Shahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250418Optimization of the Structure of an Induction Motor with Magnetic Bearings48248810566910.48308/ijrtei.2025.237898.1068ENHamedAzadrouDepartment of Electrical Engineering, Salmas Branch, Islamic Azad University, Salmas, Iran.RezaGhanizadehDepartment of Electrical Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran.Journal Article20241209Although 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.https://ijrtei.sbu.ac.ir/article_105669_b9a2e445723f66f5230cb666debc01fd.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250506A Transfer Learning-based Convolutional Neural Network for Event Classification with Small Databases48949810571710.48308/ijrtei.2025.238734.1075ENMohammadAryanfarDepartment of Electrical and Computer Engineering, Semnan University, Semnan, IranMostafaJazaeriDepartment of Electrical and Computer Engineering, Semnan University, Semnan, IranJournal Article20250210Power 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.https://ijrtei.sbu.ac.ir/article_105717_e659d991b9c6c76a65a03ea66c917b5f.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250528Fixed-Time Containment Tracking Control of Fractional-Order Multi-Vehicle Systems with Multiple Leaders: SMC approach49950810575910.48308/ijrtei.2025.238781.1076ENHosseinZamaniThe School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranVahidJohari MajdThe School of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, IranKhosroKhandaniThe Department of Electrical Engineering, Faculty of Engineering, Arak University, Arak, IranJournal Article20250215This 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.https://ijrtei.sbu.ac.ir/article_105759_35ef4e0ec51aab98216590a8d65b04f5.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250531A Transfer Learning Method for Intelligent Load Shedding using Graph Convolutional Network considering Unknown Faults50951610532810.48308/ijrtei.2024.237789.1066ENNazaninPourmoradiFaculty of Electrical Engineering, Shahid Beheshti University, Tehran, IranMohammad TaghiAmeliFaculty of Electrical Engineering, Shahid Beheshti University, Tehran, IranJournal Article20241201Event-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.https://ijrtei.sbu.ac.ir/article_105328_8370a780a8c4918463a160a1ef1791dd.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250903Classification Algorithms of EEG Signals based on Motor Imagery51752510611010.48308/ijrtei.2025.239583.1081ENAmirhosseinKonjkavDepartment of Electrical Engineering, Shahid Beheshti University, Tehran, IranFatemehJahangiriDepartment of Electrical Engineering, Shahid Beheshti University, Tehran, IranAtenaSajedinDepartment of Electrical Engineering, Amirkabir University of Technology, Hafez Ave., 15875-4413 Tehran, IranJournal Article20250420This 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.https://ijrtei.sbu.ac.ir/article_106110_1d538b55e34e97ac7ca9c9fd12e9aa93.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250908Improving the Performance of SIW-based Leaky Wave Antenna using the Creation of an Entrance Hole52653410611110.48308/ijrtei.2025.239587.1082ENHeshmatNooriElectrical and Engineering Department, Razi university, Kermanshah, IranSeyed Vahab Al-DinMakkiElectrical and Engineering Department, Razi university, Kermanshah, IranJournal Article20250420In 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.https://ijrtei.sbu.ac.ir/article_106111_6039eea4470655de71a3a80a67f05c9b.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250906A novel method to control sub synchronous oscillations of DFIG wind turbine in a power grid53554410612310.48308/ijrtei.2025.239762.1083ENAsaadShemshadiElectrical engineering department, Arak university of technology, Arak, IranHamidYousef KhaniElectrical engineering department, Arak university of technology, Arak, IranJournal Article20250502Wind power systems provide significant benefits, including ease of installation, high efficiency, and scalability. However, one of the major challenges in these systems is the occurrence of sub-synchronous oscillations (SSOs), which can severely compromise power grid stability. This study examines recent advancements in SSO mitigation strategies for Doubly-Fed Induction Generator (DFIG)-based wind energy systems, the most widely adopted technology in modern wind turbines. Various control approaches, such as intelligent controllers, adaptive control mechanisms, and predictive algorithms, are reviewed. Simulation experiments carried out using MATLAB/Simulink software confirm the effectiveness of the proposed Direct Current Vector (DCV) control method in attenuating SSOs and improving overall system performance. In addition to identifying current challenges and research gaps, this work emphasizes the critical importance of ongoing research to develop robust SSO mitigation techniques for grid-connected wind power systems. The results demonstrate that incorporating advanced technologies and sophisticated control strategies plays a vital role in reducing sub-synchronous oscillations and enhancing the operational performance of wind energy systems.https://ijrtei.sbu.ac.ir/article_106123_aed89669e90af7184f19eec7f53c6ddf.pdfShahid Beheshti UniversityInternational Journal of Research and Technology in Electrical Industry2821-01904120250906Discontinuous Pulse Width Modulation In Flux Angle Control Of Induction Motors54555210612410.48308/ijrtei.2025.240319.1092ENMohammad RezaMohebbiDepartment of Electrical Engineering, Iran University of Science and Technology, Tehran, IranDavoodArab KhaburiDepartment of Electrical Engineering, Iran University of Science and Technology, Tehran, IranJournal Article20250606In this study, flux angle control (FAC) with discontinuous modulation is presented for induction motor drives with the aim of optimizing motor and inverter losses under light load conditions. The proposed approach implements the discontinuous modulation technique in flux angle control for the first time and emphasizes proper drive performance while reducing its losses. The discontinuous pulse width modulation (DPWM) method optimizes switching by strategically injecting zero sequence voltages, reducing inverter losses and thermal stress on power electronics components, and improving system reliability. The proposed method is simulated in MATLAB software and compared with PWM and SVM techniques in drive efficiency and loss reduction. Comprehensive simulations confirm the superior performance of this combined approach and show the optimal balance between switching losses, harmonic quality, and current and torque ripple compared to PWM and SVM methods. The simplicity of implementation of the DPWM method, along with its ability to maintain acceptable output voltage quality and reduce overall system losses, while taking advantage of the advantages of PWM and SVM methods, emphasizes its suitability for practical industrial applications. This work contributes to the continued development of high-efficiency motor drive systems by highlighting the advantages of integrating flux angle control with discontinuous modulation strategies.https://ijrtei.sbu.ac.ir/article_106124_58b4ed16f54e59437d706179317f73a0.pdf