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PCC Voltage Stabilization using an Autonomous Volt-VAR Curve Change in Smart Inverters

Transactions of the Korean institute of electrical Engineers 2022年第4期71卷 625-631页

作者： Cha, Jee-Yoon Kim, Hyeonjin Maeng, Changyeop Lee, Kyo-Beum Dept. of Electrical Computer Engineering Ajou University Korea Republic of Research Institute Korea Republic of

This paper proposes a method of stabilizing a point of common coupling (PCC) voltage using an autonomous Volt-VAR curve change in smart inverters. When the interconnection capacity of distributed energy resources rapidly increases, the problem of a reverse power flow occurs and the PCC voltage becomes unstable. The smart inverter which is a grid-connected inverter for distributed energy resources performs Volt-VAR control in oder to stabilize the unstable PCC voltage. The smart inverter autonomously changes the Volt-VAR curve according to the PCC voltage. The smart inverter uses Volt-VAR curve with a larger slope if the PCC voltage is not stabilized when the Volt-VAR curve with a small slop is used. The Volt-VAR curve is maintained if the PCC voltage is stabilized using the Volt-VAR curve. The validity of the proposed method is verified by the simulation results and experimental results. Copyright ©The Korean institute of electrical Engineers

关键词： Electric load flow

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Frame-level Pain Intensity Assessment via Multilevel Hash-based Features and Transformer

Frame-level Pain Intensity Assessment via Multilevel Hash-ba...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Kong, Xuelin Wang, Bo Guo, Yongxin National University of Singapore Dept. Electrical and Computer Engineering Singapore Research Institute Singapore City University of Hong Kong Dept. Electronic Engineering Hong Kong

ISBN: (纸本)9798350386226

Facial-expression-based pain assessment represents a promising computer vision application in medical diagnostics. However, improving the generalization capability of pain recognition models remains challenging due to the susceptibility of pain-related features to individual differences. The lack of large-scale pain-related datasets hinders the conventional approach relying on data diversity to enhance model generalization. Recent methods to solve this problem include feature descriptors for video classification and frame-level similarity calculation. The former captures better emotion-related features effectively, while the latter shows better generalization. We propose a two-stage framework combining the two strategies to address reference data limitations and ensure robust frame-level recognition. The initial stage introduces a simple but high-efficiency feature descriptor for emotion detail capture, which outputs a reference frame and an initial pain level score. In the second stage, we add a hash layer into the transformer and embed the transformer into the Siamese network to get the final pain scores by comparing the test frames with reference images obtained in the first stage. Experimental evaluations on UNBC McMaster and BioVid Heat Pain databases showcase our approach's state-of-the-art accuracy, robust generalization, and faster processing speed. © 2024 IEEE.

关键词： Diagnosis

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Convoluted Frequency-Selective Surface with Reconfigurable Response

Convoluted Frequency-Selective Surface with Reconfigurable R...

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2024 IEEE Pacific Rim Conference on Communications, computers and Signal Processing, PACRIM 2024

作者： De Sousa, Thayuan Rolim Mamedes, Deisy Formiga Da Silva Neto, Valdemir Praxedes Neto, Alfredo Gomes Federal University of Rio Grande do Norte Dept. of Electrical and Computer Engineering Natal Brazil Herzberg Astronomy and Astrophysics Research Centre National Research Council Victoria Canada Federal Institute of Paraiba Dept. of Electrical and Computer Engineering João Pessoa Brazil

ISBN: (纸本)9798350362312

This paper presents the development of an RFSS based on slotted square with convoluted arms geometry, for advanced applications. The RFSS presents single- and dual-band response for the TM and TE polarization, respectively. The proposed RFSS uses two PIN diodes per unit cell to reconfigure its frequency response in one polarization. Three bias voltages were applied to the PIN diodes;reverse, threshold and forward bias. In the TM polarization, the diodes state switching does not change the RFSS response. For the TE polarization, the structure presents dual-stopband response, and the total impedance of the FSS changes when applying different voltage levels to the PIN diodes, hence shifts its resonance frequencies. Simulated and measured results show to have a good agreement. © 2024 IEEE.

关键词： Polarization

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Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchronous Machines

Novel PM-Assisted Model of the Two-Layer Sub-Harmonic Synchr...

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2023 International Applied Computational Electromagnetics Society Symposium, ACES-Monterey 2023

作者： Sajjad Hossain Rafin, S.M. Ali, Qasim Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Dept. of Electrical and Computer Engineering MiamiFL United States Sukkur Iba University Dept. of Electrical Engineering Sukkur Pakistan

ISBN: (纸本)9781733509633

In this paper, a new pm-assisted model has been developed for the already introduced two-layer sub-harmonic synchronous machine. This work aims to increase the torque-producing capability of the brushless wound rotor machines while keeping the use of rare earth magnets to a minimum. A 2D finite element analysis has been performed to validate the proposed model and compare the performance with the reference model. The results in the paper demonstrate that the proposed machine's average torque has increased. © 2023 ACES.

关键词： Rare earths

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Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybrid Edge-Cloud Computing

Energy-Optimized Offloading of Delay-Sensitive Tasks in Hybr...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Yuan, Haitao Wang, Shen Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia Zhou, MengChu School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

Currently, a cloud-edge collaborative system combines almost unlimited storage and computing resources where tasks can be migrated to high-performance servers in edge servers or the cloud. However, resource allocation and task offloading present big challenges due to the competition among mobile devices (MDs) for communication and computing resources of edge servers. Therefore, it is significant to properly offload MDs' tasks to edge servers or the cloud. This work proposes a collaborative edge-cloud architecture, including a centralized cloud, edge servers, and MDs. Then, this work jointly considers computing power, task sizes, computing resources, transmission power of MDs, transmission rates, computing power, transmission power, computing resource of edge servers, and computing resource of the cloud. Considering the abovementioned factors, this work designs a mixed-integer non-linear programming problem. To solve it, a Genetic Simulated annealing-based Particle Swarm Optimization (GSPSO) algorithm is proposed to obtain the best solution. Building upon it, this work proposes an energy-minimized task offloading and resource allocation strategy, thereby minimizing the system's energy consumption while ensuring strict task response time limits. Experimental results show that GSPSO reduces the system's energy by 66.34%, 34.65%, and 4.95% more than particle swarm optimization (PSO), self-adaptive PSO, and Tyrannosaurus optimization. © 2024 IEEE.

关键词： Mobile edge computing

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Energy-Efficient and Latency-Optimized Computation Offloading with Improved MOEA for Industrial Internet of Things

Energy-Efficient and Latency-Optimized Computation Offloadin...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Yang, Jinhong Zhang, Jia Zhou, MengChu College of Computer Beijing University of Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States New Jersey Institute of Technology Dept. of Electrical and Computer Engineering NewarkNJ07102 United States

ISBN: (纸本)9781665410205

The unprecedented prosperity of the industrial Internet of Things has thoroughly facilitated the transition from traditional manufacturing towards intelligent manufacturing. In industrial environments, resource-constrained industrial equipments (IEs) often fail to meet the diverse demands of numerous compute-intensive and latency-sensitive tasks. Mobile edge computing has emerged as an innovative paradigm for lower latency and energy consumption for IEs. However, computational offloading and coordinating of multiple IEs with diverse task types and multiple edge nodes in industrial environments poses challenges. To address this challenge, we propose a multi-task approach encompassing scientific and concurrent workflow tasks to achieve energy-efficient and latency-optimized computation offloading. Furthermore, this work designs an improved Quantum Multi-objective Grey wolf optimizer with Manta ray foraging and Associative learning (QMGMA) to optimize multi-task computation offloading. Comprehensive experiments demonstrate the superior efficiency and stability of QMAGA compared to state-of-the-art algorithms in balancing latency and energy consumption. QMAGA improves average inverse generation distance and average spacing by 37% and 31% on average than multi-objective grey wolf optimizer, non-dominated sorting genetic algorithm II, and multi-objective multi-verse optimization, proving the convergence and diversity of its non-dominated solutions. © 2024 IEEE.

关键词： Smart manufacturing

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An Introduction to Power Electronics Design Methodology

An Introduction to Power Electronics Design Methodology

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2023 IEEE Design Methodologies Conference, DMC 2023

作者： Rafin, Sm Sajjad Hossain Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Dept. of Electrical and Computer Engineering MiamiFL United States

ISBN: (纸本)9798350315547

The development of effective and dependable electronic systems for a variety of applications, from renewable energy systems and electric vehicles to industrial automation and consumer electronics, depends heavily on power electronics design techniques. Moreover, the power electronics design technique offers a methodical way to create high-performance, dependable, and efficient electronic systems, which helps scholars and engineers address the unique requirements of varied applications while taking thermal management, electromagnetic interference, and safety considerations by integrating theoretical analysis, simulation tools, prototyping, and testing. The effectiveness of this methodology is further increased by the ongoing development of power semiconductor and design methodologies, which makes it possible to realize cutting-edge and environmentally friendly power electronic solutions for the modern world. Considering above mentioned importance, this paper is written as an initial guideline for scholars and engineers to design power electronic systems. © 2023 IEEE.

关键词： Design

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Decentralized AI-based Fault Detection and Localization to Enhance Dynamic Response of Grid-Forming Inverters 50

Decentralized AI-based Fault Detection and Localization to E...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Behnam, Reza Gohari, Amirhosein Shadmand, Mohammad B. Bayhan, Sertac Abu-Rub, Haitham Dept. of Electrical & Computer Engineering University of Illinois Chicago IL United States Qatar Environment and Energy Research Institute Hamad Bin Khalifa University Doha Qatar Dept. of Electrical & Computer Engineering Texas A&M University at Qatar Doha Qatar

ISBN: (纸本)9781665464543

Grid-forming inverters (GFMIs) are promising solutions for voltage and frequency support in upcoming power electronics-dominated grids (PEDG). However, current state-of-the-art decentralized control schemes for GFMIs are designed for operation under normal conditions. These decentralized control schemes could result in an adverse dynamic response if a cluster of GFMIs network disconnects from the rest of the grid due to a fault. The dynamic response of GFMIs could be improved with coordinated control schemes which require communication and make the system more complex. This paper proposes a decentralized Artificial Intelligence (AI)-based method for online fault detection and localization to enhance the dynamic response of GFMIs without using a communication layer. Each GFMI will detect and localize the line tripping based on its output active and reactive power measurement. Then, according to the topology of the grid after line tripping, each GFMI updates its nominal power and inertia to suppress the frequency transient caused by the line tripping. The inertia and damping factor of the GFMIs are then recalculated to ensure optimal operation of the network of inverters after line tripping. Several case studies are presented to validate the effectiveness of the proposed method in the timely detection, localization, and mitigation of adverse frequency transient after line tripping in a communication-less manner. © 2024 IEEE.

关键词： Synchronous generators

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Movement-Based Interaction: An Approach to Visual and Auditory Stimulus and Memory 6

Movement-Based Interaction: An Approach to Visual and Audito...

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6th Experiment at International Conference, exp.at 2023

作者： Nogueira, Maria Rita Menezes, Paulo Institute of Systems and Robotics University of Coimbra Coimbra Portugal University of Coimbra Dept. of Electrical and Computer Engineering Coimbra Portugal

ISBN: (纸本)9798350314403

Movement-based interaction design is an innovative field that leverages the body's natural movements to create intuitive and engaging interfaces. This approach takes a sensorial approach to visual and auditory memory, seeking to enhance user engagement and create more meaningful experiences. The use of movement-based interfaces has significant implications for senior citizens, who may have mobility limitations or cognitive impairments. Using movement-based interfaces, seniors can engage in physical activity and maintain cognitive function, essential aspects for healthy aging. Movement-based interfaces can also help children develop physical coordination, cognitive skills, and social interaction. Furthermore, movement-based interaction design can benefit everyone's physical well-being by promoting physical activity and reducing sedentary behavior. Studies have demonstrated that movement-based interfaces can enhance posture, physical activity levels, and general health. By combining movement with sensory experiences, movement-based interaction design can create a more engaging and enjoyable experience for users, which can motivate them to maintain physical and cognitive health. In this article, we present two case studies that research the effectiveness of movement-based interaction design in enhancing visual and auditory memory. Overall, the results of these case studies suggest that movement-based interaction design is an effective approach to enhancing visual and auditory memory. The introduction of movement-based interfaces can give participants a more enjoyable and memorable experience, improving user engagement and information retention. © 2023 IEEE.

关键词： Machine learning

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Deep Learning Approaches for Stress Detection: A Survey

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IEEE Transactions on Affective Computing 2024年第2期16卷 499-517页

作者： Kyrou, Maria Kompatsiaris, IoannisSenior Petrantonakis, Panagiotis C. Aristotle University of Thessaloniki Artificial Intelligence and Information Analysis Laboratory Dept. of Informatics GR 54124 Greece Information Technologies Institute Centre for Research and Technology Hellas GR 57001 Greece Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering GR 54124 Greece

Stress has a severe impact on individuals irrespective of age, sex, work, or background. The reliable development of stress detection techniques enhances the social, educational, physical, economic, and professional quality of life, preventing chronic stress and proposing alleviation strategies. research studies examine psychological, cognitive, behavioral, and physiological reactions to identify stress adequately. Deep Learning (DL) has received significant attention in recent years as it deals with high-dimensional, heterogeneous data and automatically learns representative features. This paper presents a survey on stress detection with recent DL approaches, leveraging data from all possible sources (physiological, speech, facial expressions, gestures, and social media content). The methodological outlines, the best results, and the main contributions of each study are discussed. We also describe publicly available datasets used by several of the presented works. Finally, we emphasize various open issues within the field of research and highlight key directions for future work © 2024 IEEE.

关键词： Social psychology

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