE Transactions on Automatic Control 2025年

作者： Yucelen, Tansel Sarsilmaz, Selahattin Burak Yildirim, Emre University of South Florida Department of Mechanical Engineering Laboratory for Autonomy Control Information and Systems TampaFL33620 United States Utah State University Department of Electrical and Computer Engineering LoganUT84322 United States

Both fixed-gain control and adaptive learning architectures aim to mitigate the effects of uncertainties. In particular, fixed-gain control offers more predictable closed-loop system behavior but requires the knowledge of uncertainty bounds. In contrast, while adaptive learning does not necessarily require such knowledge, it often results in less predictable closed-loop system behavior compared to fixed-gain control. To this end, this paper presents a novel symbiotic control framework that offers the strengths of fixed-gain control and adaptive learning architectures. Specifically, this framework synergistically integrates these architectures to mitigate the effects of uncertainties in a more predictable manner as compared to adaptive learning alone and it does not require any knowledge on such uncertainties. Both parametric and nonparametric uncertainties are considered, where we utilize neural networks to approximate the unknown uncertainty basis for the latter case. Counterintuitively, the proposed framework has the ability to achieve a desired level of closed-loop system behavior even with an insufficient number of neurons (e.g., when the neural network approximation error is large) or in the face of injudiciously selected adaptive learning parameters (e.g., high leakage term parameters). © 1963-2012 IEEE.

关键词： Closed loop control systems

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Bipolar MVDC Cable Under Different Ambient Conditions for All-Electric Aircraft

Bipolar MVDC Cable Under Different Ambient Conditions for Al...

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IEEE Kansas Power and Energy Conference

作者： Farzana Islam Anoy Saha Md Asifur Rahman 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)9798350372403

ISBN: (纸本)9798350372410

To realize net zero emissions in aviation sector, all electric aircraft (AEA) are an optimistic technology. To make commercial wide body AEA a reality, one of the main challenges is to design their electric power system (EPS) having high power density and the lowest possible system mass. The power needed and pressure level during take-off and cruising time create opposing conditions, so designing one cable that meets all the requirements is crucial. In low-pressure conditions at cruising altitude (12.2 km), cable design faces significant thermal challenges because of limited heat transfer. A comprehensive multiphysics model is necessary to compute the distribution of temperature across the cable under various environmental conditions to find the optimal cable configuration. This study focuses on a cylindrical bipolar 5 kV DC cable system in a duct with multilayer insulation at atmospheric and low (18.8k Pa) pressure based on the maximum current that flows in the introduced EPS of N3-X (NASA) AEA system under the worst-case of single contingency conditions so that the designed cable can handle any single contingency situations maintaining thermal limits.

关键词： Temperature distribution Power system measurements Power cables Atmospheric modeling Simulation Contingency management Power cable insulation

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Machine Learning for Scalable and Optimal Load Shedding Under Power System Contingency

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IEEE Transactions on Power systems 2025年

作者： Zhou, Yuqi Zhu, Hao National Renewable Energy Laboratory Power Systems Engineering Center GoldenCO80401 United States The University of Texas Chandra Family Department of Electrical & Computer Engineering AustinTX78712 United States

Prompt and effective corrective actions in response to unexpected contingencies are crucial for improving power system resilience and preventing cascading blackouts. The optimal load shedding (OLS) accounting for network limits has the potential to address the diverse system-wide impacts of contingency scenarios as compared to traditional local schemes. However, due to the fast cascading propagation of initial contingencies, real-time OLS solutions are challenging to attain in large systems with high computation and communication needs. In this paper, we propose a decentralized design that leverages offline training of a neural network (NN) model for individual load centers to autonomously construct the OLS solutions from locally available measurements. Our learning-for-OLS approach can greatly reduce the computation and communication needs during online emergency responses, thus preventing the cascading propagation of contingencies for enhanced power grid resilience. Numerical studies on both the IEEE 118-bus system and a synthetic Texas 2000-bus system have demonstrated the efficiency and effectiveness of our scalable OLS learning design for timely power system emergency operations. © 1969-2012 IEEE.

关键词： Decentralized control

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Two-Stage Robust Optimization Under Decision Dependent Uncertainty

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IEEE/CAA Journal of Automatica Sinica 2022年第7期9卷 1295-1306页

作者： Yunfan Zhang Feng Liu Yifan Su Yue Chen Zhaojian Wang João P.S.Catalão State Key Laboratory of Power System and Generation Equipment the Department of Electrical EngineeringTsinghua UniversityBeijing 100084China Department of Mechanical and Automation Engineering the Chinese University of Hong KongHong Kong SARChina Ministry of Education Key Laboratory of System Control and Information Processing the Department of AutomationShanghai Jiao Tong Universityand also with Shanghai Engineering Research Center of Intelligent Control and ManagementShanghai 200240China Faculty of Engineering of the University of Porto and Institute for Systems and Computer Engineering Technology and Science(INESC TEC)Porto 4200-465Portugal IEEE

In the conventional robust optimization(RO)context,the uncertainty is regarded as residing in a predetermined and fixed uncertainty *** many applications,however,uncertainties are affected by decisions,making the current RO framework *** paper investigates a class of two-stage RO problems that involve decision-dependent *** introduce a class of polyhedral uncertainty sets whose right-hand-side vector has a dependency on the here-and-now decisions and seek to derive the exact optimal wait-and-see decisions for the second-stage problem.A novel iterative algorithm based on the Benders dual decomposition is proposed where advanced optimality cuts and feasibility cuts are designed to incorporate the uncertainty-decision *** computational tractability,robust feasibility and optimality,and convergence performance of the proposed algorithm are guaranteed with theoretical *** motivating application examples that feature the decision-dependent uncertainties are ***,the proposed solution methodology is verified by conducting case studies on the pre-disaster highway investment problem.

关键词： Benders decomposition decision-dependent uncertainty endogenous uncertainty robust optimization(RO)

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Contrast enhancement of optical videoscope images

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Iran Journal of computer Science 2023年第3期6卷 261-275页

作者： Ammar, Reda Aboshosha, Sahar El-Hag, Noha A. Saifeldeen, Reda El-Shafai, Walid El-Banby, Ghada M. Ashiba, Huda I. Khalaf, Ashraf A. M. Elazm, Atef Abou El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. El-Safrawey, Amir Department of Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Comm. Faculty of Engineering Minia University Minya Egypt Department of Management of Information Systems Higher Institute of Commercial Science El-Mahala El-Kobra Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic Engineering Menoufia University Menouf 32952 Egypt Department of Electronics and Electrical Communications Engineering Bilbis Higher Institute of Engineering Sharqia Bilbis Egypt Security Engineering Laboratory Computer Science Department Prince Sultan University Riyadh 11586 Saudi Arabia Department of Information Technology College of Computer and Information Sciences Princess Nourah Bint Abdulrahman University P.O. Box 84428 Riyadh 11671 Saudi Arabia

The videoscope (VS) images have poor quality and low contrast. Hence, in this paper, three proposed frameworks to improve the quality of VS images are presented. The first framework depends on contrast-limited adaptive histogram equalization (CLAHE) with adaptive gamma correction. The second framework depends on homomorphic processing. The third framework is based on the homomorphic enhancement with histogram equalization. The first one is superior from the entropy perspective, while the third one is superior from the contrast perspective. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2023.

关键词： Fuzzy enhancement Histogram equalization Homomorphic enhancement Spectral entropy

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Data-Driven Graph Construction of Power Flow Graphs for Electric Power Transmission Networks

Data-Driven Graph Construction of Power Flow Graphs for Elec...

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International Conference on Machine Learning and Applications (ICMLA)

作者： Benjamin Poole Rajan Ratnakumar Dulip Tharaka Madurasinghe Christian Kümmerle Ganesh Kumar Venayagamoorthy Minwoo Lee Department of Computer Science University of North Carolina Charlotte USA Real-Time Power and Intelligent Systems Laboratory Clemson University USA Department of Electrical Electronic and Computer Engineering University of Pretoria South Africa

ISBN: (数字)9798350374889

ISBN: (纸本)9798350374896

A comprehensive understanding of the topology of the electric power transmission network (EPTN) is essential for reliable and robust control of power systems. While existing research primarily relies on domain-specific methods, it lacks data-driven approaches that have proven effective in modeling the topology of complex systems. To address this gap, this paper explores the potential of data-driven methods for more accurate and adaptive solutions to uncover the true underlying topology of EPTNs. First, this paper examines Gaussian Graphical Models (GGM) to create an EPTN network graph (i.e., undirected simple graph). Second, to further refine and validate this estimated network graph, a physics-based, domain-specific refinement algorithm is proposed to prune false edges and construct the corresponding electric power flow network graph (i.e., directed multi-graph). The proposed method is tested using a synchrophasor dataset collected from a two-area, four-machine power system simulated on the real-time digital simulator (RTDS) platform. Experimental results show both the network and flow graphs can be reconstructed using various operating conditions and topologies with limited failure cases.

关键词： Robust control Network topology Data models Real-time systems Phasor measurement units Power systems Topology Power system reliability Reliability Load flow

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