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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

The need for lunar surface power is expected to grow to megawatt levels, which is in line with NASA's objective of building a long-term human presence on the moon that can house more than 100 people. As humanity seeks to establish a long-term presence on the moon and construct lunar habitats or bases, the importance of efficient and dependable power transmission on the moon becomes crucial. Power lines of varied lengths and power transfer capacities are needed for a lunar-based power system to link the various power generation, energy storage, and load facilities dispersed over the lunar surface. Developing power lines with high power density, while considering weight, volume, safety, cost-effectiveness, and reliability in the harsh lunar environment, poses a significant challenge. Three potential locations for power lines on the lunar surface can be considered: above the surface, on the surface, or below the surface. The extreme lunar environment, characterized by factors such as temperature variation, micrometeoroid bombardment, and ionizing radiation, limits the effectiveness of the first two methods for power transfer on the moon. Underground deployment offers both safety and convenience. Using the COMSOL Multiphysics software, this work proposes a novel method for designing and assessing affordable bipolar HVDC cables for power transmission below the lunar surface with an emphasis on its electrical insulation difficulties. At a voltage level of up to ±10 kV, the cables are intended to transfer power at two different levels: medium power (500 kW), and high power (1 MW). These specially designed cables offer a viable solution for power transmission on the moon, as they are capable of functioning effectively in harsh lunar conditions.

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A High Voltage Test System Meeting Requirements Under Normal and All Single Contingencies Conditions of Peak, Dominant, and Light Loadings for Transmission Expansion Planning Studies

A High Voltage Test System Meeting Requirements Under Normal...

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IEEE Texas Power and Energy Conference (TPEC)

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 introduces a new 17-bus 500 kV test system that has been specially designed to support transmission expansion planning studies. Unlike other test systems that have been developed for power system analysis, this system is unique because it focuses exclusively on transmission expansion planning at high voltage levels. Existing test systems that are used for TEP studies, in combination with electricity market problems or for integrating new generation or loads, tend to consider the system operations only under normal conditions and one loading condition. However, in practical scenarios, the system must be able to operate under different loading conditions and single contingencies to ensure reliability. Therefore, a reliable test system should mimic the behavior of a real power system, operating under both normal and all single contingency conditions for different loading conditions. This paper addresses this need by introducing a 17-bus 500 kV test system that can operate successfully under three different loading conditions (peak load, dominant load - 60% of peak load, and light load - 40% of peak load), while also being able to handle the operation at normal and single contingency conditions in each case.

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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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Evaluating Dibenzyltoluene as a Dielectric Liquid for Encapsulation in High Voltage, High Power Density U(WBG) Power Electronics Modules

Evaluating Dibenzyltoluene as a Dielectric Liquid for Encaps...

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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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