IEEJ Transactions on Electrical and Electronic Engineering最新文献_第9页

Dynamic Texture Segmentation Using Robust Dynamic Fuzzy Clustering with Student's t-Hidden Markov Model 基于学生t-隐马尔可夫模型的鲁棒动态模糊聚类动态纹理分割

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-06-14 Epub Date: 2025-10-31 DOI: 10.1002/tee.70193

Zhengyi Xing, Yulong Qiao, Wenhui Liu, Yue Zhao

引用次数: 0

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-05-08

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IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-30

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IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-20

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Trends in Isolated Power Converters Using High-Frequency Transformers 使用高频变压器的隔离电源变换器的发展趋势

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2026-03-17 DOI: 10.1002/tee.70259

Takaharu Takeshita, Wataru Kitagawa, Kohei Budo

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Intelligent Building Electrical Fault Diagnosis Based on Relevant Vector Machine Optimized by Intelligent Algorithm 基于智能算法优化的相关向量机智能楼宇电气故障诊断

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2025-09-27 DOI: 10.1002/tee.70179

Jiahui Li, Dongliang Gu

{"title":"Intelligent Building Electrical Fault Diagnosis Based on Relevant Vector Machine Optimized by Intelligent Algorithm","authors":"Jiahui Li, Dongliang Gu","doi":"10.1002/tee.70179","DOIUrl":"https://doi.org/10.1002/tee.70179","url":null,"abstract":"Smart buildings are developing rapidly, and electrical fault diagnosis is becoming increasingly important for the stable operation of smart buildings. To improve the accuracy and efficiency of electrical fault prediction, this study constructs a model based on an improved gray wolf optimization algorithm to optimize the relevant vector machine. A multi-core correlation vector machine model is proposed by optimizing the parameters of the correlation vector machine through an improved gray wolf optimization algorithm. The results demonstrated that the accuracy of the proposed model reached 96.8%, which was the highest improvement of 17.2% compared to the comparative model. On an imbalanced dataset, the accuracy of the research model was 96.4%, the recall was 86.4%, and the F1 value was 0.91, verifying that the model performed well in classification tasks and had strong generalization ability. At the same time, the false alarm rate of the system in practical applications was 1.3%, and the false negative rate was 2.6%, indicating that the system had strong real-time monitoring and identification capabilities for electrical faults. This study provides a theoretical basis for electrical fault diagnosis in smart buildings and reliable technical support for the stable operation of smart buildings. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 5","pages":"704-714"},"PeriodicalIF":1.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Insulator Defect Detection Method Based on Model Parameter Migration BAAC-YOLOv11 基于模型参数迁移的绝缘子缺陷检测方法

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2025-10-21 DOI: 10.1002/tee.70180

Bo Chen, Tao Li, Zucai Xiao

{"title":"Insulator Defect Detection Method Based on Model Parameter Migration BAAC-YOLOv11","authors":"Bo Chen, Tao Li, Zucai Xiao","doi":"10.1002/tee.70180","DOIUrl":"https://doi.org/10.1002/tee.70180","url":null,"abstract":"To enhance the accuracy and operational applicability of transmission line insulator defect detection systems, this study proposes a hybrid framework integrating model parameter transfer learning with an optimized YOLOv11 architecture. First, A parameter transfer framework was engineered to mitigate cross-domain performance degradation in insulator defect detection, incorporating an adversarial domain discriminator with dual-branch architecture for simultaneous image-level texture analysis and instance-level feature alignment. This module was trained using gradient reversal layers to enforce domain-invariant feature learning while preserving discriminative defect patterns; Second, taking advantage of the advantages of YOLOv11 model being lighter and easier to deploy on resource constrained devices compared to other versions, as well as its friendly application scenario for detecting insulator defects in outdoor transmission lines, a bidirectional feature pyramid network (BiFPN) is adopted as a new multi-scale feature extraction network, and attention mechanism and anchor box clustering method are introduced to improve the YOLOv11 model and further enhance its performance. An anchor box clustering YOLOv11 algorithm based on BiFPN and Attention mechanism (BAAC-YOLOv11) was proposed. Through simulation experiments on the artificial sample generation set of insulator defects, the results show that under harsh conditions such as foggy weather, this method has good transfer recognition effect on the insulator defect detection model, verifying the effectiveness of this method. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 5","pages":"715-727"},"PeriodicalIF":1.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147684030","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Identification and Diagnosis of Stator Inter-Turn Short Circuit Faults Based on Current Harmonic Characteristic Analysis in Synchronous Generators 基于电流谐波特性分析的同步发电机定子匝间短路故障识别与诊断

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2025-10-01 DOI: 10.1002/tee.70168

Ming-Xing Xu, Yu-Ling He, Wen Zhang, De-Rui Dai, Yu-Song Zhang, Xiao-Liang Feng

{"title":"Identification and Diagnosis of Stator Inter-Turn Short Circuit Faults Based on Current Harmonic Characteristic Analysis in Synchronous Generators","authors":"Ming-Xing Xu, Yu-Ling He, Wen Zhang, De-Rui Dai, Yu-Song Zhang, Xiao-Liang Feng","doi":"10.1002/tee.70168","DOIUrl":"https://doi.org/10.1002/tee.70168","url":null,"abstract":"This paper presents a comprehensive study of the electromagnetic characteristic laws of synchronous generator for effective monitoring of stator inter-turn short circuit (SISC). Different from other studies, this paper not only investigates the magnetic flux density (MFD) harmonic characteristics, but also the stator currents and the circulating current inside parallel branches (CCPB) harmonic characteristics. Theoretical calculations, finite element analysis (FEA), and experimental validation were carried out on the MJF-30-6 generator. It is shown that after SISC, the MFD first harmonic amplitude decreases, and the third harmonic amplitude increases. The stator current first harmonic amplitude decreases, but the third harmonic amplitude becomes larger. In addition, when SISC occurs in only one branch or SISC asymmetrically occurs in the two parallel branches, the CCPB will generate the fundamental (first) and the third harmonic, and the harmonic amplitude will increase with the intensification of the SISC asymmetry degree. However, when SISC symmetrically occurs in both the two parallel branches, there is no CCPB, regardless of how the short circuit varies. These united characteristics can be applied to the diagnosis and online monitoring of SISC fault. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 5","pages":"745-754"},"PeriodicalIF":1.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147682985","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Hybrid Physical and Data-Driven Framework for Temperature Evaluation of Permanent Magnets in PMSMs 永磁同步电机中永磁体温度评估的混合物理和数据驱动框架

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2025-10-02 DOI: 10.1002/tee.70170

Huizhen Wang, Benchao Zhu, Yueyun Feng, Zichen Gao, Lijun Diao

{"title":"A Hybrid Physical and Data-Driven Framework for Temperature Evaluation of Permanent Magnets in PMSMs","authors":"Huizhen Wang, Benchao Zhu, Yueyun Feng, Zichen Gao, Lijun Diao","doi":"10.1002/tee.70170","DOIUrl":"https://doi.org/10.1002/tee.70170","url":null,"abstract":"To ensure the reliable operation of permanent magnet synchronous machines (PMSMs), accurate temperature evaluation and monitoring of permanent magnets (PM) are essential, as excessive heat can lead to performance degradation and irreversible damage. A hybrid physical and data-driven framework is proposed to address this challenge. The framework combines a physical model, which captures the thermal dynamics of PM, with advanced machine learning techniques. The physical model is translated into a graph structure, enabling the use of a graph attention network (GAT) to update node features and extract complex thermal interactions. A convolutional neural network (CNN) is subsequently applied for precise temperature evaluation. Experimental results validate the robustness and generalization capability of the proposed method, providing a reliable approach for assessing the thermal performance of PM in PMSMs. This work establishes a foundation for exploring temperature-dependent magnet degradation and implementing preventive measures in PMSMs. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 5","pages":"755-764"},"PeriodicalIF":1.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Passive Islanding Detection Method for Power System Connected with Multiple Distributed Generations Using an Unscented Kalman Filter 基于无嗅卡尔曼滤波的多分布式电网无源孤岛检测方法

IF 1.1 4区工程技术

IEEJ Transactions on Electrical and Electronic Engineering Pub Date : 2026-04-06 Epub Date: 2025-10-15 DOI: 10.1002/tee.70174

Nauman Ali Larik, L. L. Zhang, M. S. Li, Q. H. Wu

{"title":"A Passive Islanding Detection Method for Power System Connected with Multiple Distributed Generations Using an Unscented Kalman Filter","authors":"Nauman Ali Larik, L. L. Zhang, M. S. Li, Q. H. Wu","doi":"10.1002/tee.70174","DOIUrl":"https://doi.org/10.1002/tee.70174","url":null,"abstract":"The integration of distributed generations (DGs) powered by renewable energy sources into the power grid has attracted a lot of interest. Despite the many benefits of distributed energy resources (DER), unintentional islanding remains a severe problem that endangers working employees, causes power quality deviation, poses a risk to power utility equipment, and out-of-phase connection may arise because of rapid reclosing. This paper provides an innovative passive islanding detection approach (PIDA) formulated on an unscented Kalman filter (UKF) that is suited for numerous distributed generations. The unscented transformation (UT) overcomes the linear Kalman filter's limitation for higher-order terms in the Taylor series. The suggested approach computes selected harmonic distortion from three voltage recorded at the point of common coupling (PCC). The performance of the proposed technique is thoroughly investigated using IEEE-13 bus system simulation in MATLAB/Simulink. In addition, various non-islanding events with significant inductive load and fault conditions have been observed to test for nuisance tripping. Evaluation of the results reveals that the proposed passive method with less computation burden provides rapid detection with a negligible non-detection zone and distinguishes islanding and normal state effectively. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 5","pages":"647-656"},"PeriodicalIF":1.1,"publicationDate":"2026-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147683718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0