IEEJ Transactions on Electrical and Electronic Engineering最新文献

The safe operation of transmission lines will be an important guarantee for domestic economic construction, and it is urgent to improve the fault diagnosis and identification ability of electrical components in transmission lines. In order to realize electrical components identification and thermal defect detection from massive aerial images, in this study, a cascaded detection method is proposed based on infrared images and YOLO models. Firstly, two infrared datasets used for classification and localization are created, totaling 4887 infrared images. Secondly, to enhance the accuracy and robustness of electrical components identification, a similarity-based attention mechanism module, a cross-level weighted feature pyramid network, and Wise IoU are introduced to the original YOLOv7. Finally, the improved YOLOv7 model and comparative models are trained and then tested on the infrared datasets. The mAP of the improved model reaches 97.4%, which is 6% higher than that of the original YOLOv7. More importantly, by cascading the improved YOLOv7 and YOLOv7-tiny for thermal defect detection, the AP value of the proposed method (87.91%) is more than 20% higher than that of YOLOv7 (67.17%). The experimental results show that the cascaded model is superior to mainstream object detection models in electrical components identification and thermal defect detection, and it is expected to be deployed on embedded devices for real-time inspection of transmission lines. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Usually, the frequency characteristics of the complex filter can be examined by applying a pair of quadrature sinusoidal waves ($��\cos ��\omega �t��$ and $��\sin ��\omega �t��$) to a FUT (filter under test). While this method is recognized for its accuracy, it requires two sinusoidal test signals with tightly matched amplitudes and a strict $��90�°�$ phase shift over a broad frequency range. This becomes particularly challenging at higher frequencies. To address this issue, this paper proposes a new measurement method for the complex filter. Instead of sinusoidal waves, the proposed method adopts square waves which can be easily generated by using a digital divider as the test signals to the FUT. The output signals are observed using a spectrum analyzer. Since the Fourier-series expansion of a square wave is decomposed into its fundamental and harmonic components, the frequency response is measured from the fundamental component using a spectrum analyzer equipped with a high-resolution bandpass filter. This paper describes the proposed square-wave method from theoretical aspects. Through experiment, the validity of the proposed method is confirmed. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Under the situation with decarbonization and introduction of renewable energy, procurement of frequency containment reserve using distributed energy resources (DERs) has become important. We focus on aggregation using DERs installed in hundreds of consumers and propose a method for determining reserve power shared by each consumer to procure the required amount based on the residential electricity demand. © 2025 The Author(s). IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

To address the DC bus voltage instability problem arising from uncertain disturbances such as random fluctuation of photovoltaic output power and periodic wide-range fluctuation of well site loads within oilfield DC microgrids, this paper focuses on the oilfield bidirectional grid-connected converter and proposes a virtual inertia control strategy based on improved dual sliding mode control. First, a virtual inertia equation is established and the voltage sliding mode controller is designed in the outer loop to enhance the inertia of the DC microgrid and restrain the DC bus voltage fluctuation. Subsequently, the current fractional-order sliding mode controller is designed in the inner loop, which can quickly track the given value of grid-connected current and improve the dynamic response speed of the system, and reduce the inherent chattering phenomenon of the sliding mode through the adjustable characteristics of the differential order parameter. Finally, simulation analyses and experimental verifications are conducted under various typical disturbing working conditions in the oilfield. The results show that, compared with the virtual inertia control strategy based on traditional PI control and single-loop sliding mode control, the control strategy proposed in this paper has better dynamic and static performance and robustness, improves the stability of the bus voltage, and can better meet the basic requirements of oilfield DC microgrids operation. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Variable leakage flux technique can be applied in hybrid magnetic circuit memory machine (HMC-MM) to further expand the flux regulation (FR) range. Aiming to explore and obtain the reasonable leakage flux paths that can maximize performance, this paper first evolves an initial HMC-MM into an HMC-MM with a different leakage flux path by changing the PM arrangement. The mechanism that the evolved leakage flux path has a wider FR range is theoretically revealed by analyzing and comparing the equivalent magnetic circuit models of the two machines. Subsequently, the electromagnetic characteristics of the two machines under different magnetization states (MSs) are comprehensively computed and compared by the finite element (FE) method. The results indicate that evolved leakage flux path design into HMC-MM can make it have more superior performance. Finally, the evolved machine is manufactured and tested. Experimental results validate the correctness of the FE analysis. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

In recent years, the remarkable development of the solar power industry has been undeniable. This led to the necessity of monitoring and maintenance of solar power plants due to many issues and failures that affect the efficiency and lifespan of the system. One of the most common fault cases of solar systems is the hot spot in photovoltaic panels created by the mismatch of cells in the panel due to incompatible environmental conditions or physical damage causing the heat up in lower power produced cells. The hot spot is detected through thermal image capture by a thermal camera and analyzed through image processing techniques with manual adjustment and testing. With recent significant achievements and growth of machine learning methods in image processing and technical diagnosis, it is greatly possible to develop a machine learning model to perform hot spot detection for higher efficiency and automation. In this study, our research group proposes an application of RetinaNet to develop a model capable of detecting hot spots in photovoltaic panels through processing thermal images. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Temperature sensors are used in many places, and various types of sensors have been developed. There has been much discussion, and many papers have been written about improving sensitivity, power consumption, and measurement range. However, many papers do not focus on improving response performance. Therefore, in this paper, a method for implementing a fast-response temperature sensor is discussed. A thermistor capable of high-speed response was selected, and a temperature sensor was prototyped using a 0.18 μm-CMOS process. Since thermistors have a large nonlinearity, this paper proposes a method to linearize thermistors using a new thermistor model. The prototype temperature sensor achieved a thermal time constant of 0.092 s in water. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

The voltage tracking capability of conventional moving-discretized-control-set model-predictive-control (MDCS-MPC) in the dual-active-bridge (DAB) converter is affected by parameter mismatch and measurement noise. To address these issues and enhance voltage tracking capability, a continuous-control-set model-free predictive voltage control with noise-suppression (CCS-MFPVC-NS) method is proposed. The proposed method eliminates the parametric effect by establishing and updating an ultra-local model of DAB. Additionally, it reduces the impact of measurement noise on MFPVC by using an extended state observer, thereby enhancing the voltage tracking capability of the DAB converter. Experimental results validate the feasibility and performance of the proposed CCS-MFPVC-NS method. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

This paper proposes an improved duty cycle model-free predictive current control algorithm using ultralocal model (ULM) with an adaptive scaling factor for surface-mounted permanent magnet synchronous motors (SPMSMs). First, the ULM of SPMSM is established, which uses an extended state observer (ESO) to obtain lumped disturbances in the system. Second, an easy-implemented adaptive law without any machine parameter is devised to automatically update the scaling factor of ULM and ESO, thereby completely eliminating the sensitivity of conventional model predictive current control (MPCC) to motor parameter variation and system uncertainty. Third, 18 new virtual voltage vectors are constructed, which combine with the duty cycle control theory, significantly reducing the steady-state fluctuations and total harmonic distortion of phase current. Simultaneously, a simple candidate voltage vector selection method is proposed, which abandons the cost function and enumeration process in conventional finite-control-set MPCC, consequently reducing the computational complexity greatly. Moreover, all simulation and experimental results have confirmed the feasibility and effectiveness of the proposed method. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Planning and developing infrastructure for electric vehicle charging stations (EVCS) is necessary to serve the rapidly increasing number of electric vehicles (EVs) today. On the other hand, issues of stable operation, solving complex constraints, optimizing renewable energy sources, and economic efficiency of EVCS investment in the distribution network are of interest. In this study, we proposed a model to determine the optimal location and size of EVCS in the distribution network with integrated distributed generation (DG) in two objective functions; the first is to minimize power loss, the second is to minimize the cost of power loss and the cost of installing EVCS in the distribution network. The Coronavirus Herd Immunity Optimization (CHIO) method is applied to solve the problem; the IEEE 34 bus distribution network is used for testing and it is simulated in Matlab software. The results of the problem are compared with published models, demonstrating the effectiveness of the CHIO algorithm, as well as the EVCS location optimization problem model. © 2025 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.