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2023 IEEE Workshop on Power Electronics for Aerospace Applications, PEASA 2023

作者： Alkhalid, Khalid Wang, Jin Fu, Pengyu Schweickart, Daniel Grosjean, Dennis The Ohio State University Dept. Electrical and Computer Engineering ColumbusOH United States Air Force Research Laboratory/RQQE Wright Patterson AFBOH United States Innovative Scientific Solutions Inc DaytonOH United States

ISBN: (纸本)9798350324099

The higher efficiency of wide-bandgap (WBG) power switching devices makes them a promising candidate for future more electric aircraft (MEA). However, partial discharge (PD) behavior and its impact on the failure of the insulation system under pulse width modulated (PWM) voltages are not yet fully understood. This paper investigates the impact of temperature on PD current pulse and time-to-failure of aircraft power wiring. The experimental setup includes a repetitive high dv/dt unipolar square pulse voltage excitation, with the test sample placed in a vacuum chamber containing dry air to minimize the influence of humidity. The aviation wires are tested at room temperature (22.5°C), 50 ° C, 75 ° C, and 100 ° C. The results show that the wire's temperature has a negligible impact on the partial discharge inception voltage (PDIV) or PD current pulse. However, when the voltage is higher than the PD IV, the failure time decreases significantly at higher temperatures. The findings show that the degradation rate due to PD accelerates exponentially at higher temperatures, even if the temperature is below the rated temperature of the wire. Furthermore, the results show the limitations of PD measurements as a stand-alone evaluation parameter for insulation status, and additional measurements such as temperature could enhance the evaluation accuracy. © 2023 IEEE.

关键词： Degradation

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Model Predictive Planning: Trajectory Planning in Obstruction-Dense Environments for Low-Agility Aircraft

Model Predictive Planning: Trajectory Planning in Obstructio...

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IEEE Conference on Decision and Control

作者： Matthew T. Wallace Brett Streetman Laurent Lessard dept. of Electrical and Computer Engineering Northeastern University Boston MA USA Charles Stark Draper Laboratory Cambridge MA USA dept. of Mechanical and Industrial Engineering Northeastern University Boston MA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We present Model Predictive Planning (MPP), a trajectory planner for low-agility vehicles such as a fixed-wing aircraft to navigate obstacle-laden environments. MPP consists of (1) a multi-path planning procedure that identifies candidate paths, (2) a raytracing procedure that generates linear constraints around these paths to enforce obstacle avoidance, and (3) a convex quadratic program that finds a feasible trajectory within these constraints if one exists. Low-agility aircraft cannot track arbitrary paths, so refining a given path into a trajectory that respects the vehicle’s limited maneuverability and avoids obstacles often leads to an infeasible optimization problem. The critical feature of MPP is that it efficiently considers multiple candidate paths during the refinement process, thereby greatly increasing the chance of finding a feasible and trackable trajectory. We demonstrate the effectiveness of MPP on a longitudinal aircraft model.

关键词： Trajectory planning Atmospheric modeling Refining Predictive models Aircraft navigation Trajectory Planning Aircraft Nonlinear systems Optimization

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Improving Grid Resilience and Reliability Through Dynamic Line Rating During Ice Precipitating Conditions

Improving Grid Resilience and Reliability Through Dynamic Li...

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Grid Edge Technologies Conference & Exposition (Grid Edge), IEEE PES

作者： Sarangan Rajendrants Amirthagunaraj Yogarathnarrus Tianqiao Zhao Satoshi Endo Visvakumar Aravinthan Meng Yuee Interdisciplinary Science Dept. Brookhaven National Laboratory Upton NY USA Electrical and Computer Engineering Wichita State University Wichita KS USA Environmental and Climate Sciences Dept. Brookhaven National Laboratory Upton NY USA

ISBN: (数字)9798350352528

ISBN: (纸本)9798350352535

Ahstract-A significant aspect of global decarbonization efforts hinges on expanding electric transmission capacity to accommodate renewable energy sources. Within this context, dynamic line rating (DLR) systems for overhead lines present an opportunity to enhance the flexibility of power grids while utilizing existing infrastructure. Specifically, during ice-precipitating conditions, the combined effect of lower ambient temperatures and ice buildup on the conductor aids in maintaining a lower conductor temperature, allowing for increased current flow. This research introduces an innovative approach for estimating line ratings during icing conditions. Unlike previous methods, our approach accurately accounts for temperature variations and incorporates the clouding effect during precipitation events. By addressing these variables, we seek to enhance current methodologies for estimating line ratings in adverse weather conditions. To validate the efficacy of our proposed method, we conduct experiments using real-world weather data and line current measurements under both precipitating and non-precipitating conditions. Through these experiments, we illustrate the effectiveness of our approach in accurately estimating line ratings during icing events, thus improving the reliability and efficiency of power line operations during adverse weather conditions.

关键词： Temperature measurement Renewable energy sources Precipitation Power system dynamics Conductors Low carbon economy Ice Power grids Reliability Resilience

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Robust Scheduling of Microgrids Considering Unintentional Islanding Conditions

Robust Scheduling of Microgrids Considering Unintentional Is...

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2022 IEEE Power and Energy Society General Meeting, PESGM 2022

作者： Liu, Guodong Ferrari, Max Ollis, Ben Sundararajan, Aditya Tomsovic, Kevin Oak Ridge National Laboratory Grid Component & Controls Group Oak Ridge United States The University of Tennessee Dept. of Electrical Engineering and Computer Science Knoxville United States

ISBN: (数字)9781665408233

ISBN: (纸本)9781665408233

This paper proposes a robust scheduling model for microgrids considering the stochastic unintentional islanding conditions. The proposed model minimizes the total operating cost of the microgrid by efficiently coordinating the supply of power from local distributed energy resources and the main grid. To capture the prevailing uncertainties in renewable generation and demand as well as unintentional islanding conditions, a two-stage adaptive robust optimization model is formulated to minimize the total operating cost under the worst realization of the modeled uncertainties. The column and constraint generation (C&CG) method is used to solve the problem in an iterative manner. The solution of the proposed scheduling model ensures robust microgrid operation in consideration of all possible realization of renewable generation, demand and unintentional islanding condition. Numerical simulations on a microgrid consisting of a wind turbine, a PV panel, a fuel cell, two micro-turbines, a diesel generator and a battery demonstrate the effectiveness of the proposed approach. © 2022 IEEE.

关键词： Scheduling

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Robotic Teleoperation Control Using PSO-Tuned Fractional Order Active Disturbance Rejection

Robotic Teleoperation Control Using PSO-Tuned Fractional Ord...

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IEEE SSD International Multi-Conference on Systems, Signals and Devices

作者： Omar Gad R. Fareh J. Ghommam M. Nasir S. Khadraoui M. Bettayeb Dept. of Electrical Engineering University of Sharjah UAE Research Inst. of Sciences and Engineering University of Sharjah UAE Dept. of Electrical and Computer Engineering Sultan Qaboos University Oman Control and Energy Management Laboratory (CEM Lab) Ecole National d'Ingenieur de Sfax Tunisia

ISBN: (数字)9798331542726

ISBN: (纸本)9798331542733

Bilateral teleoperation systems have become essential for enabling precise remote manipulation in fields such as robotic surgery and hazardous material handling. However, these systems face significant challenges, including time delays, environmental uncertainties, and nonlinear dynamics, which can affect their stability and performance. This research aims to investigate the application of Active Disturbance Rejection Control (ADRC) to address these challenges. ADRC is chosen for its real-time estimation and compensation for disturbances, even with limited knowledge of the system model. Additionally, the study incorporates enhancements such as Fractional-Order control and optimization techniques, including Particle Swarm Optimization (PSO), for parameter tuning. A simulation study is conducted to assess the performance of the proposed controller in tracking control of bilateral teleoperation systems with 3-DOF and 4-DOF manipulators. Results illustrate the superiority of the fractional order ADRC in disturbance rejection and trajectory tracking compared to linear ADRC, making it a promising solution for teleoperation applications.

关键词： Robust control Uncertainty Trajectory tracking Simulation 3-DOF Surgery Manipulators Stability analysis Particle swarm optimization Tuning

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A New Image Based Regression CNN for Predicting V-band Power Attenuation

A New Image Based Regression CNN for Predicting V-band Power...

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2022 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2022

作者： Gesner, Ralph L. Christodoulou, Christos G. Lane, Steven A. The University of New Mexico The Dept. of Electrical & Computer Engineering AlbuquerqueNM United States Kirtland AFB Airforce Research Laboratory Space Vehicles AlbuquerqueNM United States

ISBN: (数字)9781665496582

ISBN: (纸本)9781665496582

A novel application of machine learning and image processing for estimating mm-wave attenuation due to varying weather conditions is developed and tested. A deep learning approach is taken to analyze doppler weather radar images obtained from the NEXRAD radar system. Pairing the radar images with RF power data obtained from a 24 km, 72 GHz terrestrial link, the received power is estimated through a Convolutional Neural Network, tuned for regression, and the attenuating effects of clear skies and cloudy days are compared to experimentally measured results. © 2022 IEEE.

关键词： Millimeter waves

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Data Generation for Photovoltaic Systems: Enhancing Accuracy with the Huld Model

Data Generation for Photovoltaic Systems: Enhancing Accuracy...

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IEEE International Conference on Big Data

作者： Ali Aghazadeh Ardebili Andreas Livera George E. Georghiou Antonella Longo Antonio Ficarella Dept. of Research and Development HSPI Rome Dept. of Engineering for Innovation CRISR University of Salento Lecce Italy PV Technology Laboratory FOSS Research Centre for Sustainable Energy Dept. of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Gaps in current numerical models highlight challenges in accurately simulating photovoltaic (PV) systems, particularly their sensitivity to weather forecast outputs and the impact of environmental factors such as wind speed and humidity. These factors introduce discrepancies between simulated and real-life PV system performance. This study investigates the potential of open-source PV physical models, leveraging pvlib-python, to address these challenges. The research focuses on enhancing the accuracy of these models to generate synthetic data that closely replicate real-world conditions. Using the single diode equation implemented in pvlib-python, DC power production data from a real-life PV module is simulated, with initial discrepancies identified and mitigated through the integration of Huld model. This integration aims to improve the accuracy of synthetic data, thereby bridging the gap between simulated and actual performance metrics. The efficacy of the enhanced predictive models is evaluated using historical field measurements from a PV system testbed, demonstrating advancements in simulating PV system behaviors with enhanced accuracy and reliability. The results unveil a significant enhancement in synthetic data generation using PVLib and Huld model.

关键词： Photovoltaic systems Accuracy Sensitivity Wind speed System performance Data models Mathematical models Numerical models Wind forecasting Synthetic data

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Computational Modeling Towards Focused Ultrasound Therapy for Spinal Cord Injury: Visualization of Beam Propagation through Patient-Specific Anatomy

Computational Modeling Towards Focused Ultrasound Therapy fo...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Kumar, Avisha Tsehay, Yohannes Gonzalez, Eduardo Kerensky, Max J. Lediju Bell, Muyinatu A. Theodore, Nicholas Thakor, Nitish V. Manbachi, Amir Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Johns Hopkins HEPIUS Innovation Laboratory BaltimoreMD21218 United States Dept. of Neurosurgery School of Medicine Johns Hopkins University BaltimoreMD21218 United States Dept. of Biomedical Engineering School of Medicine Johns Hopkins University BaltimoreMD21218 United States Dept. of Computer Science Johns Hopkins University BaltimoreMD21218 United States Dept. of Mechanical Engineering Johns Hopkins University BaltimoreMD21218 United States Dept. of Anesthesiology and Critical Care Johns Hopkins University School of Medicine Johns Hopkins University BaltimoreMD21218 United States

ISBN: (纸本)9781510660373

Ultrasound holds promise for use in spinal cord injury cases for both diagnostic and therapeutic purposes. Focused ultrasound applications demand an added threshold of study to ensure the safety and efficacy of the therapy. For optimal treatment outcomes, it is crucial to understand whether relevant structures are being targeting with sufficient energy without damaging neighboring tissue and vasculature. However, it is difficult to predict the expected displacement and pressure profile of the ultrasound wavefront due to challenges with visualizing an acoustic beam in real-time and complex patient-specific anatomy. This challenge is particularly prominent in anatomies with varying medium acoustic properties that cause reflection and distortion of the signal, which is inherent to the composition of the spinal cord and is exacerbated by the formations of injury-induced hematomas. Incorrect placement of focused ultrasound transducers can be detrimental to patient health, specifically if therapeutic ultrasound is used at higher intensities, as the beam propagation can target healthy tissue and important structures that could lead to tissue damage and death. We study how computational tools can be leveraged to aid placement of the transducer using an ultrasound simulation software, Wave 3000 Plus, that allows for the visualization of ultrasound propagation through anatomical structures. By simulating the propagation of ultrasound beams through patient-specific Digital Imaging and Communications in Medicine (DICOM) images, we study computational approaches to determine the optimal placement of devices. In this study, we use in vivo porcine spinal cord images following spinal cord injury (as an example medical use case) to determine if the injury site is being targeted appropriately and to visualize the distribution of pressure throughout the simulation. We demonstrate that Wave 3000 Plus is a viable approach for visualizing ultrasound propagation through patient-specific a

关键词： Visualization

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Design and Construction of an Arbitrary Pulse Compressive Amplifier

Design and Construction of an Arbitrary Pulse Compressive Am...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Cody Goins Aaron Harmon Victor Khilkevich Daryl Beetner Dept. of Electrical and Computer Engineering - Electromagnetic Compatibility Laboratory Missouri University of Science and Technology Rolla MO USA

Compressive pulse amplifiers are a class of amplifiers that convert long low amplitude signals into very broadband pulses of high amplitude, yielding a very high instantaneous peak power output pulse. However, in the realm of electronic immunity and susceptibility testing, very broadband short pulses are not always desired. This work presents a design for a compressive amplifier that is aimed at creating arbitrary pulsed signals of varying bandwidths. Limitations of the achievable gain and methods used are discussed.

关键词：

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FASTER AND ACCURATE NEURAL NETWORKS WITH SEMANTIC INFERENCE

arXiv

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arXiv 2023年

作者： Sayyed, Sazzad Ashdown, Jonathan Restuccia, Francesco Dept. of Electrical and Computer Engineering Northeastern University United States Air Force Research Laboratory United States

Deep neural networks (DNNs) usually come with a significant computational and data labeling burden. While approaches such as structured pruning and mobile-specific DNNs have been proposed, they incur in drastic accuracy loss. Conversely from prior work, in this paper we leverage the intrinsic redundancy in latent representations to drastically reduce the computational load with very limited loss in performance. Specifically, we show that semantically similar inputs share a significant number of filter activations, especially in the earlier layers. As such, semantically similar classes can be "clustered" so as to create cluster-specific subgraphs. These may be "turned on" when an input belonging to a semantic cluster is being presented to the DNN, while the rest of the DNN can be "turned off". To this end, we propose a new framework called Semantic Inference (SINF). In short, SINF (i) identifies the semantic cluster the object belongs to using a small additional classifier;and then (ii) executes the subgraph extracted from the base DNN related to that semantic cluster to perform the inference. To extract each cluster-specific subgraph, we propose a new approach named Discriminative Capability Score (DCS) that effectively finds the subgraph with the capability to discriminate among the members of a specific semantic cluster. Importantly, DCS is independent from SINF, as it is a general-purpose quantity that can be applied to any DNN. We benchmark the performance of DCS on the VGG16, VGG19, and ResNet50 DNNs trained on the CIFAR100 dataset against 6 state-of-the-art pruning approaches. Our results show that (i) SINF reduces the inference time of VGG19, VGG16, and ResNet50 respectively by up to 35%, 29% and 15% with only 0.17%, 3.75%, and 6.75% accuracy loss;(ii) DCS achieves respectively up to 3.65%, 4.25%, and 2.36% better accuracy with VGG16, VGG19, and ResNet50 with respect to existing discriminative scores;(iii) when used as a pruning criterion, DCS achieves up to

关键词： Deep neural networks

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