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Comparative Analysis of Direct Torque Control with Space Vector Modulation (DTC-SVM) and Finite Control Set-Model Predictive Control (FCS-MPC) of Five-Phase Induction Motors

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Progress in Electromagnetics Research B 2024年 108卷 89-104页

作者： Hoggui, Abdelfattah Benachour, Ali Madaoui, Mohamed Chafaa Mahmoudi, Mohand Oulhadj Ecole Nationale Polytechnique El Harrach Algiers Algeria Ecole Nationale Polytechnique El Harrach Algiers Algeria Department of Electrical Engineering Ecole Nationale Supérieure des Technologies Avancées Algiers Algeria

This study presents a comparative analysis of Direct Torque Control with Space Vector Modulation (DTC-SVM) and FiniteControl Set Model Predictive Control (FCS-MPC) applied to five-phase induction motors. Five-phase induction motors offer enhancedperformance, reliability, and efficiency over traditional three-phase motors, making them suitable for high-reliability applications. Theperformance of DTC-SVM and FCS-MPC is evaluated through experimental implementation on a 3.5 kW five-phase induction motor,focusing on both dynamic response during speed reference changes and load variations, and static response, under steady-state conditions,as well as energy quality, specifically stator voltage and current. Experimental results show that FCS-MPC provides superior dynamicresponse, effectively managing speed changes and load variations, while DTC-SVM, owing to its fixed switching frequency, excelsat reducing torque ripple and minimizing stator current harmonics. The choice between DTC-SVM and FCS-MPC depends on theapplication's needs, weighing dynamic performance, torque stability, and harmonic content. This study provides valuable insights foroptimizing five-phase induction motor control and encourages future research to refine these methods or develop hybrid approaches thatcombine their strengths. © (2024), (Electromagnetics Academy). All rights reserved.

关键词： Model predictive control

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Privacy-preserving explainable AI: a survey

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Science China(Information Sciences) 2025年第1期68卷 23-56页

作者： Thanh Tam NGUYEN Thanh Trung HUYNH Zhao REN Thanh Toan NGUYEN Phi Le NGUYEN Hongzhi YIN Quoc Viet Hung NGUYEN School of Information and Communication Technology Griffith University School of Computer and Communication Sciences Ecole Polytechnique Federale de Lausanne Faculty of Mathematics and Computer Science University of Bremen Faculty of Information Technology HUTECH University Department of Computer Science Hanoi University of Science and Technology School of Electrical Engineering and Computer Science The University of Queensland

As the adoption of explainable AI(XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and their countermeasures. Our contribution to this field comprises a thorough analysis of research papers with a connected taxonomy that facilitates the categorization of privacy attacks and countermeasures based on the targeted explanations. This work also includes an initial investigation into the causes of privacy leaks. Finally, we discuss unresolved issues and prospective research directions uncovered in our analysis. This survey aims to be a valuable resource for the research community and offers clear insights for those new to this domain. To support ongoing research, we have established an online resource repository, which will be continuously updated with new and relevant findings.

关键词： privacy-preserving explainable AI privacy attacks privacy defences PrivEx PPXAI

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Fault Ride-Through Strategy for MMC-HVDC System Under Single Line-to-Ground Fault

Fault Ride-Through Strategy for MMC-HVDC System Under Single...

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International Conference on Artificial Intelligence in Renewable Energetic Systems, IC-AIRES 2024

作者： Korzane, Hicham Benrabah, Abdeldjabar Khoucha, Farid Benmansour, Khelifa Department of Electrical Engineering Ecole Militaire Polytechnique Algiers Algeria Department of Electrical Engineering University of Medea Medea Algeria

ISBN: (纸本)9783031803000

This paper investigates the single-line-to-ground (SLG) faults that can occur on the valve side of modular multilevel converter (MMC) stations used for integrating offshore wind farms. The grounding faults at the onshore converter installations result in significant threats that could compromise the stable performance of the power system. Furthermore, the paper also explores how SLG faults influence the DC link voltage and impact the wind farm operation. To address this issue, a control strategy using zero-sequence voltage modulation is used to reduce overvoltage in the DC voltage from such faults. Finally, MATLAB/Simulink simulations are carried out to verify the mathematical model of valve-side SLG faults and validate the effectiveness of the proposed control strategy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： MATLAB

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Emergency events detection based on integration of federated learning and active learning

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International Journal of Information Technology (Singapore) 2023年第6期15卷 2863-2876页

作者： Alfalqi, Khalid Bellaiche, Martine Computer and Software Engineering Department Ecole Polytechnique Montreal Canada

Social media networks now make it easy to access, in real-time, massive amounts of information from all over the world. They are often the primary source of information for billions of people. In addition to other uses, social media platforms can be valuable to provide real-time information about emergency events and can help detect emergency situations. This can provide affected individuals with crucial information about imminent dangers, prompting them to take appropriate action and significantly influencing their decisions. The continuous flow of data generated during these emergencies requires efficient training and modeling. Deep learning can simplify this process and make it less reliant on feature extraction strategies. However, implementing deep learning-based solutions can be expensive and time-consuming due to the vast amounts of data involved. To address these limitations, in this paper, we employed and analyzed the effectiveness of utilizing Active Learning techniques along with Federated Learning for emergency events using a dataset garnered from the social media. After collecting images, we used a Federated Learning paradigm to split the data amongst different clients and used vision transformers as local models for each client. Furthermore, we used an ensemble approach to integrate the results from these strengthened local and global models in a novel way, demonstrating that the proposed setup yields an accuracy of 99%. The Logloss score reached 0.043, indicating the outstanding performance of our approach. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management.

关键词： Active learning Emergency event detection Federated learning Object detection Snapchat Social networks Twitter Vision transformers ViT

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Study and Implementation of DTC-SVM Control of Five-Phase Induction Motor Fed by Five-Phase Voltage Source Inverter 3

Study and Implementation of DTC-SVM Control of Five-Phase In...

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3rd International Conference on Advanced electrical engineering, ICAEE 2024

作者： Hoggui, Abedelfattah Benkouachi, Khoutir Benachour, Ali Mahmoudi, Mohand Oulhadj Ecole Nationale Polytechnique Department of Electrical Engineering Algiers Algeria Ecole Supérieure des Sciences Appliquées Department of Electrical Engineering Algiers Algeria

ISBN: (纸本)9798350349351

This paper introduces a well-known Direct Torque Control-Space Vector Modulation (DTC-SVM) approach for controlling a five-phase induction motor powered by a two-level, five-phase voltage source inverter. The proposed method employs two Proportional-Integral (PI) controllers to manage flux and torque. To assess the static and dynamic capabilities of the DTC-SVM technique, simulations were conducted using MATLAB/Simulink. Experimental trials were carried out on a custom-built test bench in the LCP laboratory of ecole Nationale polytechnique to corroborate the simulation outcomes and evaluate the actual efficacy of the control system. The DTC-SVM strategy demonstrated robust electrical and electromagnetic performance, adopting a constant switching frequency to mitigate a common issue associated with traditional DTC systems. © 2024 IEEE.

关键词： Vector spaces

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Data-driven Reactive Power Optimization of Distribution Networks via Graph Attention Networks

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Journal of Modern Power Systems and Clean Energy 2024年第3期12卷 874-885页

作者： Wenlong Liao Dechang Yang Qi Liu Yixiong Jia Chenxi Wang Zhe Yang Wind Engineering and Renewable Energy Laboratory Ecole Polytechnique Federale de Lausanne(EPFL)Lausanne 1015Switzerland the College of Information and Electrical Engineering China Agricultural UniversityBeijing 100083China the College of Electrical Engineering and Automation Shandong University of Science and TechnologyQingdao 266590China the Department of Electrical and Electronic Engineering(Energy Digitalization Laboratory) The University of Hong KongHong KongChina the Department of Electrical Engineering The Hong Kong Polytechnic UniversityHong KongChina

Reactive power optimization of distribution networks is traditionally addressed by physical model based methods,which often lead to locally optimal solutions and require heavy online inference time *** improve the quality of the solution and reduce the inference time burden,this paper proposes a new graph attention networks based method to directly map the complex nonlinear relationship between graphs(topology and power loads)and reactive power scheduling schemes of distribution networks,from a data-driven *** graph attention network is tailored specifically to this problem and incorporates several innovative features such as a self-loop in the adjacency matrix,a customized loss function,and the use of max-pooling ***,a rulebased strategy is proposed to adjust infeasible solutions that violate *** results on multiple distribution networks demonstrate that the proposed method outperforms other machine learning based methods in terms of the solution quality and robustness to varying load ***,its online inference time is significantly faster than traditional physical model based methods,particularly for large-scale distribution networks.

关键词： Reactive power optimization graph neural network distribution network machine learning data-driven

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Enhancing Renewable Energy Integration:A Gaussian-Bare-Bones Levy Cheetah Optimization Approach to Optimal Power Flow in electrical Networks

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computer Modeling in engineering & Sciences 2024年第8期140卷 1339-1370页

作者： Ali S.Alghamdi Mohamed A.Zohdy Saad Aldoihi Department of Electrical Engineering College of EngineeringMajmaah UniversityAl-Majmaah11952Saudi Arabia Electrical and Computer Engineering Department Oakland UniversityRochesterMI48309USA Institute of Earth and Space Science King Abdulaziz City for Science and Technology(KACST)Riyadh11442Saudi Arabia Computer Science and Systems Engineering Institute Polytechnique de ParisPalaiseau CedexFrance

In the contemporary era,the global expansion of electrical grids is propelled by various renewable energy sources(RESs).Efficient integration of stochastic RESs and optimal power flow(OPF)management are critical for network *** study introduces an innovative solution,the Gaussian Bare-Bones Levy Cheetah Optimizer(GBBLCO),addressing OPF challenges in power generation systems with stochastic *** primary objective is to minimize the total operating costs of RESs,considering four functions:overall operating costs,voltage deviation management,emissions reduction,voltage stability index(VSI)and power loss ***,a carbon tax is included in the objective function to reduce carbon *** scrutiny,using modified IEEE 30-bus and IEEE 118-bus systems,validates GBBLCO’s superior performance in achieving optimal *** results demonstrate GBBLCO’s efficacy in six optimization scenarios:total cost with valve point effects,total cost with emission and carbon tax,total cost with prohibited operating zones,active power loss optimization,voltage deviation optimization and enhancing voltage stability index(VSI).GBBLCO outperforms conventional techniques in each scenario,showcasing rapid convergence and superior solution ***,GBBLCO navigates complexities introduced by valve point effects,adapts to environmental constraints,optimizes costs while considering prohibited operating zones,minimizes active power losses,and optimizes voltage deviation by enhancing the voltage stability index(VSI)*** research significantly contributes to advancing OPF,emphasizing GBBLCO’s improved global search capabilities and ability to address challenges related to local *** emerges as a versatile and robust optimization tool for diverse challenges in power systems,offering a promising solution for the evolving needs of renewable energy-integrated power grids.

关键词： Renewable energy integration optimal power flow stochastic renewable energy sources gaussian-bare-bones levy cheetah optimizer electrical network optimization carbon tax optimization

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An experimental proof of concept of time-reversal single-sensor acoustic source localisation:Application to partial discharges

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High Voltage 2025年第2期10卷 351-361页

作者： Hamidreza Karami Zhaoyang Wang Parsa Khorasani Javad Zohrevand Marcos Rubinstein Farhad Rachidi Institute for Information and Communication Technologies University of Applied Sciences and Arts Western Switzerland(HES-SO)Yverdon-les-BainsSwitzerland Ecole Polytechnique Fédérale de Lausanne(EPFL) Electromagnetic Compatibility LaboratoryLausanneSwitzerland Department of Electrical and Electronic Engineering Imperial College LondonLondonUK State Key Laboratory of Electrical Insulation and Power Equipment School of Electrical EngineeringXi'an Jiaotong UniversityXi'anChina Department of Electrical Engineering Bu-Ali Sina UniversityHamedanIran

In this paper,for the first time,an experimental proof of concept for utilising time reversal(TR)in the acoustic regime to localise partial discharge(PD)sources was presented using a single *** achieve this,an experimental setup comprising a water tank,acoustic transducers for signal transmission and reception,and 2D scanners was *** performance of the proposed method was evaluated across various scenarios,encompassing different PD source locations and sensor placements,barriers obstructing the line of sight between the receiving sensor and PD sources,varying levels of noise,and different frequency bandwidths for the PD *** experimental results demonstrate a near-zero localisation error in all considered *** achieved resolution was approximately half of the minimum ***,the acoustic TR method exhibits remarkable precision in source localisation,even when faced with obstacles such as a multi-layered metallic cylinder and in scenarios lacking a direct line of sight between the sensor and the *** accuracy of acoustic TR's localisation remained robust in the presence of noise,showcasing resilience at signal-to-noise ratio levels as low as−20 ***,the performance of the acoustic TR method remained consistent across a broad frequency spectrum,spanning from 60 to 200 kHz.

关键词： single sensor d scanners localise partial time reversal water tankacoustic transducers acoustic regime proof concept partial discharge

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Implementing and Evaluating a P4-based Access Gateway Function on a Tofino Switch 6

Implementing and Evaluating a P4-based Access Gateway Functi...

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6th International Conference on Advanced Communication Technologies and Networking, CommNet 2023

作者： Makhroute, El-Mehdi Elharti, Mohammed-Ayoub Brouillard, Vincent Savaria, Yvon Ould-Bachir, Tarek Polytechnique Montréal Department of Computer and Software Engineering Montreal Canada Polytechnique Montréal Department of Electrical Engineering Montreal Canada

ISBN: (纸本)9798350329391

This paper introduces an Access Gate Function (AGF) that has been implemented using the P4 language and evaluates its performance when running on a Tofino switch. Through the process of translating specifications from the Broadband Forum (BBF) for Fixed Mobile Convergence in the 5G Core into a P4 code and by conducting data plane simulations on software switch models, we provide evidence that the AGF solution conforms to industry standards and exhibits efficient and scalable performance characteristics. Subsequently, we assess the prototype's implementation on a Tofino switch, analyzing its capabilities and limitations. The reported results demonstrate that the programmable P4 Tofino switch efficiently handles a significant number of sessions, further reinforcing the potential and practicality of the proposed solution for industrial deployment in fixed-mobile convergence scenarios. © 2023 IEEE.

关键词： 5G mobile communication systems

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Enabling Human Activity Recognition in Smart Public Transportation Systems in Presence of Dataset Imbalance 30

Enabling Human Activity Recognition in Smart Public Transpor...

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30th IEEE International Conference on Electronics, Circuits and Systems, ICECS 2023

作者： Alizadeh, Roya Savaria, Yvon Nerguizian, Chahé École Polytechnique de Montréal Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350326499

A passenger detection method is required for smart public transportation systems. Such a method would enable control and orchestration of transit routes and schedules. To validate the method we proposed to differentiate people who appear to get into a transportation vehicle from others, we used an imbalanced WiFi-based dataset. However, the imbalanced dataset which includes majority and minority classes in the training set affects the performance of machine learning algorithms. Unreliable samples in the majority class can perturb the minority class. This paper proposes a method to efficiently classify imbalanced datasets. Then we explore the impact on classification performance of the imbalance observed in the WiAR dataset in relation to feature selection in a proposed machine-learning-based classification algorithm. The results show that the proposed method improves the F1-score performance for the minority class from 46% to 95%. © 2023 IEEE.

关键词： Classification (of information)

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