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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Che-Wei Chang Matthias Spieler Rolando Burgos Ayman El-Refaie Renato Amorim Torres Dong Dong Virginia Tech Center for Power Electronics Systems (CPES) Blacksburg USA Electrical and Computer Engineering Marquette University Milwaukee USA General Motors Detroit USA

ISBN: (数字)9798350376067

ISBN: (纸本)9798350376074

Paralleling the SiC MOSFETs is a low-cost solution to increase current rating in high-power applications. However, the unbalanced dynamic current sharing during switching transient can cause unequal thermal distribution which may lead to shorter operating lifetime. To balance the dynamic currents, employing active gate driver with feedback control proves effective. Yet, for the feedback processing circuit, a current sensor that can track the high-di/dt dynamic current is needed. This paper proposes an improved dynamic current sensing structure for paralleled SiC MOSFETs based on the conventional RC filter-based current sensing. The proposed structure only requires three passive components for each MOSFET. Both analysis and experimental results are presented to verify the proposed sensing structure. The performance of the proposed structure is evaluated under different scenarios. The results show that the proposed structure can accurately track the dynamic current from each MOSFET and provide correct information for signal processing.

关键词： MOSFET Accuracy Silicon carbide Layout Switches Signal processing Information filters Gate drivers Sensors Transient analysis

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Transfer learning-enhanced transformer for virtual process sensing in resistance spot welding

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Manufacturing Letters 2025年 45卷 13-16页

作者： Ethan York Xijia Zhao Hassan Ghessemi-Armaki Blair Carlson Peng Wang Department of Mechanical and Aerospace Engineering University of Kentucky Lexington KY USA Department of Electrical Computer and Systems Engineering Case Western Reserve University Cleveland OH USA General Motors R&D Warren MI USA Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland OH USA

Resistance Spot Welding (RSW) is a crucial manufacturing process, especially in automotive manufacturing. However, quality assurance in RSW remains a challenge, due to the lack of low-cost, non-invasive process sensing techniques. This paper investigates Transformer for generating virtual electrode force and displacement signals from physically measurable DR signals. Furthermore, to enhance the generalizability, the transformer has been enhanced by transfer learning to adapt to dynamic welding scenarios, by pre-training the transformer model with data from diverse welding conditions and fine-tuning using minimal experimental data from novel conditions. Experimental results validate the effectiveness of virtual sensing technology for RSW process monitoring.

关键词： Virtual sensing Machine learning Transfer learning Expulsion detection Resistance spot welding

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Unsupervised learning on particle image velocimetry with embedded cross‐correlation and divergence‐free constraint

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IET Cyber-systems and Robotics 2022年第3期4卷 200-211页

作者： Yiwei Chong Jiaming Liang Tehuan Chen Chao Xu Changchun Pan College of Control Science and Engineering Zhejiang UniversityHangzhouZhejiangChina School of Mechanical Engineering and Mechanics Ningbo UniversityNingboZhejiangChina Ningbo Artificial Intelligence Institute Shanghai Jiao Tong UniversityNingboZhejiangChina The State Key Laboratory of Industrial Control Technology Institute of Cyber‐Systems and ControlZhejiang UniversityHangzhouZhejiangChina School of Electronic Information and Electrical Engineering Key Laboratory of Shanghai Navigation and Location Based ServicesShanghai Jiao Tong UniversityShanghaiZhejiangChina

Particle image velocimetry(PIV)is an essential method in experimental fluid *** recent years,the development of deep learning‐based methods has inspired new ap-proaches to tackle the PIV problem,which considerably improves the accuracy of ***,the supervised learning of PIV is driven by large volumes of data with ground truth ***,the authors consider unsupervised PIV *** has been some work on unsupervised PIV,but they are not nearly as effective as supervised learning *** authors try to improve the effectiveness and accuracy of unsupervised PIV by adding classical PIV methods and physical *** this paper,the authors propose an unsupervised PIV method combined with the cross‐correlation method and divergence‐free constraint,which obtains better performance than other unsupervised PIV *** authors compare some classical PIV methods and some deep learning methods,such as LiteFlowNet,LiteFlowNet‐en,and UnLiteFlowNet with the authors’model on the synthetic ***,the authors contrast the results of LiteFlowNet,UnLiteFlowNet and the authors’model on experimental particle *** a result,the authors’model shows comparable performance with classical PIV methods as well as supervised PIV methods and outperforms the previous unsupervised PIV method in most flow cases.

关键词： neural network particle image velocimetry unsupervised learning

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DATASISMO: A model for the forecast of health needs via analysis of the demand for supplies and medicines 6

DATASISMO: A model for the forecast of health needs via anal...

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6th Congreso Internacional en Inteligencia Ambiental, Ingenieria de Software y Salud Electronica y Movil, AmITIC 2023 - 6th International Congress on Environmental Intelligence, Software engineering and Electronic and Mobile Health, AmITIC 2023

作者： Chang, Ignacio Alvarez, Humberto R. Aguilar, Orlando Garcia, Antony Cabrera, Victor L. Castillo, Juan Marcos Univversidad Tecnologica de Panama Department of Control and Instrumentation Engineering Panama Panama Univversidad Tecnologica de Panama Industrial. Engineering Department Panama Panama Univversidad Tecnologica de Panama Mechanical Engineering Department Panama Panama Ingenieria Electrica Univversidad Tecnologica de Panama Electrical Engineering Department Panama Panama Ingenieria Electrica Univversidad Tecnologica de Panama Departament of Information Control Systems and Evaluation of Informatics Resources Panama Panama Ingenieria Electrica Univversidad Tecnologica de Panama CIDITIC/CIDETYS AIP Panama Panama

ISBN: (纸本)9798350318692

At the end of 2019, the disease later known as COVID-19 emerged, causing the recent pandemic. The objective of this study was to propose an adaptive mathematical model with the capacity to forecast the health needs of the conntry in the face of the pandemic situation, based on the health needs and the demands for supplies, medicines and other resources identified in the database of the Ministry of Health from the Republic of Panama. In addition, information from the GitHub Repository of the John Hopkins University Center for systems Science and engineering was considered. A quantitative methodology was used from existing data, based on a mixed approach that incorporated dynamic simulation, machine learning and Six-Sigma design. A monitoring model has been developed focused on reducing health risk due to shortages of medicines and medical supplies in the event of a pandemic or similar situation. This research has laid the foundations for the development of epidemiological models in Panama to facilitate data processing, providing a complementary tool for decision-making based on short-Term predictions, which is expected to achieve a better distribution of health resources in the country in cases of national health emergency. © 2023 IEEE.

关键词： Supply chains

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Robust Stochastic Shortest-Path Planning via Risk-Sensitive Incremental Sampling

arXiv

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arXiv 2024年

作者： Enwerem, Clinton Noorani, Erfaun Baras, John S. Sadler, Brian M. The Department of Electrical & Computer Engineering The Institute for Systems Research University of Maryland College ParkMD United States The University of Texas AustinTX United States

With the pervasiveness of Stochastic Shortest-Path (SSP) problems in high-risk industries, such as last-mile autonomous delivery and supply chain management, robust planning algorithms are crucial for ensuring successful task completion while mitigating hazardous outcomes. Mainstream chance-constrained incremental sampling techniques for solving SSP problems tend to be overly conservative and typically do not consider the likelihood of undesirable tail events. We propose an alternative risk-aware approach inspired by the asymptotically-optimal Rapidly-Exploring Random Trees (RRT*) planning algorithm, which selects nodes along path segments with minimal Conditional Value-at-Risk (CVaR). Our motivation rests on the step-wise coherence of the CVaR risk measure and the optimal substructure of the SSP problem. Thus, optimizing with respect to the CVaR at each sampling iteration necessarily leads to an optimal path in the limit of the sample size. We validate our approach via numerical path planning experiments in a two-dimensional grid world with obstacles and stochastic path-segment lengths. Our simulation results show that incorporating risk into the tree growth process yields paths with lengths that are significantly less sensitive to variations in the noise parameter, or equivalently, paths that are more robust to environmental uncertainty. Algorithmic analyses reveal similar query time and memory space complexity to the baseline RRT* procedure, with only a marginal increase in processing time. This increase is offset by significantly lower noise sensitivity and reduced planner failure rates. © 2024, CC BY.

关键词： Stochastic systems

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Data-driven Optimal Cooperative Output Regulation of Unknown Linear Discrete-time Multi-agent systems

Data-driven Optimal Cooperative Output Regulation of Unknown...

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第35届中国控制与决策会议

作者： Dong Liang Yi Dong Chaoli Wang Engang Tian Department of Control Science and Engineering School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous Systems Tongji University

The cooperative output regulation problem has been extensively studied on the basis of the distributed observer ***,the majority of the existing research assumes that the dynamics is known *** remove this condition,the cooperative output regulation problem is further solved via the data-driven framework where the dynamics of the plant is ***,a data-driven distributed observer is established to estimate the state of the leader with unknown dynamics subject to external ***,the unknown regulator equations are solved using the iterative recurrent neural network ***,the state-based data-driven distributed control law is synthesized to solve the *** optimal gains are derived by solving convex optimization problems using input and state ***,a numerical example is presented to verify the feasibility of the proposed framework.

关键词： Linear systems multi-agent systems cooperative output regulation data-driven control optimal control

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Multivariate Optimal Hybrid Deep Learning Model for Forecasting of Day-Ahead Solar Irradiance with Meteorological Constraints

Multivariate Optimal Hybrid Deep Learning Model for Forecast...

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Annual North-American Power Symposium

作者： Sohag Kumar Saha Satish M. Mahajan Dept. of Electrical & Computer Engineering (ECE) Tennessee Technological University Center for Energy Systems Research (CESR) Tennessee Technological University Cookeville TN USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

With the growing integration of solar power gener-ation into smart grids, accurate solar irradiance forecasting is of paramount importance for efficient grid operation and renew-able energy management. In this paper, a data decomposition approach with two different methods of deep learning and grid search optimization was combined to develop a better hybrid model to forecast solar irradiance. The proposed model combines spatial and temporal information to improve forecasting accuracy by considering the impact of meteorological constraints such as global horizontal irradiance, temperature, relative humid-ity, wind speed, cloud type, direct normal irradiance, diffuse horizontal irradiance, and solar zenith angle. In addition to the model architecture, this work incorporates hyper-parameter optimization to fine-tune the parameters of the model for optimal performance. The design of the model ensures that the system adapts to the specific characteristics of the solar irradiance data and meteorological conditions under consideration. The proposed hybrid model was evaluated using real-world data to outperform traditional forecasting methods in terms of accuracy. The results of the proposed hybrid model indicate better prediction ability as measured by four parameters (lower RMSE and MAE, fewer epochs, and a higher R2 co-efficient).

关键词： Solar irradiance Deep learning Adaptation models Accuracy Predictive models Data models Smart grids Forecasting Optimization Load modeling

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CPS, Sustainability and Efficiency in an Aquaponic Greenhouse

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 210-215页

作者： Galvão, J. Santos, P. Aires, L. Neves, F. Department of Electrical Engineering/ESTG Polytechnic of Leiria Leiria2411-901 Portugal LSRE-LCM-Laboratory of Separation and Reaction Engineering Laboratory of Catalysis and Materials Escola Superior de Tecnologia e Gestão Instituto Politécnico de Leiria Leiria2411-901 Portugal ALiCE-Associate Laboratory in Chemical Engineering Faculty of Engineering University of Porto Rua Dr. Roberto Frias Porto4200-465 Portugal R&D Unit Institute for Systems Engineering and Computers/INESC Coimbra Portugal Department of Computer Science Polytechnic of Leiria Leiria2411-901 Portugal

Nowadays the humanity needs to produce food in greater quantity and quality, in order to satisfy citizens and markets, supported by advanced technologies such as cyber-physical system (CPS) or intelligent systems where software and electronic devices and receivers of physical variables are mechanisms that control the management of multiple actions inherent to plant growth. Electronic devices and receivers of physical variables are mechanisms that involve a network of IoT components and control multiple actions inherent to plant growth in agricultural greenhouses, which in short, are intensive food production systems. In this context, this paper presents a case study that uses a monitoring and management system that considers environmental parameters measured inside a greenhouse, allowing the manager to assess the working status of an aquaponic systems, and detect critical conditions that require quick decisions in real time. It is intended to develop technology that can be used in greenhouses, close to consumer centres or cities, to mitigate the impact of transport, lowering CO2 emissions resulting from the distribution channels between the producers and consumers. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy efficiency

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Performance Analysis of Hybrid Power Plant under Varying Grid Conditions

Performance Analysis of Hybrid Power Plant under Varying Gri...

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Australasian Universities Power engineering Conference, AUPEC

作者： Ashan Imantha M. H. Bandara Fatemeh Shahnazian Ruifeng Yan Tapan Kumar Saha UQ School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia Department of Wind and Energy Systems Technical University of Denmark (DTU) Roskilde Denmark

ISBN: (数字)9798350377941

ISBN: (纸本)9798350377958

Large synchronous generators are being retired, while inverter-based renewable energy sources (IBR) are increasing, weakening future power system strength. As the grid weakens, grid-following (GFL) IBRs may become unstable. However, grid-forming (GFM) IBRs can reinforce weak grids, ensuring stability. Hybrid power plants (HPPs) with GFM and GFL can operate in very weak grids. However, the dynamic differences of IBR plants complicate interactions between plant-level controllers in HPPs. This study uses advanced modelling and time-domain simulations to examine HPP dynamic behaviour under various grid strengths. An HPP with three IBR power plants is used to evaluate the centralised, hierarchical control system under different system strengths and X/R ratios. The HPP’s stability boundary is maintained across different system strengths. The findings improve our understanding of the interactions between the HPP and the grid and support optimised design and operation strategies for future energy systems.

关键词： Reactive power Grid following Power system dynamics Power system stability Stability analysis Hybrid power systems Synchronous generators Time-domain analysis Power generation Grid forming

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A Novel Decentralized Coordination Control Scheme for the Complex Transactive Energy Prosumers

A Novel Decentralized Coordination Control Scheme for the Co...

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Energy, Power, Environment, Control, and Computing (ICEPECC), International Conference on

作者： Muhammad Ikram Zakir Ullah Muhammad Nasir Salman Ahmed Safdar Nawaz Khan Marwat Dept. of Computer Systems Engineering University of Engineering and Technology Peshawar Pakistan Dept. of Electrical Power Engineering University of Engineering and Technology Peshawar Pakistan Deptment of Physics Allama Iqbal Open University Islamabad Pakistan

The significant penetration of renewable energy sources (RES) in the smart grid (SG) provides a new landscape for researchers to develop an optimal energy management model. The households with RES become prosumers to provide the surplus energy in the local community. The coordination and control of such RES prosumers are important to estimate the available energy in the system and the total demand required. In this paper, we proposed a decentralized coordination control approach for transactive energy systems (TES). The proposed TES is capable of coordinating and controlling a complex network of prosumers with limited control information. The consensusability and graph-theoretic schemes are used for prosumer RES nodes. In the proposed model, energy trading among large-scale prosumers has been demonstrated. The algorithm is capable to provide the total surplus energy available across the network and total demand in an autonomous fashion. The coordination control among prosumers is achieved through a computationally efficient approach without sharing the prosumer’s profile to preserve the security and privacy of the prosumers. To validate the proposed scheme, we perform extensive simulations on decentralized 450 prosumers. The resiliency of the proposed scheme is confirmed through dynamic and contingent communication topologies for energy trading ratios, cumulative returns, and convergence ratios. The cumulative returns improved from 0.447% to 24.8% while the energy trade among the prosumers recorded from -0.89 to 2.09. The final return is enhanced from −1.95% to 22.3% due to internal and external coordination control of nonlinear loads.

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