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Compatibility in Power Electronics (CPE)

作者： Mohammadreza Azizi Oleksandr Husev Carlos Roncero-Clemente Oleksandr Veligorskyi Ryszard Strzelecki Department of Radiotechnic and Embedded Systems Chernihiv Polytechnic National University Chernihiv Ukraine Department of Electric Electronic and Automation Engineering University of Extremadura Badajoz Spain Faculty of Electrical and Control Engineering Gdansk University of Technology Gdansk Poland

ISBN: (数字)9798331515171

ISBN: (纸本)9798331515188

In this paper, flatness-based control theory is developed to enhance the dynamic performance of a multiport energy router. The energy router integrates multiple power sources and sinks, including photovoltaic systems, battery storage, the grid, and various loads. Consequently, any sudden change in a subsystem can induce dynamic conditions across the entire system. The presented method controls the grid-side inverter and regulates the de link voltage. To implement this control approach, the system equations have been established, and it has been demonstrated that the system is differentially flat. Afterward, the controller design has been addressed. The simulation results confirm the proper performance of this method, and the comparisons made validate the high speed and accuracy of the system responses compared to conventional solutions.

关键词： Photovoltaic systems Power engineering Accuracy Simulation Modulation control systems Inverters Mathematical models control theory Voltage control

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Survey of Distributed Algorithms for Resource Allocation over Multi-Agent systems

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Pirani, Mohammad Nekouei, Ehsan Zarrabi, Houman Keypour, Reza Rikos, Apostolos I. Johansson, Karl H. Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical Engineering and Computer Science Oregon State University United States Department of Mechanical Engineering University of Ottawa OttawaON Canada City University of Hong Kong China Tehran Iran Faculty of Electrical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multiagent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain. © 2024, CC BY.

关键词： Multi agent systems

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Advance Artificial Intelligence Technique for Designing Double T-Shaped Monopole Antenna

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Computers, Materials & Continua 2021年第12期69卷 2983-2995页

作者： El-Sayed M.El-kenawy Hattan F.Abutarboush Ali Wagdy Mohamed Abdelhameed Ibrahim Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Electrical Engineering Department College of EngineeringTaibah UniversityMedina 42353Saudi Arabia Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza 12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt

Machine learning(ML)has taken the world by a tornado with its prevalent applications in automating ordinary tasks and using turbulent insights throughout scientific research and design *** is a massive area within artificial intelligence(AI)that focuses on obtaining valuable information out of data,explaining why ML has often been related to stats and data *** advanced meta-heuristic optimization algorithm is proposed in this work for the optimization problem of antenna architecture *** algorithm is designed,depending on the hybrid between the Sine Cosine Algorithm(SCA)and the Grey Wolf Optimizer(GWO),to train neural networkbased Multilayer Perceptron(MLP).The proposed optimization algorithm is a practical,versatile,and trustworthy platform to recognize the design parameters in an optimal way for an endorsement double T-shaped monopole *** proposed algorithm likewise shows a comparative and statistical analysis by different curves in addition to the ANOVA and *** offers the superiority and validation stability evaluation of the predicted results to verify the procedures’accuracy.

关键词： Antenna optimization machine learning artificial intelligence multilayer perceptron sine cosine algorithm grey wolf optimizer

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Unscented Kalman Filter for State of Charge Estimation of Lithium Titanate Battery

Unscented Kalman Filter for State of Charge Estimation of Li...

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Joshua Chun-Ken Dardchuntuk David Banjerdpongchai Department of Electrical Engineering Center of Excellence in Intelligent Control Automation of Process Systems Faculty of Engineering Chulalongkorn University 254 Phayathai Road Pathumwan Bangkok Thailand

An accurate estimation of the state of charge (SoC) of lithium titanate (LTO) batteries is required for their effective operation and management. In this study, we propose an unscented Kalman filter (UKF) approach for estimating the SoC of LTO batteries, which are challenging to assess due to the nonlinear voltage-SoC relationship and aging impact. Our approach uses a state and measurement model based on LTO’s electrochemical characteristics and employs sigma points and weights to address nonlinearities. According to the findings of our research, the UKF-based methodology has high accuracy, rapid convergence, and resilience to discharge rate, outperforming or matching the capabilities of existing state-of-the-art approaches. This work provides a novel and effective solution for LTO battery SoC estimation, useful for applications in electric vehicles, energy storage, and smart grid energy systems.

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Design of a Solar Pumping System with Battery Storage Based on Three-Port DC-DC Converter

Design of a Solar Pumping System with Battery Storage Based ...

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International Youth Conference on Radio Electronics, electrical and Power engineering (REEPE)

作者： A. Abu Zaid Ibrahim Abdelsalam M. I. Marei Electrical Power Systems Department College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical and Control Department College of Engineering & Technology Arab Academy for Science Technology & Maritime Transport Cairo Egypt Electrical Power and Machines Department Faculty of Engineering Ain Shams University Cairo Egypt

This paper introduces a design for a photovoltaic (PV) pumping system that incorporates battery bank storage. A three-port DC-DC converter (TPC) is employed to integrate the PV, battery bank, and induction motor drive (IMD) based pumping system. The purpose of the battery bank is to provide support to the pump during shading conditions to prevent frequent suspension and down-thrust situations. Additionally, the proposed system aims to maintain the free-floating impeller operating range by imposing rotational speed limits on the pump. The input ports of the TPC consist of the PV port, which is controlled to harvest the maximum power point (MPP) using the perturb and observe technique, and the battery port. The output port is linked to a DC bus using passive rectifier to supply power to the variable speed V/F IMD. The battery port is regulated to sustain the DC bus voltage in a desired voltage range that ensures the impeller operation under free-floating conditions. The proposed system is simulated using MATLAB/Simulink software. The simulation results are discussed to evaluate the dynamic performance.

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Performance Comparison of Lightweight CNN Models for Drone Collision Avoidance Dataset

Performance Comparison of Lightweight CNN Models for Drone C...

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International Conference on electrical engineering/Electronics, Computer, Telecommunications and Information Technology, ECTI-CON

作者： Rifqi Nabila Zufar David Banjerdpongchai Department of Electrical Engineering Faculty of Engineering Center of Excellence in Intelligent Control Automation of Process Systems Chulalongkorn University254 Phayathai Road Pathumwan Bangkok Thailand

Collision Avoidance System (CAS) is important for drone safety. CAS consists of three steps e.g., obstacle sensing, collision prediction, and collision avoidance. Collision prediction enables drones to gain information, process information, and estimate whether the object has a risk of collision. Convolution Neural Network (CNN) is one of the methods that can be employed for collision prediction. However, CNN is a method that needs a large data in the training. Dario Pedro et al. provided a dataset called the CoLANet dataset that consists of VDOs of collision drones. Subsequently, they proposed a new algorithm called Neural Network Pipeline which has a Convolution Neural Network (CNN) part to extract the feature from a couple of images. CNN extracts images by using MobileNetV2 as a pre-trained model. They chose MobileNetV2 due to training performance from another dataset. This paper aims to assess the performance of lightweight CNN models using the CoLANet dataset. The models will be trained on the Keras library with parameters of fewer than ten million. The models will be validated by Confusion Matrix and Receiver Operating Characteristics. In conclusion, we examine which pre-trained CNN model has the best performance and suggest ongoing work.

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Comparative analysis of bioinspired algorithms for tuning a flatness-based controller: A ball and beam case study

Comparative analysis of bioinspired algorithms for tuning a ...

引用

Latin America Congress on Computational Intelligence (LA-CCI)

作者： M. A. Pastrana José Oniram de A. Limaverde Filho Luiz Henrique Oliveira Jaider Bautista Eugênio Fortaleza Daniel M. Muñoz Department of Mechanical Engineering Automation and Control Group (GRACO) University of Brasília Brasília Brazil Department of Electrical Engineering University of Brasília Brasília Brazil Faculty of Engineering Systems Undergraduate Program University of the Amazonia Florencia Colombia

ISBN: (数字)9798350374575

ISBN: (纸本)9798350374582

This paper provides a comprehensive comparative analysis of the performance of four bioinspired algorithms (Differential Evolution, Grasshopper Optimization Algorithm, Moth-flame Optimization, and Particle Swarm Optimization) in designing a flatness-based controller for a ball and beam system simulated in MATLAB/Simulink environment. The assessment involves the tuning of feedback gains utilizing four distinct fitness functions: Integral Square Error, Integral Absolute Error, Integral Time Absolute Error, and an ad-hoc cost function. Hypothesis tests are employed to choose the optimal controllers from each combination for every bioinspired algorithm and a fitness function. Subsequently, a control performance analysis of these controllers for the ball and beam system is conducted, focusing on critical parameters such as overshoot, settling time, and steady-state error. This analysis demonstrates that all the controllers achieve a zero steady-state error. Noteworthy is the flatness-based controller tuned with the bioinspired Grasshopper Optimization Algorithm and utilizing the Integral Time Absolute Error fitness function, which stands out for achieving the fastest response with the lowest overshoot. This particular controller emerges as the most recommended choice for the specified application.

关键词： Particle beams Focusing Cost function MIMO Steady-state Performance analysis Particle swarm optimization MATLAB Tuning Computational intelligence

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Optimization with genetic algorithms of individual pitch control design with and without azimuth offset for wind turbines in the full load region

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IFAC-PapersOnLine 2023年第2期56卷 342-347页

作者： Manuel Lara Juan Garrido Jan-Willem van Wingerden Sebastian Paul Mulders Francisco Vázquez Department of Electrical Engineering and Automation University of Cordoba Campus of Rabanales 14071 Cordoba Spain Delft Center for Systems and Control Faculty of Mechanical Engineering Delft University of Technology Mekelweg 2 2628 CD Delft The Netherlands

The reduction of fatigue loadings in wind turbines to increase their lifetime has become of special interest from a control viewpoint. Individual Pitch control (IPC) is a well-known approach used to mainly mitigate periodic blade loads, and it is usually implemented with the assistance of the multi-blade coordinate (MBC) transformation, which transforms and decouples the measured blade load signals from a rotating frame into a non-rotating tilt-axis and yaw-axis. Nevertheless, these axes still show coupling between them in practical scenarios adversely affecting the system performance. Previous studies have demonstrated the benefits of including an extra tuning parameter in the MBC, the azimuth offset, in improving the performance achieved by the IPC. However, the tuning of this parameter and its real improvements that can be obtained compared to the IPC without this offset require more research. Here, two 1P+2P IPC, with and without additional azimuth offset, are designed and applied to the 5 MW reference turbine model developed by NREL using the FAST software as a simulation platform. The controller parameter tuning is formulated as an optimization problem that minimizes the blade fatigue load according to the Dirlik index and that is resolved through genetic algorithms. To fairly analyze the improvement entailed by the addition of the azimuth offset, both optimized IPC schemes, with and without azimuth offset, are compared qualitatively and quantitatively using a classical controller as the baseline case. From the simulation results, it can be stated that the optimal IPC scheme with azimuth offset compared with the IPC scheme without offset achieves improvements of around 11% in load reduction and pitch signal effort.

关键词： control system design control of renewable energy resources wind turbine control individual pitch control genetic algorithms

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