IEEJ Transactions on Electrical and Electronic Engineering最新文献

Since the discovery of high-temperature superconductivity, in addition to its widespread application in the superconducting coils and cables, there is a field known as, what we call, “bulk magnets,” in which the bulk crystals are grown from the powder materials by heat treatments. Bulk magnets are used as pseudo-permanent magnets, and used for non-contact magnetic levitation, which is unique to high-temperature superconductivity, and are expected to have wide industrial applications in the future. This report introduces the pulsed field magnetizing method as a simple way to activate the bulk magnets, and refers to the research results. As for the application of strong magnetic field we refer to the studies on TD-NMR using a single magnetic pole, the magnetic separation of Ni-plating wastes, and the applications to rotating machines. As for the magnetic levitation, we take the human levitation models, the transport devices, and the non-contact bearings. In all the above application, the important thing we would propose is to utilize not only liquid nitrogen but also small and easy-to-use cryocoolers, which could enhance the performance of the devices, broadening options into the technologies that are expected to have wide markets in the future. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

To alleviate the operation pressure of island microgrids with a high proportion of renewable energy, it is necessary to fully utilize flexibility resources in multiple time scales. This study proposes a multi-time-scale optimization scheduling strategy for island microgrids that incorporates the participation of flexibility resources. Firstly, an indicator of the net load satisfaction rate is proposed to balance the economic performance and flexibility requirements. A day-ahead optimization scheduling model based on non-cooperative game is established to ensure sufficient flexibility margins. Secondly, the intraday scheduling is divided into long-time and short-time stages. The long-term scheduling updates the day-ahead dispatch plan, while the short-term scheduling considers the response characteristics of diverse flexibility resources and the priority of real-time adjustable resources. A multi-time scale optimization scheduling model is established with the objective of minimizing operational costs. Finally, the proposed strategy is validated and demonstrated for its effectiveness through a case study of a specific island microgrid. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Early fault warning in the distribution network is an effective way to mitigate the impact on power quality and safe operation of the grid. However, it is often difficult to realize in practice due to the complexity introduced by harmonics, sequence components, and other factors. Notably, early faults exhibit several distinct characteristics, such as discreteness, sparsity, and lack of periodicity in the time series, which clearly differ from normal current and voltage states, including harmonics and sequence components. Based on this inherent feature, this paper proposes a Periodic Time Series Correlation (PTSC)-based early fault warning method for distribution networks. First, a convolutional network is incorporated into the end-to-end network to mitigate the influence of interference signals. Then, an extension module, a local correlation module, and a period module are proposed to focus on the global–local correlation differences of time series signals with periodic characteristics. Finally, an optimization algorithm is designed based on a set of cosine functions to model a priori periodicity information of the grid. The proposed algorithm is applied to the IEEE-33 node grid system, and its effectiveness is verified by comparing it with state-of-the-art models such as Transformer and Autoformer. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper proposes a cascaded fuzzy PID control system based on sparrow search algorithm (SSA), which effectively reduces the torque ripple of switched reluctance motors and improves the motor control performance. The introduced sparrow algorithm solves the problem of difficult adjustment of complex parameters in control systems. The control system in this paper uses an inner and outer loop control structure, where the outer loop is the speed control loop and the inner loop is the current control loop. Simultaneously adjust the speed and current of SRM to improve motor speed regulation performance, reduce current ripple and better suppress motor torque ripple. To prove the effectiveness of the control system, a control model is established in MATLAB Simulink. It is compared with fuzzy PID control system and two cascaded PID control systems based on bacterial foraging optimization algorithm (BFOA) and spotted hyena optimization algorithm (SHO). Gray Wolf Algorithm (GWO) and Particle Swarm Optimization (PSO) were also introduced to optimize the controller and compared. The final results show that the cascaded fuzzy PID control system optimized by the Sparrow algorithm proposed in this paper has better speed control performance and lower torque ripple. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

To enable fast and accurate extraction of power line information from transmission corridor point cloud data, an adaptive key information extraction method based on the WOA-NFP algorithm is proposed. The method begins with the preprocessing of the point cloud data, followed by the extraction of power line feature points using WOA-NFP. A clustering algorithm is then applied to classify the feature points, and connectivity analysis is conducted to isolate the point cloud of individual power lines. Spatial equations of the power lines are fitted from both the lead plane and the horizontal plane to facilitate key information extraction. To enhance the robustness of the fitting process, a catenary equation with the suspension point as the coordinate origin is adopted and validated. Experimental results show that the proposed method reduces the maximum arc sag error and maximum stress error by 71.4% and 96.2%, respectively, compared to traditional clustering-based methods. Moreover, it improves time efficiency by 40.55% and reduces the mean peak error by 67.99% compared to existing approaches. These findings demonstrate that the proposed method significantly improves both the accuracy and efficiency of power line information extraction from point cloud data. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

In order to solve the problem of low prediction accuracy caused by the instability of wind power series and the limited monitoring data of some wind farms, and fully consider the complex spatiotemporal characteristics of the coupling of different base stations in wind farms, a wind power prediction model based on graph convolution network and spatiotemporal feature interaction in Informer with transfer learning (GFIITL) is proposed. First, the correlation between the historical power of wind farms in the region is calculated, the feature adjacency matrix is constructed, and the node power information is updated through graph convolution network (GCN). Second, a Feature Interaction (FI) mechanism is proposed to extract information from multiple angles with dual-channel input, and the model is migrated from the source monitoring station to the target station by using cyclic fine-tuning transfer learning, so as to improve the prediction performance under the condition of limited historical data. Finally, compared with the traditional Informer model and other baseline prediction methods, the GFIITL model shows higher prediction accuracy and stability under the condition of limited data, which provides a new perspective for the research and application of wind power prediction. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The integration of photovoltaic (PV) power generation into the electricity market presents unique challenges due to the intermittent and unpredictable nature of solar energy. This study focuses on optimizing bidding strategies for PV power on both the spot and balancing markets using genetic algorithms (GA). The objective is to maximize total bidding profits while minimizing the risks associated with PV output and market price uncertainties. The primary objective of this research is to develop an optimized bidding strategy that maximizes the total revenue from participating in the spot and balancing markets while considering the inherent risks associated with PV output and market price fluctuations. In this study, a GA-based optimization model is developed for PV power bidding on the spot and balancing markets. The results of the numerical simulation validate the model developed and highlight the impact of market risks and PV power output uncertainties on bidding strategies and revenues. The results suggest that incorporating realistic forecast errors and market risks into the bidding strategy enhances the profitability and reliability of the participation of PV power in the electricity markets. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The electrical transients contain rich frequency components due to the system disturbances or faults. In order to extract characteristic information and further analyze them, the ultra-fast frequency detection method is crucial. In this paper, an ultra-fast frequency detection method based on multiple signal classification (MUSIC) is researched. First, the frequency detection speed of MUSIC algorithm is discussed, and its minimum time window for frequency detection is obtained. Then, the frequency detection performance of the MUSIC algorithm is analyzed in detail by varying the time window, the frequencies, amplitudes, phases of the estimated signal, and the set number of frequencies in the algorithm. As for the false spectral peak phenomenon of the MUSIC algorithm, the paper proposes the improved method by combining the MUSIC algorithm with K-means clustering to identify false spectral peaks. Finally, the effectiveness of the proposed improved method is validated through case studies. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

In recent years, it has been pointed out that the power quality of a distribution system is deteriorating due to the increase of harmonics in power systems. When estimating the harmonic sources in a distribution system, it is important to know the locations and capacities of the static capacitors (SCs) located all over the distribution system, as the SCs absorb the harmonics. Due to the current diversification of loads, it may be difficult to accurately estimate the harmonic sources and locations and capacities of SCs by the method of previous study, which uses the correlation between active power and reactive power. This paper proposes a new method for estimating the capacity of SCs using the intercepts of the equal power factor lines of the net resistance and inductance. In addition, the simulations and experiments are carried out in order to verify the validity of the proposed method. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

In this paper, we propose an efficient planning method for the electric vehicle (EV) and Energy Management System (E²MS). The proposed method determines the combinations of EV travel patterns within the E²MS by Tabu Search, while each EV's charging and discharging schedule is optimized using Linear Programming. Numerical experiments show that our method achieves optimal solutions faster than a Mixed-Integer Linear Programming (MILP) solver in small-scale models, and effectively handles practical-scale models that MILP solvers struggle with. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.