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Implementation of smart diagnosis program for quantification of Arc flash hazard

Transactions of the Korean Institute of Electrical Engineers 2021年第10期70卷 1535-1539页

作者： Na, Kyung-Min Lee, Kiwon Park, Chulmin Kim, Hyungchul Park, Young Dept. of Electrical System Engineering Hanbat National University Korea Republic of Dept. of Transportation Engineering Korea University of Science and Technology Korea Republic of Smart Electrical and Signaling Division Korea Railroad Research Institute Korea Republic of

Hazardous arc generated by electrical faults are detected for safety accidents in power facilities. Arc detection has a different measurement method depending on the faults. the arc flash from short circuit or overload is analyzed by voltage and current for diagnosis. However, there is a disadvantage in detecting the arc because of sensitivity to noise before it occur. In addition, detection method based on the photosensor may cause an malfunction by the light around environment. The photosensor with 220 nm can prevent malfunction because it not work in sun of the light. The arc size according to the detection speed is shown as the arc duration. This method reduces error according to the detection speed. However, it is necessary to implement a program capable of determining detection and accuracy determination for quantitative measurement. In this paper, we propose a detection method and program with improved detection speed to detect the arc duration of 10 µs. the error of program is compared with the arc duration generated in real time. In the future, we propose that customized application will be possible according to the factors of arc generation in the electric power field. Copyright © The Korean Institute of Electrical Engineers.

关键词： Hazards

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Data-Driven Performance Modeling of Solar Panels Using Polynomial Regression

Data-Driven Performance Modeling of Solar Panels Using Polyn...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Fereshteh Jafari Kamran Moradi Qobad Shafiee Fariba Moghaddam Dept. of Electrical Engineering Smart/Micro Grids Research Center University of Kurdistan Sanandaj Iran Institute of System Engineering University of Applied Sciences Western Switzerland Sion Switzerland

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

Photovoltaic (PV) systems are integral to renewable energy, demanding accurate performance modeling for optimal functionality. This paper presents a pragmatic, data-driven approach employing Polynomial Regression (PR) for solar panel modeling to boost accuracy and adaptability to environmental variables. By emphasizing the advantages of data-driven models in attaining predictive precision, this study aims to reconcile theoretical concepts with practical solar panel performance. PR is employed as a black-box machine learning (ML) algorithm to transcend the limitations of traditional modeling methods, revealing ML's ability to grasp intricate relationships and ensure precise predictions. Utilizing real and simulated datasets encompassing solar irradiance, ambient temperature, and applied load through hardware in the loop (HIL), the model is trained to forecast the electrical outputs of solar panels in two approaches to estimate output voltage and current as well as key points on the panels' I-V curve. Assessment of model accuracy using Root Mean Square Error (RMSE) showcases the PR model’s capacity to accurately depict solar panel performance by capturing non-linear relationships between environmental variable conditions and electrical outputs. Experimentation validates the method's effectiveness, focusing on accuracy, generalization to testing, and on-site data validation in two approaches. This research endeavors to bridge the gap between theory and practice, propelling advancements in the field of solar PV systems and facilitating more efficient solar energy utilization.

关键词： Training Adaptation models Renewable energy sources Accuracy Machine learning Predictive models Polynomials

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Data-Driven Modeling of Photovoltaic Panels Using Neural Networks

Data-Driven Modeling of Photovoltaic Panels Using Neural Net...

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International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Fereshteh Jafari Kamran Moradi Fariba Moghaddam Qobad Shafiee Institute of System Engineering University of Applied Sciences Western Switzerland Sion Switzerland Dept. of Electrical Engineering Smart/Micro Grids Research Center University of Kurdistan Sanandaj Iran

ISBN: (数字)9798331532833

ISBN: (纸本)9798331532840

Photovoltaic (PV) systems are essential for the shift towards sustainable energy. Accurate performance modeling of these systems is vital for optimizing their efficiency and adapting to changing environmental conditions. Traditional analytical models often struggle with real-world complexities. This paper uses a neural networks (NNs) method to model PV system performance, offering superior accuracy and adaptability. By leveraging NNs, the model adopts a black-box methodology, bypassing the need for detailed knowledge of PV system complexities. The study utilizes historical performance data from two panels located in Sion, Switzerland, and Eugene, Oregon, in the United States. This approach captures the interdependencies between input parameters such as solar irradiance, temperature, and applied load and output variables such as voltage and current. Additionally, it accounts for key PV metrics including open circuit voltage, short circuit current, maximum power point current, and maximum power point voltage. The NN model is trained with a comprehensive dataset featuring high persistent excitation along with real measurements. Performance metrics such as root mean square error demonstrate that the data-driven NN model surpasses traditional methods, offering a more practical solution for PV system performance modeling.

关键词： Photovoltaic systems Measurement Adaptation models Computational modeling system performance Artificial neural networks Predictive models Numerical models Integrated circuit modeling Load modeling

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A comparative study of fatigue life prediction methodologies for electronic PCB under random vibration

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Transactions of the Korean Society of Mechanical Engineers, A 2021年第10期45卷 905-913页

作者： Noh, Hong-Kyun Lim, Jae Hyuk Kwon, Seong-Cheol Jeong, Seong-Keun Park, Tae-Yong Oh, Hyun-Ung Dept. of Mechanical Engineering Jeonbuk Nat'l Univ. Korea Republic of Satellite System Team 1 Hanwha Systems Korea Republic of MID Co. Ltd. Korea Republic of Dept. of Smart Vehicle System Engineering Chosun Univ. Korea Republic of Agency for Defense Development Korea Republic of

Several electronic equipment mounted on high-tech vehicles such as automobiles, aircraft, satellites, and launch vehicles comprise numerous printed circuit boards (PCBs) that are subjected to random vibrations, which are the fatal cause of life-shortening. Accordingly, various fatigue life prediction theories have been proposed, but the results of fatigue life predictions are often different because of different assumptions and approaches used. This study analyzes various fatigue life prediction methods of the PCBs and compares the fatigue life prediction results through numerical examples referring to the test results. © 2021 Korean Society of Mechanical Engineers. All rights reserved.

关键词： Printed circuit boards

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Transforming Leukemia Classification: A Comprehensive Study on Deep Learning Models for Enhanced Diagnostic Accuracy

Transforming Leukemia Classification: A Comprehensive Study ...

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Power, Electrical, and Electronics and Industrial Applications (PEEIACON), IEEE International Conference on

作者： Jawad Hasan Kamrul Hasan Abdullah Al Noman Sayed Hasan Shayma Sultana Masum Alam Arafat Saba Amena Islam Abboy Sarker Shafiur Rahman Md. Redwan Ahmed Dept. of CSE BRAC University Dhaka Bangladesh Dept. of CSE East West University Dhaka Bangladesh Dept. of Information Technology Westcliff University CA USA Dept. of Management Information System International American University CA USA Dept. of Smart Comuting Kyungdong University Gangwon-do Republic of Korea Dept. of Software Engineering Daffodil International University Dhaka Bangladesh

ISBN: (数字)9798331517984

ISBN: (纸本)9798331517991

Leukemia is a severe form of blood cancer that presents significant challenges in both diagnosis and treatment. Early and accurate detection is crucial for successful patient outcomes, but traditional diagnostic methods relying on pathologist expertise can be subjective and time-consuming. This can lead to delays in identifying the appropriate treatment plan, especially given the complexity of accurately categorizing leukemia subtypes. To address these challenges, a study has been conducted to comprehensively evaluate Deep Learning (DL) techniques for leukemia classification. The study compares the performance of conventional Machine Learning (ML) methods with cutting-edge Transfer Learning (TL) models across multiclass scenarios. The study employed Laplacian of Gaussian-based Modified High-boosting (LoGMH) for image enhancement, along with image augmentation techniques such as brightness and rotation adjustments to expand the dataset. Additionally, feature extraction using the gray-level run length matrix (GLRLM) was applied to improve feature representation. Among the models tested, Inception-ResNet emerged as the top performer, achieving an accuracy of 95.52% and an F1 score of 95.49% in distinguishing five leukemia subtypes. This research underscores the potential of TL models in advancing medical diagnostics, particularly in the early detection and precise classification of leukemia, thereby enhancing patient care in hematology and oncology. The future research will focus on integrating additional clinical data, validating models in diverse clinical environments, and emphasizing model transparency and interpretability.

关键词： Deep learning Pathology Accuracy Hematology Transfer learning Leukemia Oncology Feature extraction Data models Medical diagnosis

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Carrying the uncarriable: a deformation-agnostic and human-cooperative framework for unwieldy objects using multiple robots

Carrying the uncarriable: a deformation-agnostic and human-c...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Doganay Sirintuna Idil Ozdamar Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy

This manuscript introduces an object deformability-agnostic framework for co-carrying tasks that are shared between a person and multiple robots. Our approach allows the full control of the co-carrying trajectories by the person while sharing the load with multiple robots depending on the size and the weight of the object. This is achieved by merging the haptic information transferred through the object and the human motion information obtained from a motion capture system. One important advantage of the framework is that no strict internal communication is required between the robots, regardless of the object size and deformation characteristics. We validate the framework with two challenging real-world scenarios: co-transportation of a wooden rigid closet and a bulky box on top of forklift moving straps, with the latter characterizing deformable objects. In order to evaluate the generalizability of the proposed framework, a heterogenous team of two mobile manipulators that consist of an Omni-directional mobile base and a collaborative robotic arm with different DoFs is chosen for the experiments. The qualitative comparison between our controller and the baseline controller (i.e., an admittance controller) during these experiments demonstrated the effectiveness of the proposed framework especially when co-carrying deformable objects. Furthermore, we believe that the performance of our framework during the experiment with the lifting straps offers a promising solution for the co-transportation of bulky and ungraspable objects.

关键词：

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Enhancing Human-robot Collaboration Transportation through Obstacle-Aware Vibrotactile Feedback

arXiv

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arXiv 2023年

作者： Sirintuna, Doganay Ozdamar, Idil Gandarias, Juan M. Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy

Transporting large and heavy objects can benefit from Human-robot Collaboration (HRC), increasing the contribution of robots to our daily tasks and reducing the risk of injuries to the human operator. This approach usually posits the human collaborator as the leader, while the robot has the follower role. Hence, it is essential for the leader to be aware of the environmental situation. However, when transporting a large object, the operator's situational awareness can be compromised as the object may occlude different parts of the environment. This paper proposes a novel haptic-based environmental awareness module for a collaborative transportation framework that informs the human operator about surrounding obstacles. The robot uses two LIDARs to detect the obstacles in the surroundings. The warning module alerts the operator through a haptic belt with four vibrotactile devices that provide feedback about the location and proximity of the obstacles. By enhancing the operator's awareness of the surroundings, the proposed module improves the safety of the human-robot team in co-carrying scenarios by preventing collisions. Experiments with two non-expert subjects in two different situations are conducted. The results show that the human partner can successfully lead the co-transportation system in an unknown environment with hidden obstacles thanks to the haptic feedback. Copyright © 2023, The Authors. All rights reserved.

关键词： robots

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Carrying the uncarriable: a deformation-agnostic and human-cooperative framework for unwieldy objects using multiple robots

arXiv

引用

arXiv 2022年

作者： Sirintuna, Doganay Ozdamar, Idil Ajoudani, Arash Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy

— This manuscript introduces an object deformability-agnostic framework for co-carrying tasks that are shared between a person and multiple robots. Our approach allows the full control of the co-carrying trajectories by the person while sharing the load with multiple robots depending on the size and the weight of the object. This is achieved by merging the haptic information transferred through the object and the human motion information obtained from a motion capture system. One important advantage of the framework is that no strict internal communication is required between the robots, regardless of the object size and deformation characteristics. We validate the framework with two challenging real-world scenarios: co-transportation of a wooden rigid closet and a bulky box on top of forklift moving straps, with the latter characterizing deformable objects. In order to evaluate the generalizability of the proposed framework, a heterogenous team of two mobile manipulators that consist of an Omni-directional mobile base and a collaborative robotic arm with different DoFs is chosen for the experiments. The qualitative comparison between our controller and the baseline controller (i.e., an admittance controller) during these experiments demonstrated the effectiveness of the proposed framework especially when co-carrying deformable objects. Furthermore, we believe that the performance of our framework during the experiment with the lifting straps offers a promising solution for the co-transportation of bulky and ungraspable objects. Copyright © 2022, The Authors. All rights reserved.

关键词： Controllers

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Cellulose-Based Nanocomposite Insulation Material with Excellent Thermal Aging Resistance: KH550-hBN-Modified Cellulose Insulation Paper

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IEEE Transactions on Dielectrics and Electrical Insulation 2025年

作者： Wang, Zuhao Peng, Xiao Chronis, Ioannis Wu, Yingrui Psomopoulos, Constantinos S. Tang, Chao Southwest University College of Engineering and Technology International R & D Center for New Technologies of Smart Grid and Equipment Chongqing400715 China Southwest University College of Engineering and Technology Chongqing400715 China Chongqing University State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing400044 China University of West Attica High Voltage and Energy Systems Research Lab Dept. of Electrical and Electronics Engineering EgaleoGR-122 44 Greece

As the global power industry evolves, transformer insulation systems face increasing challenges. The aging of cellulose insulation paper (cellulose-IP) at elevated temperatures significantly impacts its insulation properties and overall lifespan. To enhance the thermal aging resistance and industrial usability of cellulose-IP, this study employs nano-boron nitride (h-BN) along with three silane coupling agents - 3-ammonopropyltriethoxysilane (KH550), 3-glycidyloxypropyltrimethoxysilane (KH560), and 3-methacryloyloxypropyltrimethoxysilane (KH570) - to modified cellulose-IP. Upon evaluating the properties and simulation results of the modified cellulose-IP, KH550-hBN demonstrated superior properties and was selected for an accelerated thermal aging test lasting 60 days. The results indicated that KH550-hBN effectively mitigates the degradation of cellulose-IP. In the late aging stage, the mechanical properties of the modified cellulose-IP deteriorated more slowly than those of pure cellulose-IP. Additionally, thermal conductivity improved by 26.9%, while breakdown field strength increased by 12.1%. The introduction of KH550-hBN promotes the formation of a complete thermal conductivity network within the cellulose matrix, increases the number of intermolecular hydrogen bonds and introduces nano-barrier regions. These improvements enhance the interactions between the cellulose systems and effectively inhibit the generation and migration of polar derivatives during the aging process, resulting in a significant improvement in the longevity and stability of transformer internal insulation systems. © 1994-2012 IEEE.

关键词： Boron nitride

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Seismic retrofit of structures using friction dampers

Journal of Dynamic Disasters

引用

Journal of Dynamic Disasters 2025年第2期1卷

作者： Yun Joo Lee Akbar Sajjad Jinkoo Kim Civil Architectural and Environmental System Engineering Sungkyunkwan University South Korea Dept. of Global Smart City Sungkyunkwan University South Korea

This study investigates the feasibility and applicability of spring-friction dampers for seismic retrofit of structures. Finite element analysis of the device was carried out to verify the relationship between the bolt pretension force and the friction yield force. Parametric study of the device was conducted to investigate the effect of the recovery force provided by the coil spring. The developed damper was applied to a five-story reinforced concrete (RC) framed building to assess its efficacy for seismic retrofit. The seismic performance of the device was thoroughly evaluated, focusing on its ability to reduce inter-story and residual displacements by dissipating seismic energy and providing stiffness and recovery force. The particle swarm optimization algorithm was applied to find out the optimum number of dampers and their optimum locations. The results demonstrate that the proposed damper and the optimum design scheme can be effectively utilized to protect structures against seismic loads.

关键词： Friction dampers Seismic retrofit Earthquakes Particle swarm optimization

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