Recent Advances in Electrical & Electronic Engineering - Volume 17, Issue 8, 2024

Background: The upsurge in the field of radio frequency power electronics has led to the involvement of wide bandgap semiconductor materials because of their potential characteristics in achieving high breakdown voltage, output power density, and frequency. III-V group materials of the periodic table have proven to be the best candidates for achieving this goal. Among all the available combinations of group III-V semiconductor materials, gallium nitride (GaN), having a band gap of 3.4eV, has gradually started gaining the confidence to become the next-generation material to fulfill these requirements. Objective: Considering the various advantages provided by GaN, it is widely used in AlGaN/GaN HEMTs (High Electron Mobility Transistors) as their fundamental materials. This work aimed to review the structure, operation, and polarization mechanisms influencing the HEMT device, different types of GaN HEMT, and the various process technologies for developing the device. Methods: Various available methods to obtain an enhancement type GaN HEMT are discussed in the study. It also covers the recent developments and various techniques to improve the performance and device linearity of GaN HEMT. Conclusion: Despite the advantages and continuous improvement exhibited by the GaN HEMT technology, it faces several reliability issues, leading to degradation of device performance. In this study, we review various reliability issues and ways to mitigate them. Moreover, several application domains are also discussed, where GaN HEMTs have proven their capability. It also focuses on reviewing and compiling the various aspects related to the GaN HEMT, thus providing all necessary information.

Background: The Internet of Things (IoT) is the interconnection of physical devices, controllers, sensors and actuators that monitor and share data to another end. In a smart home network, users can remotely access and control home appliances/devices via wireless channels. Due to the increasing demand for smart IoT devices, secure communication also becomes the biggest challenge. Hence, a lightweight authentication scheme is required to secure these devices and maintain user privacy. The protocol proposed is secure against different kinds of attacks and as well as is efficient. Methods: The proposed protocol offers mutual authentication using shared session key establishment. The shared session key is established between the smart device and the home gateway, ensuring that the communication between the smart devices, home gateway, and the user is secure and no third party can access the information shared. Results: Informal and formal analysis of the proposed scheme is done using the AVISPA tool. Finally, the results of the proposed scheme also compare with existing security schemes in terms of computation and communication performance cost. The results show that the proposed scheme is more efficient and robust against different types of attacks than the existing protocols. Conclusion: In the upcoming years, there will be a dedicated network system built inside the home so that the user can have access to the home from anywhere. The proposed scheme offers secure communication between the user, the smart home, and different smart devices. The proposed protocol makes sure that security and privacy are maintained since the smart devices lack computation power which makes them vulnerable to different attacks.

Background: The distribution feeders usually include many laterals, too many sensors need to be installed to locate the fault positions in electrical distribution systems by using the traditional double-ended traveling-wave-based fault-location methods. Objective: Fault location based on the time delays between the moments of the first wavefronts of zero-mode voltages and that of aerial-mode voltages arriving at one end of the distribution feeder. Methods: Large decomposition level in wavelet transformation is adopted in this paper to obtain the zero-mode velocity. Results: Both theoretical research and simulation experiments have proved that the proposed fault location method can greatly improve the accuracy of the fault location in the electrical distribution systems without many sensors. Conclusion: The proposed fault location method can provide one feasible way to locate the faults in the electrical distribution systems.

Background: Daily inspections of insulators are necessary because they are indispensable components for power transmission lines. Using deep learning to monitor insulators is a newly developed method. However, most deep learning-based detection methods rely on a large training sample set, which consumes computing resources and increases the workload of sample labeling. The selection of learning models to monitor insulators becomes problematic. Objective: Through comparative analysis, a model suitable for small-sample insulator learning is found to provide a reference for the research and application of insulator detection. Methods: This paper compares some of the latest deep learning models, YOLOv7, SSD, and DETR, for insulator detection based on small-sample learning. The small sample here means that the number of samples and their proportion to the total sample are relatively small. Two public insulator image sets, InsulatorDataSet with 600 insulator images and Transmission-line-pictures (TLP) with 1230 insulator images in the natural background are selected to test the performance of these models. Results: Tests on two public insulator image sets, InsulatorDataSet and TLP, show that the recognition rates of YOLOv7, DETR, and SSD are arranged from high to low. The DETR and the YOLOv7 have stable performance, while the SSD lacks stable performance in terms of the learning time and recognition rate. Conclusion: The in-domain and cross-domain scenario tests show that YOLOv7 is more suitable for insulator detection under small-sample conditions among the three models.

Background: In recent decades, numerous researchers have been keenly interested in plasmonic absorbers due to their efficiency in a variety of applications such as solar cells. This is because the surface plasmons formed at the interface between metal and insulators interact strongly with light, thereby augmenting electromagnetic (EM) waves. In most cases, plasmonic absorbers featuring metal-insulated-metal structure (MIM) are favored for their robust absorption rates, straightforward fabrication process, minuscule size, and portability. Methods: This paper proposes a tunable plasmonic perfect absorber (PPA) based on graphene and two metal-insulator substructure within the 28-60 μm wavelength range. This device is simulated by 3D finite element analysis using CST software. Also, in our proposed structure, instead of using a single micro-blade, two micro-blades are arranged opposite of each other in the absorber structure through which the electric field is locally strengthened and a sharper resonance peak with greater amplitude is obtained. Results: Simulation results demonstrate that a resonance peak is observable in the absorption spectrum of the structure and that this peak can be shifted between 30μm and 40μm by applying a gate bias voltage to the graphene nanolayer without modification of the structure's dimensions. Conclusion: The features of this absorber make it suitable for sensor applications, which will be further explored in future research. We also investigated the influence of dimensions on absorption to take into account the allowable tolerances and sensitivities associated with its fabrication. Furthermore, we proposed a structure that can enhance absorber performance in the future.

Background: This paper proposes a synthetic aperture radar (SAR) target recognition method based on adaptive weighted decision fusion of multi-level deep features. Methods: The trained ResNet-18 is employed to extract multi-level deep features from SAR images. Afterwards, based on the joint sparse representation (JSR) model, the multi-level deep features are represented to obtain the corresponding reconstruction error vectors. Considering the differences in the abilities of different levels of features to distinguish the target, the reconstruction error vectors are analyzed based on entropy theory, and their corresponding weights are adaptively obtained. Finally, the fused reconstruction error result is obtained through adaptively weighted fusion, and the target label is determined accordingly. Results: Experiments are conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under different conditions, and the proposed method is compared with published methods, including multi-feature decision fusion, JSR-based decision fusion and other types of ResNets. Conclusion: The experimental results under standard operating condition (SOC) and extended operating conditions (EOCs) including depression angle variance and noise corruption validate the advantages of the proposed method.

Introduction: Electric vehicles have become a development trend due to their good environmental protection and energy saving, etc. Prediction of electric vehicle charging volume can help relevant departments optimize power supply, service, and construction. Methods: In this paper, the Support Vector Machine (SVM) model and the combined Long Short Term Memory (LSTM) and Support Vector Regression (SVR) prediction model are constructed for the prediction of charging capacity, and simulated by actual trading power data in Hubei Province. Results: The results show that the prediction effect of the two methods is good, and the LSTMSVR algorithm is judged to have better performance and less error in predicting the fluctuation of transaction power. Conclusion: LSTM-SVR can be used as a charging prediction method to provide a reference basis for the power control strategy of electric vehicles charging management platform, which is conducive to the healthy development of electric vehicles industry.

Background: The test power supply is one of the key devices in the DC distribution network, which is necessary to supply power with high quality for testing other devices' performance. The double active bridge (DAB) converter is a common circuit topology for test power supply, which has the advantages of high-frequency electrical isolation, bi-directional power flow, and high power density. For the converter, control methods, such as single-phase-shift (SPS) control, and single-current stress or single-return power optimization under extended-phase-shift (EPS) control, have their limitations, which influences efficiency. Objective: This paper aims to propose a dual-objective optimal control method, which can effectively improve efficiency of the test power supply. Methods: This paper addresses the limitations of SPS control, and single-current stress or singlereturn power optimization under EPS control of the DAB converter, and proposes a dual-objective optimal control method based on the idea of using objective planning in the full power range under the condition of satisfying soft switching, which effectively improves efficiency of the test power supply. Results: With the proposed dual-objective optimal control method, the converter achieves a smaller current stress similar to that with the single-current stress optimal control, and the return power is also reduced by 29.51%. Efficiency of the test power supply reaches 86.9%, which is better than 82.5% with SPS control and 85.3% with single-current stress optimization under EPS control. The experimental results fully verify the effectiveness of the proposed control method. Conclusion: A dual-objective optimal control method is proposed. By using the presented method, current stress and return power are both optimally designed, so the efficiency of the test power supply can be effectively improved.

Background: How to solve the inconsistency of battery pack is a key point to ensure reliable operation of electric vehicles. Battery equalization is an effective measure to address the inconsistency. Passive equalization method has poor efficiency and thermal management problems. Average voltage equalization method is only suitable for situations where there is a significant voltage difference between batteries. The SOC-based equalization method is relatively difficult and may inevitably lead to the accumulation of errors during the process. Objective: In order to avoid the disadvantages of traditional control methods, a new control method is proposed to improve the accuracy and self-adaptation of active equalization, which is easy to be realized without online calculation. Methods: Cascaded bidirectional Buck-boost circuit is adopted as the novel equalization topology. Based on fuzzy PID theory, an adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed in this paper. Parameter design of the fuzzy PID controller is carried out. A battery equalization system based on cascaded bidirectional Buck-boost circuit is designed and developed. Experimental verification is conducted on relevant hardware platforms. Results: An adaptive digital-analog hybrid control strategy based on fuzzy PID is proposed. Compared to passive equalization, this proposed method provides high efficiency. Regarding traditional voltage control, the method improves control reliability and flexibility. Compared to the average voltage equalization method, the approach needs less convergence time. Moreover, the control method is much easier to realize than the SOC-based equalization method. Conclusion: By using the presented adaptive control based on DC energy conversion circuit, the degree of self-adaptation of the equalization process has been obtained as higher and the inconsistency as smaller.

Background: As the frequency of transformer winding faults becomes higher and higher, the frequency response analysis used to detect the winding status has attracted more and more attention. At present, there is still a lack of reliable and intelligent technologies for detecting the state of transformer windings in this field. Objective: This paper focuses on studying a high-precision method for transformer fault diagnosis, which can be easily and effectively applied to daily life. Methods: By changing the detection method, the traditional detection method can not distinguish the problem that the detection data are highly overlapping when identifying the same fault of the head and tail symmetric points, and the problem that the phase is too similar is changed. In order to solve the problem that the fault samples of transformer frequency response curve are scarce and the one-dimensional data cannot be read by partial deep learning method, the one-dimensional data of frequency response curve is first converted into chracteristic index and then into a three-dimensional image by moving window calculation method and Gramian Angular difference field transformation. The fault classification is realized by a convolutional neural network. Results: The accuracy of the final model for slice classification reached 100%. Conclusion: Illustrative examples show that the method is distinguishable from different fault types. The traditional method only uses the amplitude of the frequency response curve, but this method displays the two features of the amplitude-phase together in the image. Compared with the traditional method, more features and samples are added to further improve the accuracy of the method. The accuracy of diagnosis results reached 100%, which showed the feasibility of the method.