International Journal of Research and Technology in Electrical Industry
Year:2025 | Volume:4 | Issue:1|Papers Count:6

Although conventional high-speed gas turbines and electric motors equipped with mechanical gearboxes are practical solutions, they face significant environmental constraints and suffer from the inefficiencies associated with mechanical converters. As a result, high-speed electric motors, especially those designed to overcome these limitations, have become increasingly favorable. Bearing-less induction motors (BLIMs) offer notable advantages, including the elimination of friction losses, minimization of wear, reduced maintenance requirements, and the inclusion of an internal monitoring system. However, due to their unique structure and the complex interaction between torque and force winding fields, BLIMs are not well-suited for high-power applications. This research investigates the analytical design of a high-speed BLIM, aiming to enhance performance, efficiency, and torque density. To achieve this, a multi-objective optimization process of the derived dimensions is employed. Furthermore, a finite element analysis of the motor is conducted, and the results are compared with those of a BLIM optimized using a genetic algorithm.

Power system reliability hinges on accurate and timely fault classification, yet many real-world scenarios face data scarcity due to logistical and economic constraints. Traditional methods often struggle to maintain performance with limited training samples, creating a critical gap in practical applications. Fault classification in power systems often requires robust models that can be generalized from limited data. Traditional deep learning approaches, while highly effective, usually need large datasets to achieve acceptable performance. In this paper, we propose a novel convolutional neural networks (CNN) framework for fault classification tasks using small-scale databases. This is novel because it leverages transfer learning to adapt a pre-trained model in deep learning to the target domain of fault classification. Compared with other methods, our approach minimizes the dependency on large datasets besides achieving high accuracy and generalizability. Extensive experiments demonstrate that the proposed approach achieves state-of-the-art performance, validating its efficacy for scenarios with limited data availability. This research provides an essential step in applying deep learning to the fault classification problem of limited data resources, further pushing toward practical and accessible solutions for the field.

This paper addresses sliding mode control (SMC) design for disturbed fractional-order multi-vehicle networks in order to achieve containment tracking within a certain settling time. The multi-leader case is investigated where the aim of the containment protocol design is that the states of the fractional-order followers eventually are placed inside a convex hull made by the states of the leaders. The convergence rate is designed such that achieving the containment tracking occurs in a fixed-time manner. Unlike the previous works on finite-time containment control protocols of multi-agent systems, here, we offer a tractable design as the upper limit of the settling time of the convergence is achieved independent of the preliminary conditions of the vehicles' states. A novel SMC approach is proposed which enables the multi-vehicle network to reach the containment tracking at presence of the external disturbances. The numerical simulations reveal the correctness and effectiveness of the proposed theoretical approaches.

Event-based load shedding (ELS) is a vital emergency countermeasure against transient voltage instability in power systems. Deep learning(DL)--based ELS has recently achieved promising results. However, in power systems, faults may occur that are not in the training database, reducing the model's effective performance. In this situation, it is necessary to update the model. On the other hand, updating the model for new faults requires a large database. To address the problem of unknown faults, this paper proposes a transfer learning-based graph convolutional network (GCN) model that allows updating the model with a small database. In the first step, an ELS model is trained with a large database. Then, if a new fault occurs, the model is transferred to the new fault and updated using transfer learning and with a small database. To evaluate the performance of the proposed model, it was implemented and tested on the IEEE 39 bus system. The results show that the proposed model has high-performance accuracy and can be updated with a small database when encountering an unknown fault. According to the results, the proposed model has reduced the database size by 78.91% for optimal updating.

This paper proposes a method for processing motor imagery-based Electroencephalography (EEG) signals to generate precise signals for Brain-Computer Interface (BCI) devices used in rehabilitation and physical treatments. BCI research is mainly used in neuroprosthetic applications to help improve disabilities. We analyze EEG data from seven healthy individuals using 59-channel caps. The signals are down-sampled to 100 Hz after pre-processing to remove artifacts and noise by using Filter Bank Common Spatial Patterns (FBCSP). EEG features are extracted using the Fisher Discriminant Ratio (FDR). A comprehensive comparison of classification methods is conducted, encompassing statistical techniques, machine learning algorithms, and neural network-based models. Specifically, Linear Discriminant Analysis (LDA) and K-Nearest Neighbors (KNN) are evaluated as statistical classifiers; Support Vector Machine (SVM) is used for the machine learning approach; and Radial Basis Function (RBF), Probabilistic Neural Network (PNN), and Extreme Learning Machine (ELM) are explored as neural network models. Model performance is validated using K-fold cross-validation and confusion matrix analysis. Among all evaluated classifiers, the ELM model—implemented as a single-layer neural network—demonstrates superior classification accuracy, suggesting its strong potential for real-time BCI applications in neurorehabilitation.

In this paper, the effect of input holes on the performance of leaky wave antennas(LWA) is investigated by presenting two designs based on the substrate integrated waveguide(SIW) structure. Both antennas are made of unit cells with transverse and longitudinal slots, the main difference being the placement of holes in the input ports of one of the designs. The simulation, fabrication and measurement processes for both LWAs confirm findings. The antenna equipped with the hole in the input port achieves a bandwidth of 6.7 GHz with a minimum return loss of +10 dB over this entire bandwidth, focusing at a frequency of 12.8 GHz and a reflection coefficient of -48.5 dB. The antenna is capable of beam scanning from -66° to +5° and from +20° to +73° with a cross-polarization level of more than -45 dB over the entire frequency scan range. The final antenna has a maximum gain of 16.7 dB in the 7.9–14.6 GHz frequency band with 52% of the normalized bandwidth relative to the center frequency. Overall, the findings emphasize that the inclusion of input holes in LWA designs significantly enhances the antenna radiation performance and highlight their potential for optimizing SIW-based antennas in various applications.