IEEJ Transactions on Electrical and Electronic Engineering最新文献

In this study, machine learning is used to determine materials and thickness of materials based on gamma scattering spectra. Materials used in this study are: Al, Si, Fe, Mn, Mg, Co, Cu, Zn, and Ti, which have thicknesses varying from 1 mm to 50 mm. In order to estimate thickness as well as material simultaneously, 1-scattering spectrum and 2-scattering spectrum are used. The Random Forest algorithm was used in training and evaluating the machine learning model. Results of this study provided a coefficient of determination R² = 0.990 and mean squared error MSE = 1.250. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Different transformer winding connections feature different performance; as a result, the efficiency of the three-phase DC–DC converter with different transformer winding connections is different. Based on the same criteria, this paper focuses on the characteristics of a current–tripler–rectifier full-bridge DC–DC converter with different transformer winding connections, i.e. the Y-Y, the Δ-Δ, the Y-Δ and the Δ-Y connection, respectively. Subsequently, small-signal modeling by applying the signal flow graph approach is proposed, which is the first time that the signal flow graph approach has been used for three-phase DC–DC converter. Furthermore, comparisons are made for the four different transformer winding connections. The experimental results verify the correctness of the theoretical analysis. The analysis in this paper provides a useful guideline for circuit design and circuit control. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Many existing methods for transmission line component detection are unfeasible to deploy on resource-constrained devices. In this paper, an improved YOLOv8n for transmission line component detection is proposed to make a balance between detection efficiency and model size by using many lightweight blocks in the YOLOv8n's neck. To pay more attention to the information of interest so as to reduce computational load, an EMA module and a lightweight RepViT block are embedded in the C2f module of the 12th layer of the neck network. A C2f_SCConv module is designed and used to replace the C2f module in the 18th layer of the neck network in order to improve feature fusion across different layers. The C2f modules in the 15th and 21st layers of the neck network are replaced with a C2f_Faster module respectively, meanwhile maintaining accuracy and reducing the number of convolutional layers to improve detection speed. The Wise-IoU loss function is chosen to make the improved model more flexible and robust in object detection. Experimental results show that the proposed model obtains a significant increase in detection accuracy with a lightweight model size. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The permanent magnet rotors of the Permanent Magnet Induction Motor (PMIM) are currently designed to rotate freely. There are relatively few studies on the operation of permanent magnet rotors under load. This paper presents a thorough examination of the performance of Double Squirrel-Cage Dual-Rotor Permanent Magnet Induction Motors (DDPMIM) under varying load distribution ratios. The analysis includes permanent magnet demagnetization, steady-state operational characteristics, and the distribution of the three-dimensional temperature field. This study aims to fill the research gap in this area. First, the working principle of DDPMIM is analyzed. Second, the influence of the load distribution ratio on permanent magnet demagnetization is analyzed. Furthermore, the effects of the load distribution ratio on the efficiency, power factor, and temperature field of the motor are discussed during steady-state operation. The research demonstrates that compared with the DDPMIM with load running on the squirrel-cage rotor, whether the permanent magnet rotor is loaded alone or the squirrel-cage and the permanent magnet rotor share the load together, the efficiency of DDPMIM is improved and the temperature is reduced, but the risk of permanent magnet irreversible demagnetization is increased. The research results suggest a new practical application for the DDPMIM. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper presents a new optimization approach for coil design problems. In conventional evolutional approaches, disconnected conductor geometries are included in the search space, and this feature makes it difficult to find acceptable optimization results. Thus, in the present method, coil design problems are treated as optimum routing ones. In order to solve the routing problem, the physarum solver is used, and resultant routes obtained by the physarum solver are controlled on the basis of evolutionary computations. The present method is applied to the design problems of simple line coil and planar inductor. It is shown that the present method can effectively find coil designs with better performance in comparison with a conventional evolutional approach that does not have any technique to generate surely connect geometry. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The line side prospective transient recovery voltage (TRV) for circuit breakers should be measured by using a switching device with low stray elements and minimized or ideally no effects on the test circuit. A common method is the capacitor current injection circuit with a diode as a switching element described in IEC 62271-100 annex E.3.3. This paper discusses the diode effects (current zero crossing, voltage drop, stray capacitance) on the determination of the line side TRV. Furthermore, the influence of stray elements from the current measuring sensors such as CTs and shunts on the current zero crossing was investigated. Other procedures to determine the prospective TRV for short line faults are the reverse current injection method and the current step response method. These methods will be introduced in this paper. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Accurate electricity demand forecasting is crucial for the stable operation of the power system. A hybrid electricity demand forecasting framework, CVS-iLSTNet, was proposed, which integrates Complete Ensemble Empirical Mode Decomposition (CEEMDAN), Variational Mode Decomposition (VMD), Sparrow Search Algorithm (SSA), and Long Short-Term Network (LSTNet) to enhance forecasting accuracy and robustness. First, feature selection combining Recursive Feature Elimination (RFE) and Random Forest (RF) analyzes the relationship between electricity demand and influencing factors. Second, the Kolmogorov-Arnold Network (KAN) layer is introduced to improve its nonlinear representation capabilities. Next, CEEMDAN decomposes the raw time series data to extract intrinsic modal functions (IMFs). VMD further decomposes these IMFs to extract finer modes, which are then combined with the corresponding IMFs. Finally, SSA-iLSTNet enhances the prediction of complex patterns by hyperparameter optimization and combining multiple network structures and algorithms. Experiments were conducted using real electricity demand data from Spain and the United States. The results demonstrate that the proposed model delivers reliable and timely predictions both in short-term and long-term demand forecasting, outperforming state-of-the-art models. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper proposes a new enhanced Single Digit BCD Adder (SDBA) to perform decimal addition optimally. Quantum-dot Cellular Automata (QCA) will construct nanostructured arithmetic circuits for future computing. One of the vital structures in arithmetic structures is the Binary Coded Decimal (BCD)/Decimal Adder. The existing Decimal Adders require many cells and consume more delay, which makes the circuit inefficient in doing decimal addition faster. The binary adder structures are more significant in deciding the performance of conventional BCD adders. Multiplexers are used in the proposed BCD adder to enhance the speed with less complexity. The main contribution of the proposed SDBA design is to replace the final part of the conventional structure Carry Flow Adder (CFA) based BCD adder with a multiplexer, which improves the speed and increases the regularity of the structure. The multiplexers perform the binary to decimal sum conversion in parallel rather than the carry rippling in the lower part of the standard circuit. The SDBA uses the optimized CFA Type-II binary adder and new logic with a 2:1 multiplexer. Furthermore, the proposed BCD adder is an optimized design in terms of delay, which requires 2.75 clock cycles only compared to the recent designs. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper addresses the comprehensive scheduling problem in a flexible assembly job shop with AGV handling (FAJSP-AGVs), where machining processes and assembly activities between different processes are conducted. A mathematical model is established with objectives to minimize makespan, total machine energy consumption, and AGV working time. An improved adaptive NSGA-II algorithm (IA-NSGA-II) is proposed, incorporating a process constraint matrix-based encoding method, adaptive crossover and mutation operators, and a variable neighborhood search (VNS) to avoid local optima. Simulation experiments validate the effectiveness of the proposed algorithm, showing superior performance compared to existing methods such as INSGA-II and IGA. An ablation study further demonstrates the contributions of the adaptive probability mechanism and VNS, highlighting their roles in enhancing solution quality and avoiding local optima. The proposed method is also tested in dynamic rescheduling scenarios, demonstrating its stability and adaptability in handling machine breakdowns. Experimental results indicate that IA-NSGA-II achieves better scheduling outcomes with shorter makespan, lower energy consumption, and reduced AGV working time. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The cable intermediate joint plays a crucial role in power cable systems, as its temperature directly impacts insulation performance and longevity. Predicting temperature accurately poses challenges for operation and maintenance. This study introduces a model for electromagnetic-thermal coupling of a 110 kV single-core high voltage cable, enabling numerical simulation to determine temperature distributions within the cable body and middle joint. By employing a hybrid orthogonal design approach, training and test samples are generated from simulated temperature field data. Conductor current, ambient temperature, convective heat transfer coefficient, and insulation thermal conductivity coefficient of the intermediate joint are chosen as variables to compile the dataset. An inverse model-based prediction method is developed using a firefly-optimized BP neural network algorithm. Results demonstrate that the optimized model exhibits a correlation coefficient of 0.99, surpassing the prediction accuracy of traditional optimized BP neural networks. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.