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Virtual Power Plants for Grid Resilience: A Concise Overview of Research and Applications

IEEE/CAA Journal of Automatica Sinica 2024年第2期11卷 329-343页

作者： Yijing Xie Yichen Zhang Wei-Jen Lee Zongli Lin Yacov A.Shamash IEEE Department of Electrical Engineering University of Texas at ArlingtonArlingtonTX 76019 USA Charles L.Brown Department of Electrical and Computer Engineering University of VirginiaCharlottesvilleVA 22904 USA Department of Electrical and Computer Engineering Stony Brook UniversityStony BrookNY 11794 USA

The power grid is undergoing a transformation from synchronous generators(SGs) toward inverter-based resources(IBRs). The stochasticity, asynchronicity, and limited-inertia characteristics of IBRs bring about challenges to grid resilience. Virtual power plants(VPPs) are emerging technologies to improve the grid resilience and advance the transformation. By judiciously aggregating geographically distributed energy resources(DERs) as individual electrical entities, VPPs can provide capacity and ancillary services to grid operations and participate in electricity wholesale markets. This paper aims to provide a concise overview of the concept and development of VPPs and the latest progresses in VPP operation, with the focus on VPP scheduling and control. Based on this overview, we identify a few potential challenges in VPP operation and discuss the opportunities of integrating the multi-agent system(MAS)-based strategy into the VPP operation to enhance its scalability, performance and resilience.

关键词： Climate change renewable energy resources resilience smart grids virtual power plants(VPPs)

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Asymmetrical Fault Current Calculation Method and Influencing Factors Analysis of Droop-Controlled Inverters

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CSEE Journal of Power and Energy Systems 2024年第2期10卷 562-573页

作者： Huimin Zhao Zhikang Shuai Jun Ge An Luo Weimin Wu Z.John Shen College of Electrical and Information Engineering Hunan UniversityChangsha 410006China Electrical Engineering Department Shanghai Maritime UniversityShanghai 201306China Department of Electrical and Computer Engineering Ilinois Institute of TechnologyChicagoIL 60616 USA

Since the fault dynamic of droop-controlled inverter is different from synchronous generators (SGs), protection devices may become invalid, and the fault overcurrent may damage power electronic devices and threaten the safety of the microgrid. Therefore, it is imperative to conduct a comprehensive fault analysis of the inverter to guide the design of protection schemes. However, due to the complexity of droop control strategy, existing literatures have simplified asymmetric fault analysis of droop-controlled inverters to varying degrees. Therefore, accurate fault analysis of a droop-controlled inverter is needed. In this paper, by analyzing the control system, an accurate fault model is established. Based on this, a calculation method for instantaneous asymmetrical fault current is proposed. In addition, the current components and current characteristics are analyzed. It was determined that fault currents are affected by control loops, fault types, fault distance and nonlinear limiters. In particular, the influences of limiters on the fault model, fault current calculation and fault current characteristics were analyzed. Through detailed analysis, it was found that dynamics of the control loop cannot be ignored, the fault type and fault distance determine fault current level, and part of the limiters will totally change the fault current trend. Finally, calculation and experimental results verify the correctness of the proposed method.

关键词： Asymmetrical fault analysis droop control fault current calculation method limiter transient fault characteristics

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Supervised Learning for Real-time Model Predictive Control Leveraging Multi-parametric Mixed Integer Quadratic Programming

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Journal of Institute of Control, Robotics and Systems 2025年第2期31卷 98-105页

作者： Yoo, Seungjun Gwon, Minwoo Kim, Kwang-Ki Department of Electrical and Computer Engineering Inha University Korea Republic of

This paper presents a novel supervised learning framework for real-time optimization of multi-parametric mixed-integer quadratic programming (mp-MIQP) problems. The framework utilizes a multi-layer perceptron (MLP) model to efficiently predict both continuous and binary control inputs while classifying the feasibility of the optimization problem. To address the computational burden of branch-and-bound methods and the memory limitations of explicit model predictive control (MPC), this framework learns optimal control inputs across diverse system states and prediction horizons through offline training. Comparative evaluations reveal that the proposed method significantly outperforms traditional approaches in computational speed and memory efficiency while maintaining high accuracy in approximating optimal solutions. These advancements position the framework as a transformative solution for real-time control applications, offering a robust and efficient alternative for online optimization in mp-MIQP problems. © ICROS 2025.

关键词： Mixed-integer linear programming

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Semantic Segmentation Using an Improved ResNet Structure and Efficient Channel Attention Mechanism Applied to Atrous Spatial Pyramid Pooling in a Fully Convolutional Network

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International Journal of engineering, Transactions B: Applications 2025年第11期38卷 2511-2526页

作者： Sahragard, E. Farsi, H. Mohamadzadeh, S. Department of Electrical and Computer Engineering University of Birjand Birjand Iran

In the field of computer vision, semantic segmentation became an important problem that has applications in fields such as autonomous driving and robotics. Image segmentation datasets, on the other hand, present substantial hurdles due to the high intra-class variability, which includes differences across car models or building designs, and the low inter-class variability, which makes it difficult to discern between objects such as buildings that have facades that are visually identical. A focus-enhanced ASPP module that is coupled with an upgraded backbone for semantic segmentation networks is presented in this study as a solution to the problems that have been identified. In order to augment the adaptability of extracted features, the proposed framework utilizes the capability of an attention ASPP module to implement attention processes within the multiscale module. In order to efficiently capture complex features, the encoder stage also makes use of a ResNet-50 backbone that has been properly optimized. In addition, to increase the robustness of the model, data augmentation approaches are applied. mDice of 87.82, mIoU of 79.05, and mean accuracy of 85.2 on the Stanford dataset, and mDice of 88.91, mIoU of 80.03, and mean accuracy of 89.84 on the Cityscapes dataset, according to experimental assessments, demonstrate that the developed technique performs at an accuracy level that is believed to be modern. As a result of these findings, the possibility for greatly improving semantic segmentation performance may be highlighted by integrating attention mechanisms, ASPP modules, and upgraded ResNet structures. ©2025 The author(s).

关键词： Semantic Segmentation

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Optimizing BERT for Bengali Emotion Classification: Evaluating Knowledge Distillation, Pruning, and Quantization

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computer Modeling in engineering & Sciences 2025年第2期142卷 1637-1666页

作者： Md Hasibur Rahman Mohammed Arif Uddin Zinnat Fowzia Ria Rashedur M.Rahman Department of Electrical and Computer Engineering North South UniversityDhaka1229Bangladesh

The rapid growth of digital data necessitates advanced natural language processing(NLP)models like BERT(Bidi-rectional Encoder Representations from Transformers),known for its superior performance in text ***,BERT’s size and computational demands limit its practicality,especially in resource-constrained *** research compresses the BERT base model for Bengali emotion classification through knowledge distillation(KD),pruning,and quantization *** Bengali being the sixth most spoken language globally,NLP research in this area is *** approach addresses this gap by creating an efficient BERT-based model for Bengali *** have explored 20 combinations for KD,quantization,and pruning,resulting in improved speedup,fewer parameters,and reduced memory *** best results demonstrate significant improvements in both speed and *** instance,in the case of mBERT,we achieved a 3.87×speedup and 4×compression ratio with a combination of Distil+Prune+Quant that reduced parameters from 178 to 46 M,while the memory size decreased from 711 to 178 *** results offer scalable solutions for NLP tasks in various languages and advance the field of model compression,making these models suitable for real-world applications in resource-limited environments.

关键词： Bengali NLP black-box distillation emotion classification model compression post-training quantization unstructured pruning

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Learning-based user association and dynamic resource allocation in multi-connectivity enabled unmanned aerial vehicle networks

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Digital Communications and Networks 2024年第1期10卷 53-62页

作者： Zhipeng Cheng Minghui Liwang Ning Chen Lianfen Huang Nadra Guizani Xiaojiang Du Department of Information and Communication Engineering Xiamen UniversityXiamen361005China School of Electrical and Computer Engineering University of Texas at ArlingtonArlingtonTX76019USA Department of Electrical and Computer Engineering Stevens Institute of TechnologyHobokenNJ07030USA

Unmanned Aerial Vehicles(UAvs)as aerial base stations to provide communication services for ground users is a flexible and cost-effective paradigm in ***,dynamic resource allocation and multi-connectivity can be adopted to further harness the potentials of UAVs in improving communication capacity,in such situations such that the interference among users becomes a pivotal disincentive requiring effective *** this end,we investigate the Joint UAV-User Association,Channel Allocation,and transmission Power Control(J-UACAPC)problem in a multi-connectivity-enabled UAV network with constrained backhaul links,where each UAV can determine the reusable channels and transmission power to serve the selected ground *** goal was to mitigate co-channel interference while maximizing long-term system *** problem was modeled as a cooperative stochastic game with hybrid discrete-continuous action space.A Multi-Agent Hybrid Deep Reinforcement Learning(MAHDRL)algorithm was proposed to address this *** simulation results demonstrated the effectiveness of the proposed algorithm and showed that it has a higher system utility than the baseline methods.

关键词： UAV-user association Multi-connectivity Resource allocation Power control Multi-agent deep reinforcement learning

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Realtime and Integrated Framework for LiDAR-based Object Tracking

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Journal of Institute of Control, Robotics and Systems 2025年第3期31卷 196-205页

作者： Lee, Gyuseok Kim, Kana Lee, Jejun Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of

This study proposes a real-time integrated framework for LiDAR-based object tracking in autonomous driving environments. Advancements in LiDAR sensors are increasing point cloud data collection, leading to a demand for reliable real-time processing methods. The proposed framework applies voxelization and ground removal techniques to reduce computational load and integrates clustering and deep learning-based object recognition to ensure stability. Combining the point cloud data from LiDAR and the IMU data corrects distortions and refines real-time object movement, enabling accurate tracking in dynamic environments. This framework supports a maximum detection range of 100 m, with a computation time of 52 ms, a positional error of 1.06 m, a heading error of 3.79°, a relative velocity error of 1.46 m/s, and an average tracking frame count of 101, thereby improving object recognition accuracy and tracking performance while fulfilling real-time processing requirements. © ICROS 2025.

关键词： 3D object detection autonomous driving LiDAR object tracking

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Impact of Memory Motors on an IGBT-Based Inverter Efficiency

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IEEE Transactions on Industry Applications 2025年第3期61卷 4214-4225页

作者： Aljehaimi, Akrem M. Abdel-Mageed, Bassam Samy Pillay, Pragasen Department of Electrical and Computer Engineering Concordia University Montreal Canada

This paper investigates the impact of hybrid memory motors on an IGBT-based inverter in the field-weakening region. Hybrid variable-flux motors have been introduced to overcome the limitations of conventional IPMSMs in traction applications. Different memory motor topologies have been introduced and compared in the literature. Yet, their impact on the drive inverter has not been reported. An existing prototyped ten-horsepower series-hybrid variable motor has been used as a case study in this paper. In the field weakening region, comparing the operation with partially demagnetized magnets to fully magnetized magnets, it is found that not only does the motor current decrease, but the motor power factor also improves. This unique finding of improved power factor operation positively impacts the inverter conduction loss. This has significant practical implications, suggesting that hybrid memory motors can lead to more efficient traction inverters. An air-cooled IGBT-based three-phase 750 VDC, 30 ARMS, is used in this study. In the simulation, the switch characteristics from the datasheet have been used for loss calculations. Also, the inverter power loss and efficiency have been measured at different speeds, loads, and magnetization states. A two to three percent improvement in inverter efficiency was noted in the high-speed region beyond two per unit speed. © 2025 IEEE.

关键词： Permanent magnets

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Video captioning using transformer-based GAN

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Multimedia Tools and Applications 2025年第10期84卷 7091-7113页

作者： Babavalian, Mohammad Reza Kiani, Kourosh Electrical and Computer Engineering Department Semnan University Semnan Iran

Video captioning is the process of automatically generating natural language descriptions of video content. Historically, most video captioning methods have relied on extending Sequence-to-Sequence (Seq2Seq) models. However, such approaches possess limitations due to the sequential nature of the captions, which leads to less accurate captions. To address this limitation, this paper introduces a novel end-to-end architecture for video captioning that combines conditional Wasserstein Generative Adversarial Networks (cWGAN) with a transformer model. The proposed architecture consists of two modules: feature extraction and caption generation. The feature extraction module aims to obtain an encoded feature vector representing the video contents, while the caption generation module generates human-readable captions from encoded feature vector. To the best of our knowledge, this is the first architecture for generative video captioning that integrates a transformer model with GAN. The results of the proposed model based on the BLEU-4, METEOR, ROUGE-L, and CIDEr metrics, on two datasets, MSVD (BLEU-4 = 61.2, METEOR = 41.6) and MSR-VTT (BLEU-4 = 61.2, METEOR = 31.1), compared to state-of-the-art approaches, demonstrate the effectiveness of the transformer with generative model in generating accurate and human-readable captions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Generative adversarial networks

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MCVL: Multi-directional Camera-based Visual Localization Resistant to Seasonal and Illumination Changes

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Journal of Institute of Control, Robotics and Systems 2025年第2期31卷 153-159页

作者： Mun, Giyoung Kim, Hakil Department of Electrical and Computer Engineering Inha University Korea Republic of

The global navigation satellite system-based technology has inherent limitations due to its reliance on radio signals. In contrast, visual localization operates independently of radio communication, presenting a viable solution to overcome these limitations. However, it is susceptible to seasonal and illumination variations, highlighting the need for research to address these challenges. Therefore, this paper proposes the multi-directional camera-based visual localization, which is robust against seasonal and illumination changes. The proposed method combines image from multiple directions and extracts global deep learning features. Subsequently, local deep learning features are extracted to preserve the characteristics of each combined image, allowing for the identification of geographically similar images. This approach utilize multi-directional cameras, enabling resilient performance under various constraints. Moreover, it demonstrates an improvement of 7.56% in recall rate at 1-meter threshold compared to existing methods. © ICROS 2025.

关键词： Global positioning system

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