IEEJ Transactions on Electrical and Electronic Engineering最新文献

{"title":"Development of a Handheld Hotspot Detecting Device Based on YOLOv5 Application on Thermal Images of Photovoltaic Panels","authors":"Nguyen Phuoc Hoang Khang,&nbsp;Duong Minh Hai,&nbsp;Nguyen Chi Nhan","doi":"10.1002/tee.70421","DOIUrl":"https://doi.org/10.1002/tee.70421","url":null,"abstract":"<p>The booming of solar power systems has led to an increasing demand for monitoring and maintenance of solar panels. Hotspots on photovoltaic panels are a common dysfunction when operating solar power systems, reducing the generated power, system's efficiency, PV panel's lifespan and potentially causing serious hazard. Therefore, quick and accurate hotspots detection is important for maintenance, operation and safety. With the development of machine learning methods application in engineering, the combination of a machine learning model capable of detecting hotspots with thermal camera devices has high potential to improve the hotspot detection for better maintenance process. The main trend of research in the field is the application of UAV to collect thermal images of PV panels from aerial view to maximize monitoring area. However, UAV applications are facing many restrictions in several regions due to military capability, weather conditions and wide area requirements in operation. On the other hand, because of its complexity and flying control tasks, there is limited room for integration of machine learning models in UAV applications for real-time hotspot detection. Currently on the market, most existing devices and manufacturers still haven't applied AI achievements into products. This research is about the designing, developing and testing of a handheld hotspot detecting device with YOLOv5 application to process captured thermal images to detect hotspot area appeared. The development of the device included case designing, electronic designing, user interface designing and firmware development. The developed device using common components of Raspberry Pi 4, thermal camera with application of pre-trained YOLOv5 model with features of capturing and detecting hotspot areas on resulting thermal images and displaying result images on device's interface. Although the design is primitive with limited processing speed of 0.7 FPS when performing real time detections using YOLOv5 model, the device has capability of an Edge-AI device to apply in PV panels hotspot detection. Furthermore, the research also shows the requirements and difficulties in work-flow design when integrating machine learning models into practical applications and real time performance devices. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1743-1750"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099627","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}

{"title":"Design and Vibration Noise Analysis of Hybrid Magnetic Core for a Transformer","authors":"Chengcheng Liu,&nbsp;Weisong Li,&nbsp;Zhigang Zhao,&nbsp;Youhua Wang","doi":"10.1002/tee.70278","DOIUrl":"https://doi.org/10.1002/tee.70278","url":null,"abstract":"<p>Amorphous alloy is widely used in fabricating the core and shield part of transformers because of its low loss characteristic. However, it also suffers the disadvantages of low saturation flux density and great magnetostrictive effect. In this paper, a hybrid magnetic core structure is proposed to release above problems: specifically, oriented silicon steel (GOES) is utilized to design the inner core to improve the working flux density and reduce the vibration while the amorphous alloy is used to design the outer core to reduce the core loss. First, an equivalent magnetic circuit model of the hybrid magnetic core is established. The permeability of adopted two soft magnetic materials is iterated for obtaining accurate flux density distribution in each section of the hybrid magnetic core. In addition, the vibration and noise analysis model of the magnetic core is developed, and the calculated performance is verified by the measured results. Finally, the performance of a three-phase five-column transformer designed with the hybrid magnetic core is compared with the benchmark transformer with the amorphous alloy and GOES cores. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1665-1674"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099459","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}

{"title":"A Deep Transfer Convolutional Neural Network Framework for Microgrid Fault Detection","authors":"Zhaoqiang Su,&nbsp;Wei Jiang","doi":"10.1002/tee.70287","DOIUrl":"https://doi.org/10.1002/tee.70287","url":null,"abstract":"<p>Microgrid fault detection is challenged by diverse operating modes, nonlinear system dynamics, and limited availability of labeled fault data. Conventional threshold-based and model-based methods often exhibit reduced robustness under noise and multi-parameter fault conditions, while data-driven machine learning approaches typically require extensive training data. This paper proposes a Deep Transfer Convolutional Neural Network (DTCNN) framework for microgrid fault detection, leveraging a pretrained VGG-16 model to enhance feature extraction under limited data conditions. Voltage, current, and frequency signals are transformed into time–frequency spectrograms using the Short-Time Fourier Transform (STFT), enabling effective representation of transient and steady-state fault characteristics. Simulation results based on an industrial park microgrid demonstrate that the proposed DTCNN consistently outperforms conventional machine learning methods, including Support Vector Machines and Random Forests, in terms of accuracy, recall, and F1 score. Moreover, the proposed framework maintains stable detection performance across different operating modes and load conditions, indicating strong robustness and generalization capability. These results suggest that deep transfer learning provides an effective and practical solution for reliable microgrid fault detection. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1656-1664"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099575","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}

{"title":"Research Progress on Load Forecasting and Scheduling Optimization for New Power Systems","authors":"Yong Wang,&nbsp;Licheng Sha,&nbsp;Xijuan Yu,&nbsp;Kai Xu,&nbsp;Ning Wang","doi":"10.1002/tee.70274","DOIUrl":"https://doi.org/10.1002/tee.70274","url":null,"abstract":"<p>With the gradual diversification of energy demand and consumption patterns, new types of loads—such as integrated energy systems and electric vehicles—are emerging. The characteristics of electricity consumption behavior and load components are becoming increasingly complex, reducing load forecasting accuracy in new power systems, and increasing the difficulty of grid load dispatching optimization. This paper first focuses on load forecasting in new power systems, analyzing classical methods like regression analysis, gray forecasting, and time series analysis, as well as artificial intelligence approaches such as artificial neural networks and wavelet analysis. It then examines modern ensemble forecasting methods for multi-load integration scenarios, which address traditional methods' limitations in handling nonlinear and nonstationary loads while avoiding local optimization or overfitting in simple machine learning. Finally, it summarizes research on load-side resource allocation and scheduling control using mathematical programming and genetic algorithms, with in-depth discussion on synergistic optimization of forecasting and dispatching. Furthermore, this review maps methods to specific application scenarios, offering practical guidance for researchers and practitioners. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1641-1655"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099991","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}

{"title":"Skeleton-Parted 3D Human Motion Prediction Using ST-GC and Attention Mechanisms","authors":"Shubin Yang,&nbsp;Yali Zheng,&nbsp;Bo Yang,&nbsp;Xiaoming Peng,&nbsp;Libing Bai,&nbsp;Zongyuan Ge","doi":"10.1002/tee.70288","DOIUrl":"https://doi.org/10.1002/tee.70288","url":null,"abstract":"<p>Human motion prediction involves accurately modeling and forecasting the future trajectories of human movements. It is a key step in understanding complex human dynamics and has broad applications in areas such as rehabilitation training, and autonomous driving. The complexity of motion differs between the core skeletal joints and the distal limb joints, and so does their similarity to the trajectory of the hip joint, resulting in varying prediction errors for different joints. In this paper, a two-stage 3D human motion prediction algorithm based on spatiotemporal enhancement (STE) is proposed, combining spatiotemporal graph convolutional networks (ST-GC) and self-attention (SA) mechanisms. Our method estimates 3D human motion by separating core skeletal joints from distal limb joints. In predicting core skeletal joints, the spatial graph convolution(S-GC) and spatiotemporal attention (STA) mechanisms incorporate learnable graph enhancement matrix and spatio-temporal biases to improve motion prediction stability. For distal limb joints, the STA mechanism employs a learnable spatiotemporal graph enhancement matrix (ST-GEM). The proposed method is validated on three datasets. Experimental results demonstrate the rationale of using a partitioned and staged strategy for the skeletal structure and verify the effectiveness of the motion prediction models for both core skeletal joints and distal limb joints. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1684-1693"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099975","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}

{"title":"Prediction of Positive/Negative Error of a Day-Ahead Forecast of Aggregated PV Power Output","authors":"Miyu Nakamura,&nbsp;Takeyoshi Kato","doi":"10.1002/tee.70271","DOIUrl":"https://doi.org/10.1002/tee.70271","url":null,"abstract":"<p>To enhance the profitability of electricity retailers, minimizing the penalty cost associated with imbalances between day-ahead electricity supply plans and the actual supply on the designated day is crucial. One significant contributor to these imbalances is the forecast error related to Photovoltaic (PV) power output, especially when a considerable amount of PV is incorporated into the supply plan. A Battery Energy Storage System (BESS) can help mitigate these imbalances, although its limitations concerning available energy capacity can be more critical than those concerning available power capacity. To use the limited energy capacity of a BESS efficiently, it should be fully charged for expected positive forecast errors and fully discharged for negative ones. This study introduces a streamlined approach to predicting the sign of positive or negative (P/N) errors—specifically, whether the day-ahead forecast error of aggregated PV power output will be positive or negative. The method bases its predictions on the direction of change in forecasted insolation for subsequent initial time intervals. Our evaluation, conducted over a year using aggregated PV power data, reveals that the hit ratio for predicting the P/N error sign exceeds 70%. Furthermore, this prediction demonstrates increased reliability on days characterized by substantial absolute forecast errors. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1634-1640"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100306","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}

{"title":"Robust Adaptive Fixed-Time Fast Integral Terminal Sliding Mode Control for PMLSM Position Tracking","authors":"Gang Chen,&nbsp;Longxin He,&nbsp;Wei Wu,&nbsp;Yabin Xu,&nbsp;Chenguang Yang,&nbsp;Huosheng Hu,&nbsp;Zhengwei Chen,&nbsp;Xingxing Wang,&nbsp;Li Chen,&nbsp;Chuanyu Wu,&nbsp;Yu Ma,&nbsp;Pengguo Xia,&nbsp;Yu Huang","doi":"10.1002/tee.70286","DOIUrl":"https://doi.org/10.1002/tee.70286","url":null,"abstract":"<p>A robust adaptive fixed-time integral terminal sliding mode control (AFITSMC) method is proposed in this study to solve the problems of the slow convergence, low position-tracking accuracy, and inability to achieve stability within a fixed time of a permanent magnet linear synchronous motor (PMLSM) servo system. First, a dynamical mathematical model of PMLSM is established to describe the thrust and unknown uncertainty in the motion process. Second, a novel sliding mode surface is designed to address the problem of the slow convergence of traditional controllers. In addition, an adaptive law is used to dynamically estimate the lumped uncertain parameters to achieve high position-tracking accuracy, considering the existence of uncertain factors, such as friction, load change, and parameter change in the system, which always decrease position-tracking accuracy. The proposed adaptive control is completely chattering-free, Lipschitz continuous and no gain overestimation exists, which is critical to practical applications under measurement noises. Third, a fixed-time fast integral terminal sliding mode control strategy is proposed in this study, namely, the AFITSMC strategy, to address the problem of traditional controllers' inability to achieve fixed-time convergence. The controller can drive the sliding mode surface near the predefined equilibrium within a fixed time and make it converge within a finite time. Last, experiments are conducted, and the results show that the proposed controller can achieve not only system state convergence within a fixed time but also fast convergence and high position-tracking accuracy. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1728-1739"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099640","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}

{"title":"Calculating the Interconnection Limit from a Comprehensive Voltage Evaluation for Distribution Systems with Large Distributed Energy Resources","authors":"Satoshi Uemura,&nbsp;Koushichi Nemoto,&nbsp;Yasuhiro Hayashi","doi":"10.1002/tee.70266","DOIUrl":"https://doi.org/10.1002/tee.70266","url":null,"abstract":"<p>Japan is pursuing power source decarbonization and is thus reducing fuel-based thermal power generation while promoting renewable energy resource use. Given the expected power fluctuations, this transition will require novel voltage management and control methods. This study investigates the current voltage management and control methods for distribution systems and the voltage control methods adopted by distributed energy resources (DERs). An equation is derived for accurately estimating the voltage under conditions in which the power flow is heavy and changes frequently. A method is proposed for calculating the hosting capacity (HC) using the derived equation and trial calculations are conducted on a standard distribution system model. The results indicate a lower HC than that of conventional operations. Finally, the study findings are summarized and a vision and recommendations for future research directions and challenges are provided, contributing to the national objective of achieving carbon neutrality by 2050. © 2026 The Author(s). <i>IEEJ Transactions on Electrical and Electronic Engineering</i> published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1609-1622"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/tee.70266","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100219","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Scale Underwater Image Enhancement Network Based on VM-Unet Improvement","authors":"Xuhong Huang,&nbsp;Weixuan Zhan,&nbsp;Yuanhai Chen,&nbsp;Yixin Zhang,&nbsp;Jinghui Lin","doi":"10.1002/tee.70261","DOIUrl":"https://doi.org/10.1002/tee.70261","url":null,"abstract":"<p>Underwater images suffer from color distortion, low contrast, and blurred details due to selective light absorption, scattering, and suspended particles in water, severely limiting the visual perception and autonomous operation capabilities of underwater robots. To address these issues, this paper proposes a multi-scale underwater image enhancement network based on an improved VM-Unet. Centered on the Visual Mamba model, this network employs an asymmetric encoder-decoder architecture and incorporates parallel Visual Mamba layers to enhance long-range dependency modeling. Additionally, it integrates an attention mechanism to construct a channel-level multi-scale feature fusion module, enabling dynamic integration of features across different scales and improving the model's adaptability and robustness in complex underwater environments. Experiments on the UIEB dataset demonstrate that the proposed method outperforms traditional approaches and mainstream deep learning models in both subjective visual quality and objective evaluation metrics (including PSNR, SSIM, UCIQE, and UIQM), particularly excelling in color restoration, detail preservation, and noise suppression. Furthermore, the method processes single-frame images in just 0.2 s, offering excellent real-time performance that meets the demands of real-time visual enhancement for underwater robots. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1708-1717"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099974","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}

{"title":"Tuning the Anomalous Hall Conductivity of Hcp-Cobalt by Constant-Volume Biaxial Strain","authors":"Trinh Thi Ly,&nbsp;Nguyen Bui Trung Kien Non-member,&nbsp;Do Duc Cuong","doi":"10.1002/tee.70345","DOIUrl":"https://doi.org/10.1002/tee.70345","url":null,"abstract":"<p>Controlling the anomalous Hall effect (AHE) is crucial for advancing spintronic devices. In this work, we investigate how constant-volume biaxial strain affects the intrinsic anomalous Hall conductivity (AHC) of hexagonal close-packed (hcp) cobalt. The calculations combine first-principles density functional theory (DFT) with a Wannier-based tight-binding model. Our results reveal a non-monotonic AHC response to applied strain. The conductivity reaches a maximum under small compressive strain and is suppressed more rapidly by larger compressive strain than by equivalent tensile strain. We examine microscopically how strain shifts the energy position of anti-crossing band features near the Fermi level. This shift drives a redistribution of the Berry curvature hotspots. These results confirm constant-volume biaxial strain as a viable approach to tuning the intrinsic AHE in hcp cobalt. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.</p>","PeriodicalId":13435,"journal":{"name":"IEEJ Transactions on Electrical and Electronic Engineering","volume":"21 11","pages":"1767-1772"},"PeriodicalIF":1.0,"publicationDate":"2026-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100417","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}