Journal of Electrical Engineering & Technology最新文献

Human brains are the most complex organs. There are a number of functions that this three-pound organ performs, including intelligence, interpreter of the senses, initiator of bodily movements, and controller of behaviour. In this paper, a novel ER-GAN model has been proposed for image inpainting (IIP) Brain MRI images. Initially, the brain MRI images are segmented using Attention V-Net. In the first GAN, Edge reconstruction Generative Adversarial Networks (EGAN) are used as edge generators able to hallucinate edges in missing regions based on the rest of the image’s edges and grayscale pixel intensities. Edge generation in brain MRI images involves leveraging these grayscale pixel intensities to detect boundaries between different brain tissues or structures. The varying intensities in MRI images often correspond to changes in tissue composition or boundaries between anatomical regions, making them valuable for edge detection and delineation. The second GAN uses the Region Reconstruction Generative Adversarial Network (RGAN) to fill in the missing regions by combining edge information from the missing regions and color and texture information from the surrounding regions. In experimental analysis, the Jaccard Index (JI) and Dice Index (DI) are obtained at 0.78 and 0.84 respectively. The proposed ER-GAN model reaches an overall accuracy of 99.25%, which is comparatively better than the existing techniques.

The zero drift occurring to the sampling conditioning circuit of the non-isolated grid-connected inverter will make the output develop a DC component, thus resulting in system failure and posing safety risks. According to the IEEE standard 1547-2003, the DC component injected into the grid side should be less than 0.5% of the rated current. In this paper, a moving average filter is proposed to extract the DC component of the three-phase AC output current. The filter has a very strong attenuation ability to the fundamental integer multiple harmonics, and can accurately extract the DC component. Then the proportional integral resonant controller (PIR) is used to control the system. The control system has sufficient bandwidth to avoid the stability problem caused by frequency offset. Through the above methods, the purpose of accurately suppressing the DC component in the non-isolated grid-connected inverter is realized. Also, a 50 kVA prototype is built in this study. The experimental results show that the moving average filter is advantageous over the conventional low-pass filter method in extracting the DC component, and the PIR controller used in the closed-loop control system outperforms the proportional integral and proportional resonant controllers. Under the strategy proposed in this study, the DC component is reduced to less than 0.5% of the rated current, and the THD of the grid-connected current falls below 5%.

The effects of probe contact resistance, thermoelectric voltage and time dependence on electrical resistance measurement of superalloy single-crystal steel were examined as important error factors in determining the electrical conductivity of the sample using the four-point probe (FPP) method. It was shown that even when the contact resistance and its time dependence are large, its effect on the sample resistance measurement results is insignificant. Additionally, it was found that the thermoelectric voltage effect can cause a measurement error of several percent, and to cancel this effect, the voltage due to both polarities of the applied current must be averaged. In addition, it was found that in the first few minutes, the measured voltage values change rapidly within the first few minutes and may cause an error of several percent; to obtain accuracy and an uncertainty of about 0.1%, the measurements must be performed after voltage stabilization. The results indicated that to determine sample resistance accurately using the FPP method, the effects examined above must be considered in addition to the basic parameters of current, voltage, and geometric correction factor mentioned in literature (Kang et al. in J Electr Eng Technol 18:1419–1427, 2023).

Investigations on static air-gap eccentricity (SAGE) faults about magnetic field variations, current/ voltage changes, and stator/ rotor vibrations have been widely carried out, while the mechanical properties of the end windings have been rarely studied, especially in 3D eccentricity cases. This article provides a detailed study on the stator end winding mechanics behavior in synchronous generators at typical rotor eccentricity running conditions. Such mechanics behavior includes not only the electromagnetic force (EF) properties, but also the end winding vibrations as well as the stress/strain/deformation responses due to the uneven EF excitation. The typical rotor eccentricity running conditions include: 1) radial static air-gap eccentricity (RSAGE), 2) axial static air-gap eccentricity (ASAGE), and 3) hybrid static air-gap eccentricity (HSAGE). The theoretical analysis, finite element analysis calculation and experimental verification are performed respectively, by taking a 5kVA synchronous generator as the research object in this paper. It is shown that in normal and SAGE cases, the end winding EF/vibration contains the DC component and even harmonics, especially the 2nd harmonic. RSAGE increases the end winding EF/vibration, whereas ASAGE increases the end winding EF/vibration at the extended end of the rotor while decreasing the EF/vibration on the retracted end. Under HSAGE, both RSAGE and ASAGE will affect the variation trend of end winding EF/vibration with the rule of single direction SAGE fault. The nose part and the joint to connect the end part and the linear sections are the most dangerous positions to afford the mechanics responses, and the occurrence of SAGE will make these parts more vulnerable.

Power semiconductors play a crucial role in power conversion applications within nuclear power plants. These semiconductors are enclosed using polymeric materials for cost-effectiveness. Researchers have substantiated that polymeric materials are subject to radiation-induced degradation in nuclear power plants, prompting reliability studies. Consequently, investigating the radiation degradation behavior of polymeric materials becomes imperative to ensure their reliability and stability. This study focuses on the degradation of epoxy molding compound (EMC), a type of polymeric material, under the influence of total ionizing dose (TID). To analyze the effects of TID conditions on EMC, data was collected and subjected to various tests, including FTIR (Fourier Transform Infrared Spectroscopy) spectroscopy, thermal conductivity testing, and nanoindentation testing. These tests were conducted to assess chemical changes, thermal properties, and mechanical properties of EMC as a consequence of TID exposure. TID induces random ionization damage of EMC. Five EMC samples with different total cumulative doses were produced by varying the TID exposure time. Spectral data were obtained from the fabricated EMC samples by FTIR spectroscopy. FTIR spectral data was used to build a machine learning model, and the degree of EMC performance degradation due to TID exposure was determined. In our study, we selected an optimal algorithm among six machine learning algorithms. Dimensionality reduction methods such as ReliefF and PCA were also applied to build a more simplified discriminant model. As a result, it was confirmed that radiation changed the thermal properties of EMC materials. However, no change in the mechanical properties of EMC was observed under our test conditions.

Due to the increasing demand for underground installation of ground equipment, KEPCO has been researching the installation of Underground Compact Distribution Stations (U-CDS). To install and operate U-CDS in the distribution system, it is necessary to establish relevant standards and design and operation criteria. Therefore, this paper discusses the test method of IEC 62271-202 to establish its standard. This standard mainly comprises dielectric tests, temperature rise tests, short-time/peak current tests, and internal arc tests. However, each electrical distribution equipment has already been tested for dielectric, short-time current, internal fault arc, and temperature rise tests. Since U-CDS requires all equipment to be gathered in one place and operate safely, it is necessary to test the interconnections. As a result of the tests, some tests satisfied the standards, but others did not. Since these tests have not been performed before, additional research on the standards and test methods will be needed.

Fault diagnosis and prediction are important to prevent traffic congestion during rush hour due to door failures of urban railway vehicles. This paper is a study on improving failure classification performance through machine learning using the data set collected by installing a displacement sensor on a door simulator. First, the durability test of the sensor and the developed simulator was verified through 147,000 no-failure tests. For machine learning, 11,225 sets of normal and abnormal data of the door were collected and supervised learning was performed. In order to overcome the difficulty of fault diagnosis of the existing pressure sensor or acoustic sensor, pre-processing was performed that converted to speed-based data. In addition, feature extraction was compared with the single zone method by testing the 2-zone segmentation method. Feature selection was made using the principal component analysis algorithm developed for feature dimensionality reduction. As a result, the classification performance of the method using the single zone method with open and close data was better than the 2-zone segmentation method by acceleration and deceleration. Among the machine learning models, the LGBM model showed the highest prediction accuracy of 99.55%, which is expected to be applied to actual vehicles.

Graphical Abstract

This paper presents a novel model-free predictive control (MFPC) approach for precise voltage control in four-leg voltage-source inverters (4L-VSI). MFPC eliminates the need for complex system models, unlike traditional model predictive control (MPC) methods. It leverages the ultra-local model for controlled variable estimation, enabling robust performance without prior model information. The core contribution lies in a comprehensive mathematical model for the proposed MFPC strategy, encompassing all possible 4L-VSI switching states. This is contrasted with a detailed analysis of conventional MPC limitations for 4L-VSI control. Simulation results validate the effectiveness of MFPC in achieving accurate voltage control without explicit modeling. Furthermore, MFPC demonstrates superior robustness against parameter variations and model mismatches compared to conventional MPC. Hardware-in-the-loop testing with the RT-BOX platform strengthens the theoretical analysis. The achieved total harmonic distortion of 1.86% confirms compliance with IEEE standards, showcasing the practical effectiveness of the proposed MFPC approach.

Fault tolerant permanent magnet vernier rim-driven motor (FTPMV-RDM) have a broad application prospect in ship electric propulsion systems due to its high torque density and strong fault-tolerant capability. In order to solve the problem of large number of alternating voltage vectors and complicated calculation process, in the paper, a novel model predictive current control algorithm based on improved sector selection (ISS-MPCC) for FTPMV-RDM is proposed to suppress torque ripple and reduce current harmonic content. Firstly, a set of fundamental voltage vectors is used for initial screening. Then, a secondary screening is performed using a value function to determine the pre-selected voltage vector, thus reducing the number of voltage vector enumerations in the system and effectively suppressing the generation of harmonic currents. Finally, optimization is carried out within this sector to obtain an optimal combination of switching states for one control cycle. To obtain good performance under single-phase open-circuit fault condition, a fault-tolerant control strategy based on the healthy decoupling transformation matrix is proposed. This strategy simplifies the fault-tolerant control by only requiring changes in the reference currents in the harmonic subspace. Hardware experimental results have validated the effectiveness of the proposed control strategy in this paper.

This study presents the effect of cell temperature on the hybrid solar collectors PV/T for modules with and without using a front glass cover. A numerical model of thermal behaviour was carried out for glazed (PV/Tg ) and unglazed (PV/Tun) modules. As a result of this experimental investigation conducted for presented PV/T modules, a sufficient decrease in the cell temperature of PV/Tun compared to the PV/Tg, PV/Tun module exceeded the PV/Tg by 19%. Thus, the electrical productivity of the PV/Tun module was higher; it was 3.44% higher than the PV/Tg value. The outlet liquid temperature of the un-covered module was 6.34% higher than the outlet liquid temperature of the glass-covered module. Besides, the total efficiency of the PV/Tg was higher during the periods of study. In this sense, the glass-covered module is preferable for applications that deal with overall efficiency as a desirable factor. The un-covered module is much beneficial for applications that need high electrical productivity.