Journal of Electrical Engineering & Technology最新文献

Axial flux motors have more advantages than radial flux motors in terms of volume, torque to inertia ratio, efficiency and power density. However, when using axial flux permanent magnet synchronous motor (AFPMSM) as hub motor for electric vehicles, their torque ripples it is a thorny problem that affects its development. It is a common method to reduce the torque ripple by weakening the cogging torque. This paper studies the impact of permanent magnet deflection on motor torque ripple, efficiency and other aspects in the absence of cogging torque, that is, no stator core. Finite element simulation results show that the permanent magnet deflection of 10° is the optimal deflection angle. Currently, the electromagnetic torque pulsation is smallest, the radial air gap magnetic density waveform is closest to the sine wave, the magnetic induction intensity is the largest, and the efficiency is highest. This research result provides an effective method for reducing torque ripple of in-wheel motors.

The integration of distributed generation (DG) into active distribution networks poses significant challenges to traditional protection schemes due to altered power flow directions and the impact on short-circuit fault currents. This paper proposes a novel multi-layered protection scheme based on fault current components to address these challenges. The scheme incorporates pilot protection, DG active islanding protection, and traditional three-stage overcurrent protection, leveraging the amplitude ratios and phase differences of positive and negative sequence current components. A compensation mechanism is introduced to adjust the protection setting, enhancing the sensitivity and reliability of the protection criteria. Through MATLAB/Simulink simulations on a 10 kV active distribution network model, the proposed scheme demonstrates robust performance under various fault conditions, providing comprehensive and reliable protection. This scheme advances the state-of-the-art by integrating multiple fault characteristics and optimizing sensitivity, offering a new approach to enhance the protection of future active distribution networks.

Traditional power grids experience significant energy loss during distribution. Distributed generation (DG) technologies offer a promising solution by introducing smaller power sources closer to demand centers. These localized sources relieve a certain pressure on centralized power plants that reduces transmission losses. However, improper sizing and on-site placement of DGs can cause technical challenges, economic drawbacks, and environmental concerns. Historically, the process of identifying optimal sites and capacities for DGs has presented grid operators with formidable computational challenges, intensifying their workload. Nonetheless, the advent of sophisticated optimization software has revolutionized this task, offering a more efficient and manageable solution for operators navigating the complexities of grid management. This research deals the utilization of a fuzzified firefly optimization (FFO) algorithm to enhance the allocation of distributed generators (DGs) within a distribution system. Through strategic placement of diverse DG types, the FFO strategy targets the minimization of both real and reactive power losses. Subsequently, the study assesses the influence of this optimization on critical system performance metrics. To gauge the efficacy of the approach across varied load conditions, the investigation integrates different DG types into an IEEE 85-bus test system and conducts simulations spanning from below-rated (0.5) to above-rated (1.5) load capacities.

The ring-shaped bonded magnet has the advantages of low cost, simple molding, easy assembly, and low eddy current loss, but it has weak magnetic performance. To improve the comprehensive performance of the motor, the motor structure needs to be optimized. In this paper, the ring-shaped bonded magnet is applied to the permanent magnet brushless DC(PMBLDC) motor. According to the characteristics of ring-shaped bonded magnet, a multi-parameter and multi-objective optimization method is proposed to shorten the design time and improve the optimization effect. Based on the equivalent magnetic circuit model, the key parameters affecting the motor performance are derived, then the requirements of permanent magnet demagnetization and motor stability are used as constraints to determine the range of parameter values. The sample data sets of the relationship between the structural parameters and the motor performance are established by the Taguchi method. The quadratic response surface regression model is used to fit the sample data set. Then the modified ant colony algorithm is used to search the optimal target. According to the optimized motor structure parameters, the motor prototype is processed. The results show that the optimized structure can effectively improve the output torque and reduce the cogging torque, torque ripple and operating noise without reducing the efficiency. The research results provide a basis for further development of the application of ring-shaped bonded magnet in micro-motors, and drive the development of motors with lightweight, miniaturization and low cost.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and a major cardiovascular disease epidemic of the 21st century. Early diagnosis and intervention are crucial as AF often progresses without symptoms. This study aims to identify AF using genome-wide association studies and convolutional neural networks (CNN). Genomic data from 6,358 individuals were used to develop a CNN model, with L2 regularization applied to prevent overfitting. The L2-regularized CNN significantly outperformed the regular CNN across various p-value thresholds. For instance, at p < 0.0001, the L2-regularized CNN achieved an accuracy of 0.731 ± 0.071 compared to 0.703 ± 0.055 for the regular CNN. At p < 0.001, the L2-regularized CNN showed an accuracy of 0.630 ± 0.089, while the regular CNN had 0.577 ± 0.095. This demonstrates a notable improvement in model performance with L2 regularization. Although the regular CNN showed higher accuracy in some scenarios, such as achieving 0.984 ± 0.015 at p < 0.01 compared to 0.970 ± 0.020 for the L2-regularized CNN, the performance difference between the models decreased as the p-value threshold became more stringent. Overall, L2 regularization not only improved the model’s performance and stability but also reduced the performance gap between the models under stricter p-value conditions. These findings highlight that L2-regularized CNNs can significantly enhance performance in genomic studies, offering a more effective alternative to traditional polygenic risk score methods for AF identification study.

A small ember, created by a chemical reaction between a substance and oxygen, can grow into a large fire as temperature, wind, and weather conditions change. A growing fire incident can have devastating consequences, including property loss, environmental damage, and loss of life, which is why early fire detection is so important. There are various detection devices such as smoke detectors, heat detectors, fire detectors, and gas detectors that can be used in the early stages of a fire. While early fire detection system developments incorporating IoT technology are emerging in various industries, Smoke alarms, the most common type of smoke detector in homes and offices, accounted for 96.6% of all malfunctions from 2021 to July of the previous year, totaling 249,445 incidents. The analysis of detector malfunctions showed that non-fire alarm factors such as food, cooking, and dust accounted for the largest share of 40.6%. This paper proposes an algorithm for early fire detection by incorporating a deep learning-based model to compensate for the problem of non-fire warning malfunctions, which is a shortcoming of existing detectors. Finally, for fire detection, a bounding box for the fire is specified using a smoke detector, a thermal imaging camera, and a webcam camera trained with the Yolov7 model. Then, we propose an algorithm to remove the bounding box of non-fire reports and malfunctions from the heating map using smoke detectors and thermal imaging cameras. After applying the algorithm proposed in this paper, only fires with heat sources are recognized, and all bounding boxes for non-fire reports are removed.

This paper proposes a practical optimization algorithm for trajectory optimization in airborne wind energy system (AWES). Existing approaches generally implement optimal control approaches to handle model non-linearity and constraints. However, due to dynamic constraints in the optimal control problem, strongly non-convex objective function and numerous decision variables increase the complexity and computational burden of the optimization algorithm. To address this issue, an optimization algorithm with minimized decision variables is proposed based on a high-fidelity AWES model that incorporates a closed-loop flight control system. Utilizing the convex nature of the objective function, the Karush-Kuhn-Tucker (KKT) conditions are applied to derive optimality conditions. The decision variables are updated by a conjugate-descent algorithm with numerically approximated gradient. Computer simulations confirm the superiority of proposed algorithm in terms of computational efficiency and accuracy of the solution.

This paper presents a novel concept that the simplest memristor oscillator designed with the series connection of positive temperature coefficient (PTC) and negative temperature coefficient (NTC) memristors possesses an inherent property referred to as the “edge of chaos kernel”, hidden inside the small-signal equivalent circuit serves as an optimal mechanism for inducing a periodic oscillation. Furthermore, it is also demonstrated that the edge of chaos kernel, originated from Chua’s riddle and characterized by negative resistance (R < 0) and negative inductance (L < 0), exhibits potential stability but becomes unstable upon the addition of dissipative resistance or inductor component. This paper outlines the discoveries concerning the properties and behavior of the edge of chaos kernel circuit in the elementary memristor oscillator as well as the potential application in voltage control sensing circuit, elucidating its significance in nonlinear dynamics.

The high penetration of renewable energy sources, especially solar photovoltaic, poses a significant challenge in distribution networks. Data-driven local control is an effective and budgeted way to ensure reliable distribution operation. However, this mode will face computationally expensive and ineffective problems with extensive historical data in the same operational period. In addition, the phenomenon of missing data will worsen due to the errors of measurement instruments. Therefore, an active learning local control method is proposed to select samples with diversity to improve the efficiency of the control scheme and maintain the performance designed by the original samples under the missing condition. Firstly, an optimal power flow model in a low-voltage distribution network is constructed considering the neutral line’s impact. Then, the historical data containing missing values are processed by an imputation method, and an active learning method based on a greedy algorithm is introduced to select diverse samples, which speeds up the offline process of local control. Finally, we formulate the operation rules of the photovoltaic inverter and energy storage systems, which work as local devices in real-time control. The simulation results show that the proposed method realizes safe operation, saves the required time in the training stage, and achieves nearly approximate performance compared to the scheme designed by the original samples. Furthermore, this paper investigates the impact of different rates of missing data on local control and presents the proposed method to achieve the security and cost-effectiveness of the system under any missing condition.

A pantograph is a device that supplies electricity to electric vehicles through mechanical contact with the contact wire of the overhead contact system (OCS), and it should be designed considering the OCS characteristics and vehicle speed. In this study, to develop a pantograph suitable for operation at a maximum vehicle speed of 320 km/h under the conditions of the Honam high-speed railway in Korea, a commercial dynamics program, ANSYS Motion, was used to analyze the effect of each pantograph design parameter on the current-collection performance. These effects were reviewed by changing the pantograph design parameters, such as pantograph head weight, frame equivalent mass and stiffness, and head spring coefficient, under various span length conditions of the Honam high-speed railway. The analysis results are expected to be used as basic data for developing high-speed pantographs considering the effects of the OCS conditions.