This study develops a hybrid analytical model combining subdomain method and finite difference method (FDM) to overcome conventional limitations in modeling rotor saturation for solid‐rotor induction motors (SRIMs). The solution domain partitions into stator/air‐gap regions solved via subdomain method and rotor region solved via finite difference method with iterative permeability updates, enabling dynamic magnetic field simulation. Validation demonstrates maximum radial flux density errors of 2.43% at s = 0 and 8.26% at s = 1 across the full slip range—significantly surpassing standalone subdomain methods. With computation time reduced by over 90% versus finite element method, the model enables rapid parameter optimization during preliminary electric machine design, providing essential computational efficiency for industrial motor design where accurate saturation modeling is critical. © 2026 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Solid permanent magnets are commonly used in the rotors of ultra‐high‐speed permanent magnet synchronous motors (PMSMs). However, standard rotor designs are subject to assembly difficulties and high manufacturing costs that are not conducive to large‐scale production. The present work addresses this issue by proposing an integrated rotor structure composed of a retaining sleeve and ring‐shaped permanent magnets with a rotor core that yields an ultra‐high‐speed PMSM operating at 95 krpm under 25 kW of power. Firstly, electromagnetic finite element analysis is conducted for rotor design schemes with different permanent magnet, sleeve, and rotor core materials to determine the optimal outer rotor core diameter based on the maximum current of the applied converter, and the electromagnetic performances of the schemes are compared. Secondly, the rotor design schemes are subjected to mechanical finite element analysis to obtain stress distributions under different temperatures. Finally, the rotor design scheme applying NdFeB permanent magnets, a carbon fiber sleeve, and a Ti‐alloy rotor core is identified as providing optimal eddy current loss, temperature, tangential stress, and vibrational characteristics that surpass those of PMSMs applying conventional rotor designs. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

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.

Analysis of actual outages is essential for efficient lightning‐protection design. In this study, we estimate strokes that cause surge arrester burnout faults by considering not only the distance from the fault position to the stroke position estimated by a lightning location system but also the estimated peak current. Different configurations of the distribution line experiencing surge arrester damage are observed: various types of surge arresters are installed at the lightning striking pole, and the low‐voltage power distribution line is installed below the medium‐voltage line in some cases. The effect of this variation on the possibility of surge arrester damage is examined via numerical calculation. Results show that the variation in the withstand capability of surge arresters and the presence of appendant wires should be considered to reproduce surge arrester burnout faults on actual distribution lines in the summer. © 2026 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

Magnetostriction in non‐oriented electrical steel constitutes the primary excitation mechanism for vibration in motor stator cores. While magnetostrictive anisotropy stems from a combination of intrinsic material characteristics and extrinsic factors—including grain size heterogeneity, residual stresses, and crystallographic texture—the manufacturing processes of stator cores introduce significant additional residual stresses. These process‐induced stresses further degrade magnetostrictive performance. Prevailing research, predominantly conducted on unprocessed silicon steel laminations under idealized conditions, has substantially underestimated magnetostrictive behavior in operational cores. To address this limitation, this study implemented a magnetostriction measurement system utilizing resistance strain gauges. Comprehensive characterization was performed on stator cores manufactured from 25SW1300‐grade steel, with particular emphasis placed on quantifying the anisotropy mechanisms and the specific influence of manufacturing processes. Obtained results found that the stator core loss exceeded that of single‐piece silicon steel sheets by more than 8%. Critically, localized magnetostrictive strain was found to be amplified by welded operations by 3 μm/m, representing a twofold increase relative to non‐welded regions. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

In direct current (DC) microgrids, traditional droop control methods facilitate power sharing and stabilize bus voltage. However, this communication‐less approach is constrained by challenges including insufficient voltage restoration and imbalances in battery state of charge (SOC). This paper introduces a novel communication‐less control strategy designed to address these limitations in photovoltaic energy storage system (PV‐ESS) DC microgrids. This method assigns the energy storage unit (ESU) closest to the critical load as the ‘master unit’ setting its output voltage to nominal levels. This master unit injects an alternating current (AC) signal into the DC bus, with the signal frequency modulated by its SOC and output current. Additional ESUs, referred to as ‘slave units’ adjust their output currents based on the frequency of the AC signal and their local SOC. Furthermore, the master unit utilizes a logical signal based on its SOC to switch the photovoltaic module between droop control mode and maximum power point tracking (MPPT) mode. Stability analysis verifies the robustness of the proposed method, and simulation results demonstrate that it surpasses conventional droop control approaches. Specifically, the proposed method enhances load‐sharing precision, minimizes voltage deviation, and ensures SOC equalization across the DC microgrid without the need for communication links. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Abstract To address the complex dynamic control of multi‐source coupled drive systems in bionic robotic fish, this paper proposes a BP neural network–fuzzy fusion optimization algorithm. The method employs a parallel architecture that takes depth error, depth change rate, and pitch angle as inputs. Fuzzy control ensures rapid response to nonlinear disturbances, while an offline‐trained BP neural network enhances the modeling accuracy of complex dynamics. The core innovation involves dynamically adjusting the fusion weights of both controllers using sliding window error trends, enabling coordinated control of propeller speed, tail fin oscillation, and slider displacement. Experimental results indicate that under disturbance‐free conditions, the fused algorithm achieves an average integral absolute error (IAE) of 18.6 ms, representing reductions of 16.2% and 40.5% compared to individual fuzzy and BP neural network control, respectively. Response time is reduced to 23 s with overshoot limited to 45%. In anti‐interference and mass perturbation tests, the algorithm exhibits significantly better convergence speed and stability than conventional methods. The dynamic weight allocation mechanism successfully combines the rapidity of fuzzy logic with the precision of neural networks, offering an effective solution for multimodal motion control of underwater bionic robotic fish. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Abstract The health condition of rolling element bearings profoundly affects the longevity, operational performance, and efficiency of the entire machinery system. This makes accurate division of health stages and condition identification essential for machine monitoring. To meet this need, this paper introduces a hybrid method combining three key steps. Initially, time‐domain analysis based on Pearson correlation coefficients (PTDA) selects initial features that are most sensitive to progressive degradation. Subsequently, a combined technique using fuzzy C‐means clustering with validity function, simulated annealing and genetic algorithm (called VSAGAFCM) achieves optimal segregation of the data into distinct health states, thereby eliminating the subjectivity associated with manual stage division. Finally, a probabilistic neural network (PNN) leverages these results to accurately identify each health state, capitalizing on its rapid training speed and probabilistic output capabilities for effective pattern recognition. The proposed method's effectiveness is assessed on a publicly accessible dataset. © 2026 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Coastal erosion in Tra Vinh province and the Mekong Delta region is becoming increasingly severe due to various factors, among which hydrodynamic conditions (tides, waves, river, and sea currents) play a crucial role. This study utilizes the MIKE 21/3 Coupled Model FM to assess the effectiveness of two coastal protection scheme options for the coastal area of Hiep Thanh commune, Tra Vinh: reinforced concrete revetment (Option 1) and hybrid revetment (trees branch fences, geotube bags, and perforated breakwater – Option 2). The results indicate that Option 1 provides better protection for the existing shoreline. However, Option 2 demonstrates superior performance in wave attenuation and beach nourishment, with the ability to reduce wave height approaching the shore by up to 70%. Option 2 also contributes to erosion reduction and shoreline lowering during the Northeast monsoon season (high water levels and strong waves). This study provides a scientific basis for selecting a suitable coastal protection scheme for the Hiep Thanh commune and can serve as a reference for areas with similar natural conditions. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.