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Fair and Efficient Congestion Management for Low Voltage Distribution Networks 8

Fair and Efficient Congestion Management for Low Voltage Dis...

引用

8th IEEE International Energy Conference, ENERGYCON 2024

作者： Vadavathi, Aswin Ramesh Hoogsteen, Gerwin Hurink, Johann dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede Netherlands

ISBN: (纸本)9798350382150

The widespread increase of photovoltaic panels in the distribution grid often leads to overvoltage. Traditional voltage regulation techniques are unfair for prosumers at the sensitive part of the grid due to larger power curtailment. Consequently, there has been considerable research on implementing fair curtailment strategies for congestion management. However, existing fairness-incorporated curtailment approaches yield varying results based on changes in the grid topology and integration of renewable energy sources. Therefore, this paper introduces proportional voltage fairness that is independent of the above-mentioned network properties and inherently distinguishes regions for fair curtailment. Additionally, there is a need to consider the underutilisation of the grid while adhering to a fair curtailment scheme. Thus this paper proposes a control algorithm incorporating proportional voltage fairness and gives the opportunity to specify the trade-off between fairness and the total power curtailed. Further, a comparative analysis with respect to state-of-the-art notions of fairness is performed. ©2024 IEEE.

关键词： Electric power distribution

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Enhancing Vehicle Routing with Time Windows: A Machine Learning-Driven Ant Colony Optimization Approach

Enhancing Vehicle Routing with Time Windows: A Machine Learn...

引用

2024 International Conference on Frontiers of Information Technology, FIT 2024

作者： Tariq, Khizer Safdar, Ali Awais Zaman, Mubashir Shah, Syed Usman Ali School of Electrical Engineering and Computer Science National University of Sciences & Technology Islamabad Pakistan

ISBN: (纸本)9798331510503

The Vehicle Routing Problem with Time Windows (VRPTW) is a challenging optimization problem in logistics and transportation, where the objective is to efficiently plan routes for vehicles to service a set of customers within specified time windows while minimizing costs. This paper introduces a novel approach to solving VRPTW using a Machine Learning-Driven Ant Colony Optimization (ML-ACO) algorithm, referred to as Machine Learning-Driven Ant Colony Optimization for Vehicle Routing with Time Windows (ML-ACO-VRPTW). The proposed algorithm enhances traditional ACO by integrating a machine learning model, to predict heuristic values that capture the urgency of time windows and vehicle capacity constraints. The algorithm begins with initializing pheromone levels and parameters, followed by constructing routes based on probabilistic path selection influenced by pheromone intensity and machine learning-predicted heuristic values. Key innovations include dynamic pheromone updates that adapt to the algorithm’s progress and the incorporation of penalties for time window violations and vehicle capacity exceedances. The pheromones are used to maintain a memory of the paths, and the machine learning model ensures adaptive and accurate heuristic predictions. © 2024 IEEE.

关键词： Ant colony optimization

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Impedance Emulation Control of Wave Energy Converters 50

Impedance Emulation Control of Wave Energy Converters

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Chandran, Pranav Kim, InYong Brekken, Ted Johnson, B. Chandra Family Department of Electrical and Computer Engineering University of Texas at Austin AustinTX78701 United States School of Electrical Engineering and Computer Science CorvallisOR97331 United States

ISBN: (纸本)9781665464543

Modeling and control of wave energy conversion (WEC) systems for maximum power extraction is challenging due to complex multiphysics that include fluids, mechanics, and machine drives. To uncover an intuitive model that clearly depicts WEC system operation, we utilize a force-current equivalent circuit framework which then enables us to design an impedance emulation control strategy. To provide context for our framework, we focus on a standard reference model-3 (RM3) point absorber device coupled to a permanent magnet synchronous generator. After the proposed extremum seeking controller is computed, we experimentally validate its performance on a platform that consists of two back-to-back connected inverters that emulate the WEC system. © 2024 IEEE.

关键词： Wave energy conversion

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Implications of EU Battery Regulation on Circular Economy: Barriers and Opportunities for Repurposing Electric Vehicle Batteries

Implications of EU Battery Regulation on Circular Economy: B...

引用

2024 IEEE PES Innovative Smart Grid Technologies Europe Conference, ISGT EUROPE 2024

作者： Lin, Yi-Chun Beyene, Yared Bekele Tjernberg, Lina Bertling Kth Royal Institute of Technology School of Electrical and Computer Science Stockholm Sweden Addis Ababa University School of Electrical and Computer Engineering Addis Ababa Ethiopia

ISBN: (纸本)9789531842976

This paper presents a study of the impact of EU battery regulations, specifically Regulation (EU) 2023/1542, on end-of-life batteries, primarily focusing on repurposing within the supply chain. Qualitative data was gathered from stakeholders in the battery supply chain using a combination of surveys and interviews. Interviews provide deeper insights into aspects beyond the survey's scope, enhancing our understanding of how EU regulations influence the supply chain. The findings reveal a nuanced landscape where EU regulations bring advantages through defined producer responsibilities, improved safety standards, and facilitated data sharing. The study explores various circular business models within this context, emphasizing the need for a functional value network and effective stakeholder coordination. This research contributes valuable insights as the EU pursues circularity in the battery supply chain. By addressing barriers, capitalizing on opportunities, and promoting collaboration, it supports a sustainable and circular future in the EV battery supply chain, optimizing initial investments and aligning with the energy transition goals. © 2024 IEEE.

关键词： Battery Pack

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High-Performance Face Identification Using Randomly Selected Channels from Hyperspectral Images

High-Performance Face Identification Using Randomly Selected...

引用

2024 IEEE International Conference on Imaging Systems and Techniques, IST 2024

作者： Furusawa, Sota Premachandra, Chinthaka Graduate School of Engineering and Science Shibaura Institute of Technology Dept. of Electrical Engineering and Computer Science Tokyo Japan

ISBN: (纸本)9798350378214

With the recent development of information technology, the importance of protecting personal information has increased. Because of the vulnerability in passwords, biometric authentication is now being used as a method of personal information protection. However, biometric authentication has the possibility of malicious authentication due to the emergence of technology that can generate biometric data that is difficult to duplicate. Therefore, this study aimed to solve the security problems posed by Generative Adversarial Networks (GANs) in biometric authentication by using Hyperspectral Images (HSI). We searched for solutions to the problems identified in previous studies, such as the inability to correctly identify individuals who have not been previously learned and the length of time required for identification. In this paper, we used three channels of data randomly selected from hyperspectral face images for learning to identify individuals by binary classification into 'learned subject (class)' and 'unlearned subjects (classes)'. In this case, discriminators based on the least-squares generative adversarial network (LSGAN) model was developed for each class using three randomly selected channels of data from the HSI for two class classification. The proposed method significantly reduced the identification time and achieved very high discrimination accuracy in all classes. In addition, the results showed high accuracy throughout multiple face identification experiments. © 2024 IEEE.

关键词： Generative adversarial networks

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Comparative Analysis of ML Models for Power Prediction in Complex 3D Digital Twins Using Ray Tracing Environment

Comparative Analysis of ML Models for Power Prediction in Co...

引用

2024 International Conference on computer and Applications, ICCA 2024

作者： Mahmud, Istiak Abdelhadi, Ahmed School of Electrical Engineering & Computer Science University of North Dakota ND United States

ISBN: (纸本)9798350367560

In this study, we present the development of a comprehensive ray tracing dataset tailored for versatile 3D digital twin environments that closely mimic real-life scenarios. For precise modeling in complicated environments, this dataset includes crucial factors like phase shift, propagation distance, path loss, propagation delay, and the number of ray contacts or reflections. Using this dataset, we forecast received power and other relevant variables using a variety of machine learning (ML) techniques. To improve the data's suitability for machine learning models, we do feature extraction and exploratory data analysis to find patterns in the data. By comparing the performance and error metrics across different ML approaches, our analysis provides a robust framework for network developers seeking optimal ML strategies for intricate environments featuring real-life channels and objects. The results provide important information for choosing appropriate machine learning models for improved precision and effectiveness in ray tracing applications, ultimately promoting AI-driven solutions in network development and digital twin settings. © 2024 IEEE.

关键词： Adversarial machine learning

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A Phase Change Material Based Silicon Carbide Power Module Packaging 10

A Phase Change Material Based Silicon Carbide Power Module P...

引用

10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Rahman, Mohammad Dehan Song, Xiaoqing University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350351330

Most power electronics equipment like solar inverters or solid-state circuit breakers (SSCBs) require short-time overload current requirements, which subsequently necessitate the surge current capability of power semiconductor modules. Under overload current conditions, a significant amount of loss is generated inside the power modules within a short time period and heat up the semiconductor chip rapidly. To mitigate this, multiple power devices in parallel are usually needed which increases the circuit complexity and cost. An alternative way to address this is to increase the power module transient overload heat absorbing capability through advanced power module packaging techniques. In this paper, phase change materials (PCMs) are investigated and integrated in a silicon carbide (SiC) power module. With the PCM's latent heat capability, the SiC MOSFET power module is capable of providing an increased thermal capacity. The quantitative impact of using PCM on transient overload thermal performance is investigated. Finite element analysis model of SiC power module is built, and the junction temperature of the power module is simulated for both single-side and double-side cooling with PCM integrated. PCMs with different latent heat are also explored to study its effects on junction temperature. Experimental results using PCM validate the theoretical finite element model. Both the FEA and experimental analysis shows that use of PCM can significantly reduce temperature rise, thus helping in maintaining a more stable and controlled operating temperature. © 2024 IEEE.

关键词： Phase change materials

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AODS: Adaptive Obstacle Detection System for Smart Wheelchairs

AODS: Adaptive Obstacle Detection System for Smart Wheelchai...

引用

2024 IEEE International Conference on Control science and Systems engineering, ICCSSE 2024

作者： Li, Lucy Chen, Wenyu Chen, Keyu Electrical and Computer Engineering The Ohio State University ColumbusOH United States School of Computer Science and Engineering University of Electronic Science and Technology of China Sichuan Chengdu China

ISBN: (纸本)9798331517199

The rising global demand for smart mobility solutions underscores the critical need for advanced technologies in power wheelchairs to ensure the safety and independence of users with mobility challenges. This work introduces a novel segmentation-based pipeline for obstacle detection, specifically tailored for outdoor environments, to enhance navigational safety in smart wheelchairs. The segmentation model within the pipeline addresses prevalent issues in current technologies-such as variable lighting conditions, scale changes, and distance sensitivity-that significantly affect detection accuracy. The proposed model incorporates innovative components, including a Distance Augmentation Module (DAM), Scale Aware Module (SAM), and an Illumination Simulation Module (ISM), ensuring robust performance under diverse operational conditions. Validation on a specially curated dataset from urban settings in Chengdu, China, demonstrates a segmentation accuracy of 86.0%, out-performing existing solutions. The findings suggest substantial improvements in detecting and navigating around obstacles, thereby significantly enhancing the operational efficiency and safety of smart wheelchairs. This work not only contributes a sophisticated algorithmic approach and a tailored dataset to the field but also verifies the practical efficacy of the model through extensive experimental validation. © 2024 IEEE.

关键词： Obstacle detectors

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Power Density Limits of Propulsion Motor for Electric Aircraft: A Study on Insulation Thickness

Power Density Limits of Propulsion Motor for Electric Aircra...

引用

2024 International Conference on electrical Machines, ICEM 2024

作者： Wang, Jundong Dong, Jianning Niasar, Mohamad Ghaffarian Zhu, Gangwei Bauer, Pavol Delft University of Technology Department of Electrical Engineering Mathematics and Computer Science Delft Netherlands

ISBN: (纸本)9798350370607

This paper studies the power density limits of propulsion motor for electric aircraft considering thermal aspects and breakdown voltage reduction of insulation. The study em-ploys multi-objective optimization (MOO) to explore various mo-tor cooling options and filter configurations. The results show that motors with direct winding heat exchanger (DWHE) can reach higher specific power, while those equipped with water jacket cooling (WJC) offer a moderate design with simpler structure. Furthermore, the impact of sine wave and dv/dt filters on electric motors design is studied. The findings demonstrate that dv/dt filters enable designs with higher overall specific power compared to sine wave filters. Through simulations, this study identify the challenge faced by aviation motor design in significantly increased insulation thickness, necessitating advanced insulation materials with a minimum thermal conductivity of 5 W/(m.K) to facilitate a high specific power design. Based on this assumption, a preliminary design of 9.6 kW/kg with an efficiency of 98% is presented. © 2024 IEEE.

关键词： Thermal insulation

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Federated Learning under Sample Selection Heterogeneity

Federated Learning under Sample Selection Heterogeneity

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2024 IEEE International Conference on Big Data, BigData 2024

作者： Mai, Huy Wu, Xintao University of Arkansas Department of Electrical Engineering and Computer Science FayettevilleAR United States

ISBN: (纸本)9798350362480

Despite having benefits of privacy preservation and secure computation, federated learning (FL) faces the issue of data heterogeneity. Specifically, the performance of FL systems can be degraded due to sample selection heterogeneity. We define the sample selection heterogeneity scenario with two points. First, each local training set is subject to missing-not-at-random (MNAR) sample selection bias where some labels are non-randomly missing. This requires incorporating a sample selection model that utilizes two equations to account for the prediction and selection of samples. Choosing selection features is a challenging task, especially when the number of observed features is large. Second, the sample selection mechanism is not the same across all clients. This implies that clients do not share the same set of selection features. In this work, we propose FL-MNAR to address FL under sample selection heterogeneity. The framework integrates an existing sample selection model that robustly handles sample selection bias for each client. FL-MNAR also trains an assignment function that gives a set of selection features to each client based on how well the features fit selection. Experimental results show that FL-MNAR achieves state-of-the-art performance under sample selection heterogeneity. © 2024 IEEE.

关键词： Federated learning

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