IEEE Access最新文献_第2页

Driving Mechanisms of User Engagement With AI-Generated Content on Social Media Platforms: A Multimethod Analysis Combining LDA and fsQCA 社交媒体平台上人工智能生成内容的用户参与度驱动机制：LDA和fsQCA相结合的多方法分析

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3589286

Jiajun Hou;Hongju Lu;Baojun Wang

{"title":"Driving Mechanisms of User Engagement With AI-Generated Content on Social Media Platforms: A Multimethod Analysis Combining LDA and fsQCA","authors":"Jiajun Hou;Hongju Lu;Baojun Wang","doi":"10.1109/ACCESS.2025.3589286","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3589286","url":null,"abstract":"With the rapid development of artificial intelligence (AI) technologies, AI-generated content (AIGC) on social media platforms has significantly increased. This study collected text data related to AIGC from mainstream social media platforms and employed the Latent Dirichlet Allocation (LDA) topic model to uncover the thematic characteristics of AIGC. The analysis was further integrated with the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT) to identify seven key conditional variables: the maturity of AIGC technology, users’ perception of the authenticity of AIGC, users’ perception of the usefulness of AIGC, users’ perception of the entertainment value of AIGC, the commercialization level of AIGC, the personalization level of AIGC recommendations on the platform, and the ecosystem management and interaction atmosphere of AIGC on the platform. Using fuzzy-set Qualitative Comparative Analysis (fsQCA), this study identified seven configurational paths that drive user engagement with AIGC on social media platforms, which were ultimately summarized into three core pathways: user perception—platform recommendation pathway, user perception—platform atmosphere pathway, and technology characteristics—user perception—platform recommendation—platform atmosphere pathway. The results indicate that users’ perceptions of the usefulness of AIGC are a key factor in driving user engagement with AIGC on social media platforms.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"123994-124009"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080437","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Automated Business Decision-Making Using Generative AI in Online A/B Testing: Comparative Analysis With Human Decision-Making 在在线A/B测试中使用生成式AI的自动业务决策：与人类决策的比较分析

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3588480

Changhak Sunwoo;Hyunjin Kwon;Jong Min Kim;Ho-Hyun Lim;Yongwoo Kim;Dongwook Hwang;Jingoo Kim

{"title":"Automated Business Decision-Making Using Generative AI in Online A/B Testing: Comparative Analysis With Human Decision-Making","authors":"Changhak Sunwoo;Hyunjin Kwon;Jong Min Kim;Ho-Hyun Lim;Yongwoo Kim;Dongwook Hwang;Jingoo Kim","doi":"10.1109/ACCESS.2025.3588480","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3588480","url":null,"abstract":"Online A/B testing is widely used as an experimental methodology for product improvement and business optimization. However, interpreting experimental results often involves subjective judgment and biases from experiment designers, which can undermine the reliability and reproducibility of test outcomes. In particular, experiment designers frequently exhibit inconsistent decision-making when dealing with neutral results—cases where neither statistically significant positive nor negative effects are observed. This study aims to explore the feasibility of automating A/B test decision-making using Generative AI and empirically analyze how well AI decisions align with those of experiment designers and experts. Utilizing 1,407 experimental cases from 48 companies on the Hackle online experimentation platform, the study compares decision-making outcomes between experiment designers and Generative AI, analyzing agreement rates and identifying patterns across companies. Statistical analyses, including chi-square tests and inter-rater agreement evaluation, were employed to assess differences and reliability. The findings indicate meaningful discrepancies between AI and experiment designers but demonstrate that AI decisions closely align with expert judgments. These results suggest that Generative AI can serve as a complementary tool to enhance the consistency and reliability of A/B test result interpretation.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124530-124542"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079579","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671133","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

RAG-Driven Memory Architectures in Conversational LLMs—A Literature Review With Insights Into Emerging Agriculture Data Sharing 会话llms中ragd驱动的内存架构-对新兴农业数据共享的见解的文献综述

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3589241

Nur Arifin Akbar;Rahool Dembani;Biagio Lenzitti;Domenico Tegolo

{"title":"RAG-Driven Memory Architectures in Conversational LLMs—A Literature Review With Insights Into Emerging Agriculture Data Sharing","authors":"Nur Arifin Akbar;Rahool Dembani;Biagio Lenzitti;Domenico Tegolo","doi":"10.1109/ACCESS.2025.3589241","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3589241","url":null,"abstract":"Despite significant advances in natural language processing, conversational AI systems face persistent challenges in maintaining extensive and contextually coherent dialogues, particularly regarding long-term memory management. This literature review synthesizes current approaches to memory architectures in conversational AI, examining the transition from basic dialogue agents to more sophisticated, agentic frameworks. We analyze how vector databases and Retrieval-Augmented Generation (RAG) address fundamental challenges in storing and retrieving conversational context, maintaining system responsiveness, managing user-specific data ethically, and integrating domain-specific information. Through systematic review of papers, we identify critical limitations of vector embedding in capturing extended conversational context, particularly in agentic domains requiring semantic, episodic, procedural, and emotional memory. We evaluate how RAG frameworks can augment vector databases to handle memory-intensive tasks requiring real-time updates and domain-specific knowledge integration. Furthermore, we examine alternative architectures including knowledge graphs, finite state machines, and hybrid solutions, highlighting the data quality and ethical challenges that must be addressed for scalable, reliable AI memory management. Our analysis provides a structured framework for understanding memory evolution in conversational AI, identifies gaps in current RAG solutions, proposes hybrid memory designs, and outlines future research directions emphasizing cross-domain applications in agriculture.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"123855-123880"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671143","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning 基于因果推理和深度度量学习的少弹SAR目标识别

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3589192

Ke Wang;Yuqian Mao;Qi Qiao

{"title":"Few-Shot SAR Target Recognition via Causal Inference and Deep Metric Learning","authors":"Ke Wang;Yuqian Mao;Qi Qiao","doi":"10.1109/ACCESS.2025.3589192","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3589192","url":null,"abstract":"Deep learning, with large-scale annotated datasets, has demonstrated remarkable success in synthetic aperture radar automatic target recognition (SAR-ATR). However, the collecting of SAR images is expensive and complex, and manually labeling them requires expert knowledge. To overcome these limitations, we propose a few-shot learning model capable of accurate recognition of novel targets with minimal training samples. Our model innovatively integrates causal inference with mutual centralized learning (MCL) to address few-shot SAR-ATR tasks. First, we establish a causal inference framework to identify and model the dependencies among target characteristics, imaging conditions, and category labels. This framework incorporates a novel causal intervention method based on multi-scale random convolution to eliminate spurious correlations caused by imaging variations, thereby enhancing feature stability. Second, we introduce an advanced MCL module to effectively evaluate feature similarity in few-shot settings. MCL breaks through the unidirectional matching paradigm adopted by conventional metric learning. Through its bidirectional feature interactions and dense feature accessibility mechanisms, MCL achieves more robust feature discrimination in few-shot learning tasks. Comprehensive experiments demonstrate that our model outperforms existing advanced few-shot SAR-ATR methods, achieving superior recognition accuracy while maintaining robustness in data-scarce scenarios.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124988-125002"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080412","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin 数据科学中的人工智能：评估亚马逊流域太阳能预测模型

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3589275

André Luis Ferreira Marques;Ricardo Sbragio;Pedro Luiz Pizzigatti Corrêa;Marcelo Ramos Martins

{"title":"Artificial Intelligence in Data Science: Evaluating Forecasting Models for Solar Energy in the Amazon Basin","authors":"André Luis Ferreira Marques;Ricardo Sbragio;Pedro Luiz Pizzigatti Corrêa;Marcelo Ramos Martins","doi":"10.1109/ACCESS.2025.3589275","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3589275","url":null,"abstract":"Forecasting models employing machine learning (ML) and deep learning (DL) have become fundamental for assessing the technical feasibility of renewable energy systems. Among these, solar energy stands out as a renewable energy option, particularly relevant for supporting the preservation of the Amazon rainforest. This study introduces a novel approach using ML and DL methods—integrated with Universal Kriging and Holt-Winters (time series) models — to forecast solar irradiance (kWh/m2) in cities across the state of Amazonas. The analysis is grounded in the Data Science cycle, with input data sourced from both ground stations and satellite products. Forecasting performance was evaluated for short-term horizons (one to three days ahead) across three representative cities. The hybrid SARIMAX-CNN-LSTM, SARIMAX-CNN-Transformer, and SARIMAX-TCN models achieved MAPE values ranging from 18.1% to 26.6% for the different forecast horizons and cities. These results are consistent with existing literature and reinforce the suitability of advanced ML/DL approaches for solar energy forecasting in highly variable and challenging environments such as the Amazon Basin.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"125066-125079"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080416","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Permanent Magnet Assisted Brushless Wound Rotor Synchronous Machine With Improved Torque Characteristics 一种改进转矩特性的新型永磁辅助无刷绕线转子同步电机

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3588513

Muhammad Ahsan Ul Haq;Haroon Farooq;Rehan Liaqat;Zafar A. Khan;Abdullah Altamimi

{"title":"A Novel Permanent Magnet Assisted Brushless Wound Rotor Synchronous Machine With Improved Torque Characteristics","authors":"Muhammad Ahsan Ul Haq;Haroon Farooq;Rehan Liaqat;Zafar A. Khan;Abdullah Altamimi","doi":"10.1109/ACCESS.2025.3588513","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3588513","url":null,"abstract":"In this manuscript, a novel permanent magnet assisted brushless wound rotor synchronous machine (PMa-BLWRSM) has been presented. Contrary to the existing configurations requiring two inverters for BLWRSMs, the proposed brushless topology requires a single 3-phase inverter. Two dominant components of magneto motive force (MMF) are available in the stator MMF (sMMF) of the suggested PMa-BLWRSM: subharmonic component (SHC) and fundamental component (FC). The SHC of sMMF is utilized to obtain the brushless working of the proposed machine. The rotor design includes two windings: 1) harmonic winding (HW), and 2) field winding (FW). A rotating bridge rectifier is embedded between these two windings. The SHC of the sMMF produces an AC voltage in the HW of the rotor. The produced voltage in HW is given to the rotating rectifier, which converts it into DC. This DC is given to the FW of the rotor. A 2-dimensional finite element analysis is performed using JMAG designer to verify the operating principle of the suggested 8-pole and 12-slot PMa-BLWRSM. In comparison to existing BLWRSM, the proposed machine provides high starting and average torques with less input stator winding current and low torque ripples. The proposed machine can be used for applications requiring high starting torque.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124351-124363"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079565","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

C-Coil: A High Performance Computing Approach for Magnetostatic Circular Coil Calculations C-Coil：一种用于静磁圆线圈计算的高性能计算方法

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3589331

Davor Dobrota;Lara Vrabac;Nikola Sočec;Filip Vučić;Dario Bojanjac

{"title":"C-Coil: A High Performance Computing Approach for Magnetostatic Circular Coil Calculations","authors":"Davor Dobrota;Lara Vrabac;Nikola Sočec;Filip Vučić;Dario Bojanjac","doi":"10.1109/ACCESS.2025.3589331","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3589331","url":null,"abstract":"Accurate computation of magnetostatic coupling between non-coaxial circular coils remains prohibitively expensive when millions of configurations must be evaluated for design-space exploration. We propose a novel approach based on numerical methods to improve performance by 5 to 7 orders of magnitude while matching the accuracy of state-of-the-art semi-analytical methods. While other approaches strive to reduce the number of integration directions in the six-fold integral to 2 or 4, we propose a five-fold integral with simple-to-evaluate integrands. In place of the filament method, we employ the Gauss-Legendre quadrature due to its exponential convergence and find that numerical integration can be quicker than analytic integral evaluation. Furthermore, to tackle the complexity of allocating the computational resources to each of the five integration directions, we propose a heuristic that leads to 2 orders of magnitude lower computation time or 2 to 4 orders of magnitude higher accuracy. We also provide an implementation of our approach in C-Coil, an open-source C++ library with Python bindings that can also be used in MATLAB.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"123835-123854"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080413","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

SiamMCE: An Efficient Siamese Network for Real-Time Object Tracking With Dual-Correlation Strategy and Dynamic Template Updating 基于双相关策略和动态模板更新的高效Siamese网络实时目标跟踪

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3588568

Xin Wang;Dejiang Wang;Mingchao Sun;Hang Ren;Yulian Zhang;Songwei Han;Ligang Liu

{"title":"SiamMCE: An Efficient Siamese Network for Real-Time Object Tracking With Dual-Correlation Strategy and Dynamic Template Updating","authors":"Xin Wang;Dejiang Wang;Mingchao Sun;Hang Ren;Yulian Zhang;Songwei Han;Ligang Liu","doi":"10.1109/ACCESS.2025.3588568","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3588568","url":null,"abstract":"Real-time object tracking in dynamic environments poses significant challenges in balancing computational efficiency with robust performance under complex scenarios such as occlusion and illumination changes. This paper presents SiamMCE (Siamese MobileNet-CE) tracker, an optimized Siamese network variant that integrates three key innovations to address these challenges. First, we design a lightweight backbone network MobileNet-CE which is based on MobileNetV3-Small through strategic integration of Convolutional Block Attention Module (CBAM) and Efficient Channel Attention Module (ECAM), reducing parameters by 18% while enhancing feature discriminability. Second, we propose a Dual-Correlation Strategy combining Pixel-wise Correlation (PWC) and Channel-wise Correlation (CWC) operations to improve localization precision through complementary spatial-channel feature fusion. Third, a Dynamic Template Adaptation mechanism leverages response map analysis via UpdateNet to enable online template refinement, mitigating drift accumulation during long-term tracking. Extensive experiments on benchmarks (OTB-2015, VOT2018, UAV123) demonstrate that SiamMCE achieves robust performance across mainstream tracking tasks, balancing competitive accuracy with real-time operation on embedded platforms. This capability enables new applications in dynamic environments, such as UAV-based detection and mobile surveillance, where sustained reliability is critical.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124569-124586"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Transformer-Based Vulnerability Detection in IoT Firmware Binaries Using Opcode Sequences 使用操作码序列的物联网固件二进制文件中基于变压器的漏洞检测

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3588950

M. Nandish;Jalesh Kumar;H. G. Mohan;M. V. Manoj Kumar

{"title":"Transformer-Based Vulnerability Detection in IoT Firmware Binaries Using Opcode Sequences","authors":"M. Nandish;Jalesh Kumar;H. G. Mohan;M. V. Manoj Kumar","doi":"10.1109/ACCESS.2025.3588950","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3588950","url":null,"abstract":"Firmware security is critical for maintaining the integrity of embedded systems. However, detecting vulnerabilities in firmware binaries is a challenging task. This is due to the absence of source code, the inherent complexity of binary structures, the diversity of hardware architecture, and the difficulty of extracting deep contextual representations from binaries. In the proposed approach, the Decoding-enhanced BERT with Disentangled Attention (DeBERTa), a novel transformer-based model is used to detect vulnerabilities in firmware binaries. Initially, firmware binaries are disassembled to extract opcode sequences, which are then tokenized and encoded as inputs to the proposed DeBERTa model. The model processes instruction opcode sequences and generates meaningful embeddings, which are utilized for classification tasks. The classifiers used in the proposed approach are Random Forest, Multi-Layer Perceptron, and GAN-based classifier, which operate on the DeBERTa-generated embeddings. The proposed model learns deep contextual representations of firmware code, effectively capturing intricate syntactic and semantic relationships. The evaluation is conducted on IoT firmware binaries collected from real-world IoT projects, reflecting practical and diverse vulnerability scenarios. Experimental results demonstrate that the proposed DeBERTa-based model achieves 97% accuracy, 97% recall, and 94.6% F1-score, outperforming conventional embedding techniques. The experimental findings demonstrate that the opcode sequence feature effectively and reliably detects different types of vulnerable and benign IoT samples.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124250-124263"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11080410","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Design and Implementation of Active Control Method for Minimizing Circulating Current in MMC-VSC System MMC-VSC系统循环电流最小化主动控制方法的设计与实现

IF 3.4 3区计算机科学

IEEE Access Pub Date : 2025-07-15 DOI: 10.1109/ACCESS.2025.3588713

A. Aslam;M. Raza

{"title":"Design and Implementation of Active Control Method for Minimizing Circulating Current in MMC-VSC System","authors":"A. Aslam;M. Raza","doi":"10.1109/ACCESS.2025.3588713","DOIUrl":"https://doi.org/10.1109/ACCESS.2025.3588713","url":null,"abstract":"Modular Multilevel Converters (MMCs) have emerged as a key technology for large-scale renewable energy integration due to their scalability, fault tolerance, and superior output quality. However, internal circulating currents remain a major barrier to efficiency and long-term reliability. It causes power losses, increased thermal stress, and excessive capacitor voltage fluctuations. Existing passive and active methods often lack clear design guidelines or fail to achieve robust suppression under varying operating conditions. This paper introduces a comprehensive hybrid strategy that addresses these gaps through two key innovations. Firstly, an analytical expression for arm inductor sizing is derived using instantaneous power theory and the harmonic addition theorem. It offers an explicit passive design method rather than relying on heuristic selection. This analytical formulation ensures optimal passive suppression within practical inductor size constraints. Then an advanced active suppression scheme is developed. Unlike conventional approaches, the circulating current is regulated using a vector control strategy formulated in the dq reference frame. It enables precise control of the dominant second-order harmonic. The PI controller is tuned through a direct pole placement method. A high-pass filter is integrated upstream of the controller to eliminate the DC offset. The simulation studies demonstrates that the proposed methods outperforms traditional direct modulation by significantly reducing circulating current amplitude, lowering power losses and improving thermal performance. The results confirm that the passive and active control framework delivers a robust, scalable, and practically implementable solution for next-generation MMC-based renewable energy systems.","PeriodicalId":13079,"journal":{"name":"IEEE Access","volume":"13 ","pages":"124471-124482"},"PeriodicalIF":3.4,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11079593","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0