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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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RGB Arabic Alphabets Sign Language Dataset

arXiv

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arXiv 2023年

作者： Al-Barham, Muhammad Alsharkawi, Adham Al-Yaman, Musa Al-Fetyani, Mohammad Elnagar, Ashraf Sa’aleek, Ahmad Abu Al-Odat, Mohammad MLALP Research Group University of Sharjah United Arab Emirates Mechatronics Engineering Department The University of Jordan Jordan AppsWave for Information Technology Jordan Department of Computer Science University of Sharjah United Arab Emirates Al-Wefaq Control Systems Doha Qatar Student Guidance Department The University of Jordan Jordan

This paper introduces the RGB Arabic Alphabet Sign Language (AASL) dataset. AASL comprises 7,857 raw and fully labelled RGB images of the Arabic sign language alphabets, which to our best knowledge is the first publicly available RGB dataset. The dataset is aimed to help those interested in developing real-life Arabic sign language classification models. AASL was collected from more than 200 participants and with different settings such as lighting, background, image orientation, image size, and image resolution. Experts in the field supervised, validated and filtered the collected images to ensure a high-quality dataset. AASL is made available to the public on Kaggle.1 © 2023, CC BY.

关键词： Classification (of information)

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Bridging reinforcement learning and iterative learning control: Autonomous reference tracking for unknown, nonlinear dynamics

TechRxiv

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TechRxiv 2021年

作者： Meindl, Michael Lehmann, Dustin Seel, Thomas Embedded Mechatronics Laboratory Hochschule Karlsruhe Germany Control Systems Group Technische Universität Berlin Germany Control Systems Group Technische Universität Berlin Germany Departement Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Nürnberg Erlangen Germany

This work addresses the problem of reference tracking in autonomously learning agents with unknown, nonlinear dynamics. Existing solutions require model information or extensive parameter tuning, and have rarely been validated in real-world experiments. We propose a learning control scheme that learns to approximate the unknown dynamics by a Gaussian Process (GP), which is used to optimize and apply a feedforward control input on each trial. Unlike existing approaches, the proposed method neither requires knowledge of the system states and their dynamics nor knowledge of an effective feedback control structure. All algorithm parameters are chosen automatically, i.e. the learning method works plug and play. The proposed method is validated in extensive simulations and real-world experiments. In contrast to most existing work, we study learning dynamics for more than one motion task as well as the robustness of performance across a large range of learning parameters. The method’s plug and play applicability is demonstrated by experiments with a balancing robot, in which the proposed method rapidly learns to track the desired output. Due to its model-agnostic and plug and play properties, the proposed method is expected to have high potential for application to a large class of reference tracking problems in systems with unknown, nonlinear dynamics. © 2021, CC BY.

关键词： Dynamics

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Live Demo: E2P–Events to Polarization Reconstruction from PDAVIS Events

Live Demo: E2P–Events to Polarization Reconstruction from P...

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IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

作者： Tobi Delbruck Zuowen Wang Haiyang Mei Germain Haessig Damien Joubert Justin Haque Yingkai Chen Moritz B. Milde Viktor Gruev Sensors Group Institute of Neuroinformatics Univ. of Zurich and ETH Zurich Switzerland AIT Austrian Institute of Technology Center for Vision Automation & Control High-Performance Vision Systems Vienna Austria Intl. Centre for Neuromorphic Systems The MARCS Institute Western Sydney University Sydney Australia Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA

This demonstration shows live operation of of PDAVIS polarization event camera reconstruction by the E2P DNN reported in the main CVPR conference paper Deep Polarization Reconstruction with PDAVIS Events (paper 9149 [...

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Trajectory Tracking for Directional Drilling in Underground Coal Mine Using Robust Model Predictive control

Trajectory Tracking for Directional Drilling in Underground ...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Jiajun Wu Chengda Lu Hao Li Hongchao Wei Min Wu School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Automation China University of Geosciences Wuhan China Xi'an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi'an China China Coal Research Institute Beijing China

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

This paper studies the problem of trajectory tracking control for directional drilling in underground coal mine by devising a robust model predictive control method. First, a directional drilling trajectory extension model is obtained by the linearization of a drilling tool kinematic model through the Taylor series expansion in the borehole depth domain. Then, in view of the influence of the uncertainty of the drilling tool slope and external interference, a robust model predictive control method is introduced in the form of an optimal control problem. A sufficient condition is obtained to calculate the corresponding controller gain matrix on the premise of satisfying the stability of the closedloop control system and the $H_{\infty}$ performance index. At last, the effectiveness of our method is illustrated through simulation analysis. Compared with model predictive control, robust model predictive control directly incorporates the uncertainty of the drilling tool slope and external interference into the control design, enabling it to better adapt to the complex and variable underground coal mine environment.

关键词： Drilling Uncertainty Trajectory tracking Stability criteria Predictive models Taylor series Coal mining Trajectory Thermal stability Predictive control

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TOWARDS RELIABLE DATA-BASED OPTIMAL AND PREDICTIVE control USING EXTENDED DMD

arXiv

引用

arXiv 2022年

作者： Schaller, Manuel Worthmann, Karl Philipp, Friedrich Peitz, Sebastian Nüske, Feliks Technische Universität Ilmemau Institute of Mathematics Optimization-based Control Group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

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Enhancing Traffic Flow Forecasting with Spatio-Temporal Information: A Case Study on Highway Networks in Belgium

Enhancing Traffic Flow Forecasting with Spatio-Temporal Info...

引用

International Conference on Instrumentation control and Automation (ICA)

作者： Muhammad Farhan Fathurrahman Arne Catteeuw Sidharta Gautama Angel Lopez Aguirre Endra Joelianto Herman Y. Sutarto Department Of Industrial Systems Engineering and Product Design Ghent University Ghent Belgium Department of Intelligent System PT. Pusat Riset Energi (PRE) Bandung Indonesia FlandersMake@UGent–Corelab ISyE Lommel Belgium Instrumentation and Control Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung Indonesia

As the number of vehicles continues to increase every year, it is crucial to monitor traffic conditions effectively. Traffic forecasting serves as a valuable method in traffic management to support decision making. This study investigates the impact of utilizing spatio-temporal data for traffic flow forecasting, as opposed to forecasting each location individually. We compare spatio-temporal Long Short-Term Memory (LSTM), a machine-learning technique that utilizes spatially correlated sensor data in a network, and compare this to conventional LSTM and Seasonal ARIMA (SARIMA). Experiments have been conducted on counting loop data of a highway network in Belgium. We have investigated the performances of the forecasting model for different prediction horizons. The results show that spatial information improves the performance of the forecasting model and reduces the training time required. Further analysis show that the spatio-temporal LSTM improves majorities of detectors while the minorities detectors are degraded which imply the importances of network selection. The computational load of the spatio-temporal LSTM model is minimally impacted by the number of sensors in the network thus making it scalable. The results show that longer prediction horizons degrade the forecasting performance but the impact up to a 12 hour-horizon is not significant.

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A Novel High-Impedance Fault Recognition Method for Multi-terminal HVDC system

A Novel High-Impedance Fault Recognition Method for Multi-te...

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2022 IEEE Global Conference on Computing, Power and Communication Technologies, GlobConPT 2022

作者： Ding, Hua Huai, Qing Wang, Jing Zhu, Shu Li, Yuye Hu, Zhipeng State Grid Integrated Energy Service Group Co.Ltd Beijing China State Grid Jibei Electric Power Company Limited Electric Power Research Institute Beijing China School of Electrical Engineering and Automation Wuhan University Wuhan China Electric Power Dispatching and Control Center of Hunan Electric Power Co. Ltd. Changsha China State Key Laboratory of Operation Control of Renewable Energy and Storage Systems Beijing China State Grid Zhejiang Electric Power Company Hangzhou Power Supply Company Hangzhou China

ISBN: (数字)9781665493659

ISBN: (纸本)9781665493659

To enhance the protection sensitivity under high-impedance fault, non-unit protection is proposed for the multi-terminal high-voltage dc system based on the theory of variational mode decomposition and energy entropy (VMDEE). The innovation of the protection method is that the VMDEE of pole current and fault impedance are positively correlated. Therefore, the fault indicator of this protection method would increase with fault impedance, which essentially solves the problem of poor protection sensitivity under high-impedance fault. A four-terminal model of the Zhangbei HVDC grid is developed in PSCAD/EMTDC environment for case study, and the fault information is processed using the MATLAB. The reliability and efficiency of this non-unit protection are verified with simulation research considering different influence factors. © 2022 IEEE.

关键词： Variational mode decomposition

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Investigation of Electrical and Thermal Behavior of Modified Epoxy Structures for DC and High Frequency Applications 22

Investigation of Electrical and Thermal Behavior of Modified...

引用

22nd International Symposium on High Voltage engineering, ISH 2021

作者： Awais, Muhammad Dai, Chao Meng, Fan-Bo Paramane, Ashish Wang, Xin Chen, Xiangrong Zhejiang Provincial Key Laboratory of Electrical Machine Systems College of Electrical Engineering Zhejiang University Hangzhou China Zhejiang Key Laboratory of High-efficient Energy-saving and Pollutant Control Technology for Thermal Power Generation Zhejiang Energy Group Research Institute Co. Ltd Hangzhou China

ISBN: (纸本)9781839536052

Epoxy resin insulations are considered as a potential candidate for various applications like transformers, cable terminations, power electronic device packaging, and bushings. This has promoted a great interest in their use as high voltage insulation materials for high frequency applications. This work presents the effect of different curing agent concentrations on the electrical and thermal properties of epoxy resin for DC and high frequency applications. Differential scanning calorimetry is used to characterize the thermal performance of the materials. The insulation properties such as breakdown measurements under DC and high frequency stress, DC resistivity, and space charge distributions at different temperatures, and dielectric spectroscopy at different frequencies and temperatures are performed. Results reveal that high curing agent concentrations optimize the thermal and electrical performance of epoxy resin insulation. However, high frequency stress severely degrades the breakdown and dielectric properties of all the prepared materials. By the control of resin stoichiometry, epoxy resin with suitable electrical and thermal properties can be realized for high frequency applications. © 2021 The Institution of engineering and Technology.

关键词： Epoxy resins

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Scalable Multiport DC-DC Converter for Bidirectional EV charging in DC Traction Grids

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IEEE Transactions on Circuits and systems II: Express Briefs 2025年

作者： Lukianov, Mykola Romero-Cadaval, Enrique Kasprowicz, Andrzej Husev, Oleksandr Strzelecki, Ryszard Department of Power Electronics and Electrical Machines Gdañsk University of Technology Gdañsk80-222 Poland Power Electrical and Electronic Systems R and D Group School of Industrial Engineering University of Extremadura Badajoz06006 Spain Gdynia Maritime University Faculty of Electrical Engineering Gdynia Poland Warsaw University of Technology Institute of Control and Industrial Electronics Koszykowa 75 Warsaw00-662 Poland

The limited availability of public Electric Vehicle (EV) charging infrastructure poses a significant challenge to EV adoption in urban areas, that requires significant economic expenses. One potential solution to mitigate the problem is utilizing the existing traction DC grid infrastructure for EV charger connections. Such solution allows exploiting already existing traction substation equipment for both EV charging and powering DC traction grids, potentially reducing charging station installation costs. At the same time, bidirectional EV charger operation allows providing services such as DC grid voltage stabilization and traction transport braking energy recuperation. Therefore, this paper examines a scalable multiport DC-DC converter, suitable for multiterminal EV charging stations, located in the proximity to DC traction networks. The use of a multiport converter allows for sharing electric components across multiple ports, reducing the total number of elements required, compared to conventional solutions, where each EV charger relies on a separate converter. © 2004-2012 IEEE.

关键词： Charging stations

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