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2nd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2021

作者： Ghosh, S.K. Roy, T.K. Pramanik, M.A.H. Mahmud, M.A. Rajshahi University of Engineering Technology Department of Electrical Electronic Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electronics Telecommunication Engineering Rajshahi6204 Bangladesh Rajshahi University of Engineering Technology Department of Electrical Computer Engineering Rajshahi6204 Bangladesh Northumbria University Newcastle Electrical Power Control Systems Research Group Faculty of Engineering and Environment United Kingdom

ISBN: (纸本)9781665443197

A composite backstepping integral sliding mode controller (BISMC) is proposed in this work for a DC-DC boost converter feeding a constant power load (CPL) in a simple DC distribution system. Here, the main objective is to ensure the large-signal stability with wide variations in operating regions. The proposed BISMC is designed based on the feedback linearized model of the DC-DC boost converter with a CPL where the average state-space model is exactly linearized. The use of exact feedback linearization overcomes the non-minimum phase problem of a DC-DC boost converter due to the presence of the CPL that exhibits negative impedance characteristics. The control law is obtained using the combination of backstepping and integral sliding mode control schemes where both approaches ensure the faster convergence of errors for all states and the integral action on the sliding surface is designed in such a fashion that will ensure the constant frequency operation of the power converter feeding the CPL while eliminating the variable frequency associated problem of the existing controllers. In addition, the stability of the whole system is analyzed through the Lyapunov stability theory. Numerical simulations are conducted to verify the performance of the designed composite nonlinear controller whose performance is also compared with a double-loop proportional integral (PI) controller to verify the superiority of the designed controller. © 2021 IEEE.

关键词： Backstepping

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Evolution Behaviors and Properties of Power Compartment Fires in Underground Urban Utility Tunnels-A Review

Evolution Behaviors and Properties of Power Compartment Fire...

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China International Electrical and Energy Conference (CIEEC)

作者： Jianxin Zhou Yunlong Wang Jingrong Zheng Fei You NARI Group Corporation State Grid Electric Power Research Institute NARI Technology Co. Ltd NARI-TECH Nanjing Control Systems Ltd. Nanjing China College of Safety Science and Engineering & Institute of Fire Science and Engineering Nanjing Tech University Nanjing China

In this review, a comprehensive definition for an underground urban utility tunnel UUUT is given to reflect its inherent quality, unique features and latent fire safety issues. Following this, major fire hazards and high fire risks inside power compartments are analyzed. Based on this, international related reported fire accidents are counted and described. Such situation calls for special, systematic and deep researches of these fires. Typical international studies of power compartment fires are hereafter summarized in their main cases, contents, important results and conclusions. However, current researches are far from enough to understand power compartment fires fundamentally and practically. Therefore, seven primary future directions are proposed.

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ITSC Fault Detection in Induction Motor using a Cosine Filter and a CNN Architecture

ITSC Fault Detection in Induction Motor using a Cosine Filte...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： Carlos Morales-Perez Juan Pablo Amezquita-Sanchez Martin Valtierra-Rodriguez Jose Rangel-Magdaleno Jorge Cerezo-Sanchez Ambrosio Leon-Bonilla ENAP-Research Group CA-Sistemas Dinamicos y Control Facultad de Ingenieria Universidad Autonoma de Queretaro (UAQ) Campus San Juan del Rio Queretaro Mexico Electronics Department-Digital Systems Group Instituto Nacional de Astrofisica Optica y Electronica Puebla Mexico Division of Mechatronics Engineering Universidad Tecnologica de Puebla Puebla Mexico

ISBN: (数字)9798331530525

ISBN: (纸本)9798331530532

Detecting faults in rotating electrical machinery is necessary due to current industrial demands. This is because operations of these machines with unattended faults can lead to high electricity consumption, operational inefficiency, and unexpected breakdowns in the worst cases. One of the most common faults in induction motors tends to be stator faults, specifically the inter-turn short circuit (ITSC), which typically do not exhibit apparent symptoms in the early stages. However, these faults can be detected without physical motor intervention, as spectral signatures of faults can be identified through phase current measurements. In this way, only the sensors’ installation in the motor phase is needed to diagnose the motor health correctly. The challenge lies in selecting and designing an appropriate methodology for these purposes. So, this study introduces a technique based on constructing images from current signals, where the fundamental frequency has been suppressed to highlight fault spectral signatures. These generated images are subsequently fed into a convolutional neural network (CNN), which develops a classification stage according to the given features of the fault conditions in previous training stages, allowing the identification of the ITSC fault. Finally, tests were conducted under three mechanical loading conditions and four short-circuit damage conditions in the stator, achieving an accuracy of 98.62 %.

关键词： Training Fault diagnosis Accuracy Induction motors Computer architecture Stators Robustness Real-time systems Circuit faults Convolutional neural networks

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Improved initialization of state-space artificial neural networks

arXiv

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arXiv 2021年

作者： Schoukens, Maarten The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Eindhoven5612 AZ Netherlands

The identification of black-box nonlinear state-space models requires a flexible representation of the state and output equation. Artificial neural networks have proven to provide such a representation. However, as in many identification problems, a nonlinear optimization problem needs to be solved to obtain the model parameters (layer weights and biases). A well-thought initialization of these model parameters can often avoid that the nonlinear optimization algorithm converges to a poorly performing local minimum of the considered cost function. This paper introduces an improved initialization approach for nonlinear state-space models represented as a recurrent artificial neural network and emphasizes the importance of including an explicit linear term in the model structure. Some of the neural network weights are initialized starting from a linear approximation of the nonlinear system, while others are initialized using random values or zeros. The effectiveness of the proposed initialization approach over previously proposed methods is illustrated on two benchmark examples. Copyright © 2021, The Authors. All rights reserved.

关键词： Recurrent neural networks

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Transient and asymptotic properties of robust adaptive controllers in the presence of non-coercive lyapunov functions

arXiv

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arXiv 2021年

作者： Paranjape, Aditya A. Natarajan, Vivek Ghosh, Supratim Tcs Research India Systems and Control Engineering Group Indian Institute of Technology Bombay Mumbai India

Adaptive control architectures often make use of Lyapunov functions to design adaptive laws. We are specifically interested in adaptive control methods, such as the well-known L1 adaptive architecture, which employ a parameter observer for this purpose. In such architectures, the observation error plays a critical role in determining analytical bounds on the tracking error as well as robustness. In this paper, we show how the non-existence of coercive Lyapunov operators can impact the analytical bounds, and with it the performance and the robustness of such adaptive *** Codes 93C20, 93C25, 93C40, 93D05 Copyright © 2021, The Authors. All rights reserved.

关键词： Lyapunov functions

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Digital twin-based automatic train regulation for integration of dispatching and control 1

Digital twin-based automatic train regulation for integratio...

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1st IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2021

作者： Zhou, Min Hou, Zhuopu Liu, Jun Reborts, Clive Dong, Hairong Beijing Jiaotong University State Key Laboratory of Rail Traffic Control and Safety China CRSC Research Design Institute Group Co. Ltd. School of Electronic Electrical and Systems Engineering University of Birmingham United Kingdom

ISBN: (纸本)9781665433372

This paper proposes a framework of digital twin-based integration of train regulation and control for metro systems. We study the integration of train regulation and train control by using deep learning and hybrid search methods. Specifically, a well-trained convolutional neural network is adopted to fit the nonlinear function mapping relationship between the input set (the line conditions and running time of the train in each section) and the output set (the energy consumption of the optimal speed profile and the switching points), which greatly reduces the calculation time of the optimal recommended speed profile in the process of train regulation. An integrated optimization model is proposed with the aim to minimize the total train delay and energy consumption, a hybrid search algorithm based on particle swarm optimization and a genetic algorithm was developed to solve the problem. The effectiveness of the proposed method is verified based on the actual operating data of a Beijing subway line. © 2021 IEEE.

关键词： Energy utilization

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End-to-end Autonomous Driving in Heterogeneous Traffic Scenario Using Deep Reinforcement Learning

End-to-end Autonomous Driving in Heterogeneous Traffic Scena...

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European control Conference (ECC)

作者： Soumyajit Chakraborty Subhadeep Kumar Nirav Bhatt Ramkrishna Pasumarthy Department of Electrical Engineering Indian Instiute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence Indian Institute of Technology Madras Chennai India Department of Biotechnology Center of Data Sciences and Artificial Intelligence Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India Department of Electrical Engineering Robert Bosch Center of Data Sciences and Artificial Intelligence and Prospective Center of Excellence for Network Systems Learning Control and Evolution Group Indian Institute of Technology Madras Chennai India

In this paper, we propose an end-to-end autonomous driving architecture for safe maneuvering in heterogeneous traffic using a reinforcement learning (RL) algorithm. Using the proposed architecture we develop an RL agent that can make driving decisions directly from the sensor data. We formulate the autonomous driving problem as a Markov Decision Process and propose different architectures using Deep $Q -$Networks for two types of sensor data - top view images of the autonomous vehicle (AV) and its surrounding vehicles and information on relative position and velocities of the surrounding vehicles w.r.t the AV. We consider a highway scenario and analyze the performance of the RL agent using the proposed architectures using the highway-env simulator. We compare the driving performance of the AV for both sensor types and discuss their efficacy under varying traffic densities.

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Non-specificity-based Supervised Discretization for Possibilistic Classification 15th

Non-specificity-based Supervised Discretization for Possibi...

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15th International Conference on Scalable Uncertainty Management, SUM 2022

作者： Jenhani, Ilyes Khlifi, Ghaith Sidiropoulos, Panagiotis Jansen, Henk Frangou, George College of Computer Engineering and Science Prince Mohammad Bin Fahd University Al-Khobar31952 Saudi Arabia Massive Analytic Ltd. London United Kingdom Mullard Space Science Laboratory University College London London United Kingdom UCL Quantum Science & Technology Institute London United Kingdom Autonomous Systems Dynamics and Control Research Group Centre for Autonomous and Cyber Physical Systems Cranfield University Bedford United Kingdom

ISBN: (纸本)9783031188428

Data imperfection and information uncertainty are inevitable in many application domains, including medicine, finance, meteorology, cybersecurity, etc. Sources of uncertainty include the lack of information of the solicited experts, sensor unreliability for automatically collected data, conflicting information sources, etc. Classification techniques, such as the non-specificity based possibilistic decision trees (NSPDT) have been proposed to deal with such uncertainty. However, most of the introduced possibilistic methods are limited to categorical features, i.e. they can not process data input that is continuous. This paper address this limitation through a supervised possibilistic discretization method, which transforms continuous features into categorical features in datasets where class labels are imperfect and represented by possibility distributions. The proposed method can be used as a pre-processing step to make it possible to use methods such as NSPDT in datasets that initially have continuous features. The proposed non-specificity based discretization algorithm has provided good quality categorical features which have led to obtain NSPDT trees showing an accuracy higher than 80% in most cases. With original datasets, where the instances have certain (i.e. non-possibilistic) class labels, the proposed possibilistic discretization method has shown a robust performance compared to the standard discretization algorithm which is the default approach in this case. The difference in accuracy has not exceeded 5% for all used datasets and all tested classifiers, and in some cases, the possibilistic discretization outperformed the standard discretization. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision trees

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Frontiers in high entropy alloys and high entropy functional materials

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Rare Metals 2024年第10期43卷 4639-4776页

作者： Wen-Tao Zhang Xue-Qian Wang Feng-Qi Zhang Xiao-Ya Cui Bing-Bing Fan Jia-Ming Guo Zhi-Min Guo Rui Huang Wen Huang Xu-Bo Li Meng-Ru Li Yan Ma Zhi-Hua Shen Yong-Gang Sun De-Zhuang Wang Fei-Yang Wang Li-Qiang Wang Nan Wang Tian-Li Wang Wei Wang Xiao-Yang Wang Yi-Han Wang Fu-Jie Yu Yu-Zhen Yin Ling-Kun Zhang Yi Zhang Jian-Yang Zhang Qi Zhao Yu-Ping Zhao Xin-Dong Zhu Yasir Sohail Ya-Nan Chen Tao Feng Qi-Long Gao Hai-Yan He Yong-Jiang Huang Zeng-Bao Jiao Hua Ji Yao Jiang Qiang Li Xiao-Ming Li Wei-Bing Liao Huai-Jun Lin Hui Liu Qi Liu Qing-Feng Liu Wei-Di Liu Xiong-Jun Liu Yang Lu Yi-Ping Lu Wen Ma Xue-Fei Miao Jie Pan Qing Wang Hong-Hui Wu Yuan Wu Tao Yang Wei-Ming Yang Qian Yu Jin-Yu Zhang Zhi-Gang Chen Liang Mao Yang Ren Bao-Long Shen Xun-Li Wang Zhe Jia He Zhu Zhen-Duo Wu Si Lan School of Materials Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Jiangsu Key Laboratory for Advanced Metallic Materials School of Materials Science and EngineeringSoutheast UniversityNanjing 211189China Department of Physics City University of Hong KongHongKong 999077China School of Materials Science and Engineering Tianjin UniversityTianjin 300072China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China Department of Mechanical Engineering Research Institute for Advanced ManufacturingThe Hong Kong Polytechnic UniversityHong Kong 100872China Center of Electron Microscopy and State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Key Laboratory of Solidification Control and Digital Preparation Technology(Liaoning Province) School of Materials Science and EngineeringDalian University of TechnologyDalian 116024China Institute of Advanced Wear&Corrosion Resistance and Functional Materials Jinan UniversityGuangzhou 510632China School of Mechanics and Civil Engineering China University of Mining and TechnologyXuzhou 221116China School of Physical Sciences Great Bay UniversityDongguan 523000China School of Materials Science and Engineering Harbin Institute of TechnologyHarbin 150001China State Key Laboratory of Materials-Oriented Chemical Engineering College of Chemical EngineeringNanjing Tech UniversityNanjing 211816China Beijing Advanced Innovation Center for Materials Genome Engineering Research Institute for Carbon NeutralityUniversity of Science and Technology BeijingBeijing 100083China Department of Mechanical Engineering The University of Hong KongHong Kong 999077China School of Computing Macquarie UniversitySydney 2113Australia Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province College of Physics and Optoelectronic EngineeringShenzhen UniversityShenzhen 518060China State Key Laborato

Owing to their exceptional properties,high-entropy alloys(HEAs)and high-entropy materials have emerged as promising research areas and shown diverse ***,the recent advances in the field are comprehensively reviewed,organized into five *** first section introduces the background of HEAs,covering their definition,significance,application prospects,basic properties,design principles,and *** subsequent section focuses on cutting-edge high-entropy structural materials,highlighting developments such as nanostructured alloys,grain boundary engineering,eutectic systems,cryogenic alloys,thin films,micro-nano-lattice structures,additive manufacturing,high entropy metallic glasses,nano-precipitate strengthened alloys,composition modulation,alloy fibers,and refractory *** the following section,the emphasis shifts to functional materials,exploring HEAs as catalysts,magneto-caloric materials,corrosion-resistant alloys,radiation-resistant alloys,hydrogen storage systems,and materials for ***,the review encompasses functional high-entropy materials outside the realm of alloys,including thermoelectric,quantum dots,nanooxide catalysts,energy storage materials,negative thermal expansion ceramics,and high-entropy wave absorption *** paper concludes with an outlook,discussing future directions and potential growth areas in the *** this comprehensive review,researchers,engineers,and scientists may gain valuable insights into the recent progress and opportunities for further exploration in the exciting domains of high-entropy alloys and functional materials.

关键词： High entropy alloys Cutting-edge structural materials Cutting-edgefunctional materials

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Bidirectional EV charger integration into LV DC traction grid

Bidirectional EV charger integration into LV DC traction gri...

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Compatibility in Power Electronics (CPE)

作者： Mykola Lukianov Ievgen Verbytskyi Enrique Romero Cadaval Ryszard Strzelecki Faculty of Electrical and Control Engineering Gdańsk University of Technology Gdańsk Poland Dept. of Electronic Devices and Systems National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” Kyiv Ukraine Power Electrical and Electronic Systems R+D Group University of Extremadura Badajoz Spain

Integration of bidirectional electric vehicle (EV) chargers into low voltage (LV) DC traction grids can potentially reduce chargers installation cost, as well as provide ancillary services for the DC grid, such as voltage stabilization, energy recuperation and reduced peak load. However, due to high LV DC grid voltage fluctuations and excessive load that EV chargers can create on a grid, their integration is challenging. In order to solve the problem, an EV charger connection scheme, converters parameters, as well as charger control algorithm must be considered wisely. This paper presents different connection solutions for bidirectional EV chargers integration into traction substations. A comparison of possible converter topologies that can be used in the system is made, depending on their efficiency, cost and applicability. Consequently, the system main parameters such as converters nominal power, voltage ratings and smart charging principle are presented. As a result, the proposed solution can be beneficial for developing EVs and public electric transport, which uses LV DC grids, such as trams/trolleybuses/metro.

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