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IEEE Innovative Smart Grid Technologies - Asia (ISGT Asia)

作者： Manorath Prasad Zakir Hussain Rather Reza Razzaghi Suryanarayana Doolla Department of Energy Science and Engineering Indian Institute of Technology Bombay Mumbai India Department of Electrical and Computer Systems Engineering Monash University Melbourne Australia

The advent of distribution-level phasor measurement units (D-PMUs) has enhanced the observability of active distribution networks (ADNs) and improved their energy management and control. Recently, various ADN control strategies that require detailed network parameter information have been proposed based on the voltage-to-power sensitivities of the ADN nodes. This paper proposes a new realistic approach to determining nodal voltage-to-power sensitivities of a synchrophasor measurement-based reduced ADN model. The real-time synchrophasor data dynamically updates the parameters of the reduced equivalent model to follow the time-varying operating conditions of the ADN accurately. The voltage-to-power sensitivities of the retained observable nodes are derived from the power flow Jacobian matrix using reduced equivalent model parameters. Thus, the proposed approach is agnostic about detailed line impedance data of the complex ADN. Further, the computational complexity is significantly decreased with the reduced number of nodes in the equivalent ADN model. The proposed approach has been tested on modified IEEE-33 bus benchmark system with high penetration of photovoltaic inverters using a DIgSILENT PowerFactory MATLAB co-simulation platform.

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A Target-based Calibration Method for LiDAR-Visual-Thermal Multi-Sensor System

A Target-based Calibration Method for LiDAR-Visual-Thermal M...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Yuan, Songyu Xie, Tian Zhu, Shiqiang Chen, Yeheng Li, Yuehua Zheng, Tao Gu, Jason Research Center for Intelligent Robotics Research Institute of Interdis-ciplinary Innovation Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9781665481090

As the basis of multi-sensor fusion, accurate extrinsic calibration among multi-sensors is vital for hetero-geneous information fusion. However, most existing methods only focus on the calibration between two specific heteroge-neous sensors, such as camera-LiDAR, LiDAR-thermal, and etc. which may cause inevitable accumulation when applied to a multi-sensor system. To address this problem, a novel calibration target and a high-accuracy method are proposed to simultaneously calibrate the intrinsic and extrinsic of a 3D LiDAR, a thermal camera and a visible camera without any user intervention. The proposed method only relies on one calibration board, to calibrate all the intrinsic and extrinsic parameters in one-step, without any strict pose requirement or long-time optimization. Furthermore, 2D-3D corresponding features can be extracted with higher precision by considering sensor model, comparing to the common feature extraction methods. Experiments with Velodyne VLP-16 LiDAR, ZED camera and D843NT thermal camera demonstrate that the proposed method can complete calibration among three of them automatically. Finally, this paper also presents a target-based method of uniformly expressing the calibration errors among multi-sensor system, with a competitive performance against most state-of-the-art methods. © 2022 IEEE.

关键词： Calibration

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Guest Editorial: Special issue on computational methods and artificial intelligence applications in low-carbon energy systems

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IET Renewable Power Generation 2024年第3期18卷 303-305页

作者： Wang, Yishen Zhou, Fei Guerrero, Josep M. Baker, Kyri Chen, Yize Wang, Hao Xu, Bolun Xu, Qianwen Zhu, Hong Agwan, Utkarsha State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd. Beijing China Department of Energy Technology Aalborg University Aalborg Denmark Department of Civil Environmental and Architectural Engineering University of Colorado-Boulder BoulderCO United States Guangzhou China Department of Data Science and AI Monash University ClaytonVIC Australia Department of Earth and Environmental Engineering Columbia University New YorkNY United States Electric Power and Energy Systems Division KTH Royal Institute of Technology Stockholm Sweden State Grid Jiangsu Electric Power Co. Ltd. Nanjing China Department of Electrical Engineering and Computer Sciences University of California - Berkeley BerkeleyCA United States

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An Optimized Ensemble Model for Prediction the Bandwidth of Metamaterial Antenna

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computers, Materials & Continua 2022年第4期71卷 199-213页

作者： Abdelhameed Ibrahim Hattan F.Abutarboush Ali Wagdy Mohamed Mohamad Fouad El-Sayed M.El-kenawy Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyEgypt

Metamaterial Antenna is a special class of antennas that uses metamaterial to enhance their *** size affects the quality factor and the radiation loss of the *** antennas can overcome the limitation of bandwidth for small *** learning(ML)model is recently applied to predict antenna *** can be used as an alternative approach to the trial-and-error process of finding proper parameters of the simulated *** accuracy of the prediction depends mainly on the selected *** models combine two or more base models to produce a better-enhanced *** this paper,a weighted average ensemble model is proposed to predict the bandwidth of the Metamaterial *** base models are used namely:Multilayer Perceptron(MLP)and Support Vector Machines(SVM).To calculate the weights for each model,an optimization algorithm is used to find the optimal weights of the *** Group-Based Cooperative Optimizer(DGCO)is employed to search for optimal weight for the base *** proposed model is compared with three based models and the average ensemble *** results show that the proposed model is better than other models and can predict antenna bandwidth efficiently.

关键词： Metamaterial antenna machine learning ensemble model multilayer perceptron support vector machines

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IEEE CAS Society Turns 75—Commemorative Events and Celebrations [Special Section on the 75th Anniversary of the IEEE Circuits and systems Society]

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IEEE Circuits and systems Magazine 2024年第4期24卷 17-21页

作者： Yongfu Li Fakhrul Zaman Rokhani Hanho Lee Kea-Tiong Tang Jie Chen Mohd Hafiz Ismail Yoko Uwate Molly Brackin Amara Amara Myung Hoon Sunwoo Department of Micro and Nano Engineering Shanghai Jiao Tong University Shanghai China Department of Computer and Communication Systems Engineering Universiti Putra Malaysia Serdang Malaysia Department of Electrical and Computer Engineering Inha University Incheon South Korea Department of Electrical Engineering National Tsing Hua University Hsinchu Taiwan Institute of Biomedical Engineering and Technology Fudan University Shanghai China Faculty of Electronic Engineering Technology Universiti Malaysia Perlis Arau Malaysia Department of Electrical and Electronic Engineering Tokushima University Tokushima Japan Conference Catalysts LLC Gainesville FL USA Hangzhou International Innovation Institute Beihang University Beijing China Department of Electrical and Computer Engineering Ajou University Suwon South Korea

The IEEE Circuits and systems (CAS) Society celebrates its 75th anniversary in 2024, showcasing its evolution from a Circuit Theory Group to a leading global technological force. This year, the Society introduces a commemorative logo that encapsulates its innovative spirit and a Promotion Toolkit to amplify the celebratory atmosphere across all chapters. The 75th CAS Society’s Global Celebration is set to engage members worldwide, with a virtual event on May 21st, 2024, featuring photo and video contests highlighting member creativity and experiences. The pinnacle of the celebration was at IEEE ISCAS 2024, where the 2nd Edition of “A Short History of Circuits and systems” was launched, and contributions were recognized during the IEEE CAS Society Award ceremony. The Society, with 15,236 members and a strong global presence, continues to foster innovation and collaboration through its conferences, publications, and community programs, reflecting its commitment to advancing technology for the betterment of humanity.

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A data acquisition system to capture extreme human driving behaviour

A data acquisition system to capture extreme human driving b...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： Ines Rito Lima José Santos-Victor Department of Electrical and Computer Engineering Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal Institute for Systems and Robotics (ISR)) Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Assessing and encoding human driving expertise while handling challenging events, primarily during drifts, can unveil new insights regarding the professional response to extreme driving which can be integrated into Autonomous Vehicles (AVs) safety procedures. Considering the complex and dynamic data related to both human behaviour and car devices, this research area urges the inclusion of Artificial Intelligence (AI) strategies for the extraction of interdependencies and patterns that encode human driving expertise. To do so, acquiring data that comprises the pilot and vehicle performances is of utmost importance. The present work proposes a specially designed data acquisition system to collect data regarding the vehicle dynamic movement and the associated human behaviour while driving in challenging environments. Distinct sensing devices are placed across the vehicle to retrieve the vehicle position, acceleration, angular rotation, steering orientation, among others. Data was collected from professional drivers during official racing events, and initial processing results show spatial and temporal coherence with the racing performances. The preliminary analysis of the data also reveals potentially extreme events during the race.

关键词： Performance evaluation Data acquisition Robot sensing systems Encoding Sensors Safety Data mining

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Efficient Evaluation of Short-Circuit Faults in Active Distribution Networks

Efficient Evaluation of Short-Circuit Faults in Active Distr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA EPIC and Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Short-circuit problems in unbalanced radial distribution networks have become more common as a result of distributed generation connectivity and smart grid advancements. An accurate and effective strategy for assessing these faults due to short circuits is presented in this study. In contrast to traditional methods, this approach uses relationship matrices that are created expressly to take use of distribution network's radial characteristics. Through the examination of variations in bus voltages, bus current injections, and branch currents during fault conditions, the suggested method formulates the analysis of various fault types. The accuracy of developed method has been validated on IEEE-123 bus modified test network by comparing the obtained results with PSCAD/EMTDC simulation results.

关键词： Accuracy Simulation Distribution networks Drives Power electronics Distributed power generation Smart grids Circuit faults Active distribution networks

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A Generic Load Flow Solution for Droop-Controlled AC Microgrids Considering Electric Vehicle Loads

A Generic Load Flow Solution for Droop-Controlled AC Microgr...

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International Conference on Power Electronic Drives

作者： Ghanshyam Meena Akhilesh Mathur V. P. Singh Krishna Murari Sukumar Kamalasadan Department of Electrical Engineering Malaviya National Institute of Technology Jaipur Rajasthan India Electrical Engineering and Computer Science Department The University of Toledo Toledo Ohio USA Department of Electrical Engineering Power Energy and Intelligent Systems Laboratory EPIC University of North Carolina at Charlotte Charlotte NC USA

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

Microgrids (MGs) have become more unpredictable due to integration of renewable generation sources. Several methods are used to solve load flow problems in MGs. Load flow analysis is a complex problem for islanded MGs due to the absence of slack bus and dependency of active power generation of droop-controlled DGs on frequency. In this paper, a Modified Newton Raphson (MNR) approach is used to perform load flow analysis of isolated MGs considering various types of electric vehicle (EV) loads. The presented method has been validated on 6-bus and 38-bus test systems of the islanded microgrid to give an accurate and simple approach for computing the load flow solutions.

关键词： Renewable energy sources Microgrids Drives Electric vehicles Software Power electronics Distributed power generation Planning MATLAB Load flow analysis

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FAIR-FATE: Fair Federated Learning with Momentum

arXiv

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arXiv 2022年

作者： Salazar, Teresa Fernandes, Miguel Araújo, Helder Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering of The University of Coimbra University of Coimbra Coimbra Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering of The University of Coimbra University of Coimbra Coimbra Portugal

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data *** Codes 68T07 © 2022, CC BY.

关键词： Momentum

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Biphasic Conductive Inks & Organogels for Soft Machines and Bioelectronics

Biphasic Conductive Inks & Organogels for Soft Machines and ...

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IEEE International Conference on Flexible and Printable Sensors and systems (FLEPS)

作者： Carmel Majidi Department of Mechanical Engineering Carnegie Mellon University Pittsburgh Pennsylvania USA Department of Electrical & Computer Engineering (Courtesy) Robotics Institute (Courtesy) Carnegie Mellon University Pittsburgh Pennsylvania USA

Over the past decade, there's been tremendous advancements in soft and highly stretchable circuitry for use in epidermal electronics for health monitoring, wearable computing, and soft robotics. As these technologies continue to improve, there is increasing interest in new material architectures that allow these materials to be printable over large areas or to exhibit robust self-healing properties. Here, I present progress on new classes of highly elastic “biphasic” composites that incorporate gallium indium (EGaIn) liquid metal and silver flakes within a soft polymer matrix. These include printable inks for creating soft, sticker-like, digital circuits and bioelectronic interface using styrene-isoprene-styrene (SIS) thermoplastic elastomer as the polymer binder. I also present recent progress on EGaIn-Ag-organogel composites that exhibit unique combinations of dynamically tunable electrical conductivity and spontaneous mechanical self-healing. These composites can be utilized for applications that range from highly stretchable digital circuits to bioelectronic stickers for electrophysiological monitoring.

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