IRANIAN JOURNAL OF ELECTRICAL AND ELECTRONIC ENGINEERING

An examination of intelligent robotic wheelchairs enhancing mobility and autonomy for people with disabilities

Sudipta Chatterjee

This paper offers a comprehensive examination of smart robotic wheelchairs and their role in enhancing the mobility and independence of individuals with disabilities. Conventional wheelchairs often restrict users, leading to limited movement and accessibility. The emergence of smart robotic wheelchairs presents a promising solution to these issues. The study provides an overview of wheelchair technology, highlights challenges faced by individuals with disabilities, and assesses the benefits and drawbacks of smart robotic wheelchairs through a review of previous research. It delves into the features and functionalities of these wheelchairs, such as navigation and obstacle avoidance, autonomous and semi-autonomous modes, and customizable control options. Additionally, it analyses user experience, performance evaluation, and the impact on mobility and independence. The paper concludes by outlining future research directions and recommendations to further empower individuals with disabilities and enhance their quality of life.

Optimal integration of Renewable based Distributed Generators via Hybrid GA-PSO approach

Rupika Gandotra

The rising demand for electricity has led to the installation of renewable-based distributed generators in a power system network to meet the increasing load. The eco-friendly nature of these DGs is another compelling reason to incorporate them in a power system network but their installation process requires careful consideration such as determining the optimal quantity and location because these factors have a significant impact on various constraints and parameters of the power system network. The main objective of this paper is to determine the optimal siting and sizing of Type-1 and Type-2 DGs in a power system network such that network has minimum real and reactive power losses in the transmission lines, also fuel cost of convectional generators is reduced and voltage profile is improved. For this purpose, hybrid GA-PSO approach is developed and implemented on case 33 bus system and results were compared under different loading conditions such as 100%, 150%, 200% to show which type of DG is most effective. Further, the evaluated results have been compared with other algorithms including OCDE, WOA, SFSA, TGA and EJSA in order to ensure the validity of the suggested approach. The numerical results validate the performance of this proposed technique for DG unit placement.

Design and Validation of an Enhanced Earthing System for Low Ground Resistance

Akinola Oladeji

Grounding systems are critical for ensuring electrical safety, minimizing fault currents, and enhancing infrastructure reliability, particularly in regions with high-resistivity soil. This study presents the design, simulation, and field implementation of a low-resistance earthing system integrating bentonite, charcoal, and sodium chloride to reduce soil resistivity. Using ETAP software, the performance of the Finite Element Method (FEM) and IEEE Std. 80-2013 grounding models are compared under a 30kA fault current scenario. FEM simulations predict a ground resistance of 0.028 Ω and a Ground Potential Rise (GPR) of 627.4 V, while the IEEE method yields 0.269 Ω and 5996.5 V, respectively. Field measurements using a UNI-T Ground Tester validate the FEM results, recording an actual ground resistance of 0.023 Ω, well below the IEEE-recommended 1 Ω threshold, surpassing this conventional benchmark by 98%. A comparative analysis of recent studies highlights the superiority of the composite material approach. The FEM model’s accuracy in capturing soil stratification and material effects is validated, while safety metrics (step/touch voltages) adhere to the IEEE standard. This work bridges theoretical innovation and practical implementation, offering a replicable framework for resilient grounding systems in challenging environments.

Design and Analysis of Double Channel Organic Field Effect Transistor with Bottom Gate

Pawan Kumar Mishra

In recent years, organic field effect transistors, also known as OFETs, have witnessed a substantial demand, mainly due to their expanding applications in the display and sensor industries, owing to simple fabrication techniques and cost-effective raw materials. But due to limited charge mobility, its applications are mostly focused on non-computing applications. Since OFETs are fundamental elements employed in an electronic circuit, the performance of the whole electronic device is correlated with its performance. The development of high performance OFET is particularly beneficial for establishing non-silicon-based chip manufacturing in developing countries worldwide. In an attempt to develop a high performance OTFT, double channel bottom gate organic field effect transistor (DCBG OFET) is proposed in this research article. DCBG OFET or OTFT is a single gate device comparable to a bottom gate bottom contact (BGBC) OTFT in structure, but it generates 4 times higher drain current in its conduction channel with identical material composition and structural dimensions compared to its analogous. A comprehensive comparative study has been presented here investigating performance parameters like transconductance, threshold voltage, subthreshold slope, linear and saturation mobility, etc., to determine the functional superiority of the DCBG OFET over other single gate OTFT structures like BGBC, top gate bottom contact (TGBC), and bottom gate top contact (BGTC) OTFTs. It has been observed that DCBG OTFT exhibits a four-fold improvement in the drain current with respect to conventional single gate OTFTs, and staggering 300% enhancements in parameters like transconductance, linear and saturation mobility are also observed in DCBG OFET over other OTFT architectures with matching material configuration and structural dimensions, operational under the identical voltage conditions.

Enhancement of Sparse Spasmodic Sampling using Novel Machine Learning Fusion Technique

Deepak Karia

This research explores the demands of compressive sensing (CS) and Machine learning (ML) in biomedical signal processing. The sparse spasmodic sampling (SSS) technique has gained significant attention in compressive sensing. The SSS samples the signal irregularly and spasmodically. Combining machine learning (ML) with Sparse Spasmodic Sampling (SSS) enhances accuracy and improves anomaly detection in biomedical signals. We propose a machine learning-based novel fusion technique that enhances sparse spasmodic sampling (ML-SSS). Mathematical analysis, extensive simulations, and experimental results show notable improvements in reconstruction accuracy and precision. The reconstruction using the proposed model achieves a high signal-to-noise ratio (SNR) of up to 42 dB at a high compression factor of 10%. The achieved accuracy is approximately 95%, and the precision is about 93.3% when detecting abnormalities. This approach paves the way for advanced applications in signal processing and medical imaging, where efficient data acquisition and processing are critical. The proposed framework offers a promising direction for bridging the gap between compressive sensing and intelligent algorithms in anomaly detection.

Numerical Investigation on Forming Conditions Impact in Electromagnetic Tube Expansion Performance

Ilhem Boutana

Electromagnetic Tube Expansion (EMTE) is a high-velocity forming process that utilizes transient magnetic fields to plastically deform tubular workpieces without physical contact. The process requires the generation of large currents via a capacitor bank, producing intense magnetic pressures to achieve deformation. While EMTE offers significant advantages in precision and efficiency, a comprehensive understanding of the interplay between key working conditions and deformation mechanisms remains crucial for optimizing its performance. This paper presents a numerical investigation into the effects of critical working conditions on the electromagnetic tube expansion process. Using a coupled finite element model, the transient magnetic field and resultant tube deformation are analyzed under varying conditions. The results provide insights into the relationship between process parameters and deformation outcomes, highlighting the potential for optimizing EMTE systems for enhanced efficiency and uniformity. This study contributes to advancing the theoretical and practical understanding of EMTE, by offering guidance for the design of more effective forming strategies and equipment.

A Deep Learning Based Code Loop Discriminator for GPS Spoofing Mitigation

M. R. Mosavi

A vital part of people's daily life is the position, navigation, and time service provided by the Global Positioning System (GPS), which is always accessible globally. Consequently, the security of the GPS receivers is crucial. Occasionally, intentional and unintentional interferences cause GPS location issues. Spoofing attack is the most severe interference to the GPS receivers, which results in positional mistakes. This paper's goal is to defend against the carry-off spoofing attacks. In a carry-off spoofing attempt, the spoofer transmits signals whose code phase and carrier frequency parameters are strikingly close to the actual signal in order to change the correlation values generated in the tracking stage. Discriminator output values alter as correlation values change. As a result, the Pseudo Random Noise (PRN) code generator unit creates a local replica, which forces the tracking loop to follow the fake signal instead of the real one. It is proposed in this paper that when spoofing attacks occur, discriminator output values be generated independently of correlation values. Specifically, when a spoofing signal is detected, the conventional discriminator is replaced by a Non-linear Autoregressive Exogenous Neural Network (NARX NN)-based predictor. This strategy protects the tracking loop from the effects of the spoofing signal. The efficiency of the provided strategy was evaluated using three spoofing data sets. The results of the suggested mitigation method, based on NARAX NN, show that it mitigates spoofing attacks by an average of 95.82%.

Deep Learning Based Graph Convolutional Network Using Hand Skeletal Points For Vietnamese Sign Language Classification

Manh-Hung Ha

This paper develops a robust and efficient method for the classification of Vietnamese Sign Language gestures. The study focuses on leveraging deep learning techniques, specifically a Graph Convolutional Network (GCN), to analyze hand skeletal points for gesture recognition. The Vietnamese Sign Language custom dataset (ViSL) of 33 characters and numbers, conducting experiments to validate the model's performance, and comparing it with existing architectures. The proposed approach integrates multiple streams of GCN, based on the lightweight MobileNet architecture. The custom dataset is preprocessed to extract key skeletal points using Mediapipe, forming the input for the multiple GCN. Experiments were conducted to evaluate the proposed model's accuracy, comparing its performance with traditional architectures such as VGG and ViT. The experimental results highlight the proposed model superior performance, achieving an accuracy of 99.94% test on the custom ViSL dataset, reach accuracy of 0.993% and 0.994% on American Sign Language (ASL) and ASL MINST dataset, respectivly. The multi-stream GCN approach significantly outperformed traditional architectures in terms of both accuracy and computational efficiency. This study demonstrates the effectiveness of using multi-stream GCNs based on MobileNet for ViSL recognition, showcasing their potential for real-world applications.

Torque Ripple Mitigation and Fault-Tolerant Operation of Modular Twelve-Phase PMSM Drive using Model-Free Predictive Control

Abolfazl Halvaei Niasar

In electric propulsion systems for high-power applications, multi-phase Permanent Magnet Synchronous Motors (PMSMs) are highly advantageous due to their fast dynamic response and high reliability. This study investigates a twelve-phase PMSM with double stator windings, where each winding is powered by a single-phase H-bridge inverter. The control of both H-bridge inverters for each phase is managed by a dedicated microcontroller. Given the independence of the control systems (microcontrollers) and the absence of data exchange between them, the modeling is conducted in the 12-phase stationary reference frame. To address non-sinusoidal back-EMF phase voltages and mitigate torque ripple, a harmonic current injection method is independently applied to each phase. A model-free predictive current and speed controller (MFPCSC), based on an ultra-local model, is employed, replacing conventional PI or hysteresis current controllers. Additionally, extended state observers (ESOs) are designed to estimate uncertainties and parameter mismatches. Under fault conditions, a fault-tolerant control strategy is implemented, where the current angle of healthy windings is adjusted to suppress the second harmonic in the remaining healthy windings, thereby reducing torque ripple. The effectiveness of the proposed control methods is validated through simulations, both under normal operating conditions and various fault scenarios.

Mitigating the Effects of Rotary Transformer Leakage Flux on Brushless Synchro Accuracy

Farid Tootoonchian

Synchros are electromagnetic sensors utilized to determine the angular position of a rotating shaft. This paper examines the impact of leakage flux from the Rotary Transformer (RT) on the induced voltages and the position detection accuracy of the Wound-Rotor (WR) synchro. Various methods are proposed to mitigate the negative effects of leakage flux from the RT. The leakage flux paths, which couple with the signal winding, are identified. Based on this analysis, the optimal distance between the sensor and the RT is calculated to minimize the adverse effects of leakage flux on the synchro's accuracy. Additionally, the RT structure is modified to reduce the leakage flux. Another effective approach involves the use of Electromagnetic Interference (EMI) shielding. In this context, a shield frame is designed for the RT, and the impact of different shield materials on reducing leakage flux is investigated. The results show that a copper-based shield significantly reduces the adverse effects of leakage flux and improves the sensor’s accuracy. To evaluate the effectiveness of the proposed methods, they are assessed through 3-D Time-Stepping Finite Element Analysis (3-D TSFEA) and experimental measurements on a prototype sensor. The experimental results show close agreement with the 3-D TSFEA, confirming the accuracy of the findings.

Design and Electromagnetic Analysis of a Novel Axial-Field Flux-Switching Permanent Magnet Machine Achieving Improved Torque Characteristics

Hossein Torkaman

This paper introduces a unique rotor pole configuration for an Axial-Field Flux-Switching Permanent Magnet (AFFSPM) machine, focused on minimizing cogging torque (CT), reducing torque ripple (TR), and improving average torque (AT). This innovative design is based on the standard rotor configuration of the AFFSPM machine, with a Reversed Radial Pole (RRP) placement that this new topology will be recognized as RRPAFFSPM. To thoroughly evaluate the proposed design's effectiveness, sensitivity analysis will be conducted to determine the significance of geometric parameters and identify the best topology in comparison studies. Extensive 3D finite element analysis (FEA) confirms the design's effectiveness, demonstrating substantial reductions in CT and TR, along with an increase in AT. These results suggest that the desired rotor pole configuration is a promising solution for high-performance electric machines in demanding different applications.

An Optimized Magnetic Structure to Enhance Misalignment Tolerance in Wireless Power Transfer Systems for Electric Vehicle Charging

Aghil Ghaheri

In the rapidly advancing domain of wireless power transfer systems, particularly for electric vehicle charging, the design of the magnetic coupler plays a crucial role in determining both system efficiency and practical implementation. Variations in coupler system designs lead to differences in self-inductance, mutual inductance, and AC resistance, directly impacting the energy transfer efficiency and power delivery capability of the system. This paper proposes a novel coil design for wireless power transfer systems, incorporating Double-DZ (DDZ) and Quadrature (Q) coils to improve lateral and yaw misalignment tolerance. The proposed design integrates the advantageous features of three structures—SDDP, DDQP and TTP—to introduce a novel configuration, DDZ-DDQZ, which enhances system stability and performance. By increasing misalignment tolerance, this method substantially enhances the robustness and real-world feasibility of wireless power transfer for electric vehicle charging.

Improved Multi-Conductor Transmission Line Modeling of Transformer to Study Frequency Response

Mohammad Hamed Samimi

This paper introduces an improved multi-conductor transmission line (MTL) model for transformers' high-frequency transient and frequency response analysis, overcoming limitations in traditional models that fail to capture complex electromagnetic interactions during high-frequency events, such as lightning strikes and switching operations. The model accurately reflects real-world transformer behaviors under transient conditions by integrating particle swarm optimization (PSO) for efficient parameter estimation and incorporating frequency-dependent losses. The combined use of PSCAD and Python minimizes computational overhead, enabling high-fidelity simulations closely aligned with experimental transformer data. Validation against real transformer measurements demonstrates the model’s reliability in capturing high-frequency responses, essential for transformer diagnostics. This novel approach offers a practical tool for studying transformer frequency response analysis, which is an important tool in transformer diagnosis.

Surrogate-Based Multiphysics Design Optimization of a Wound Rotor Synchronous Generator with Enhanced Damping

Farshid Mahmouditabar

In recent years, due to the increase in electricity generation, the need for optimized Wound Rotor Synchronous Generators (WRSGs) has been felt more than ever. One of the important characteristics of a generator in a power system is its voltage harmonics. In addition to this, the amount of generated power and efficiency are also important. The goal of this research is multi-objective design using dampers, with improved number and shape. WRSGs have been selected as a case study. With the help of surrogate modeling and the PSO algorithm, which are more efficient and accurate than classical methods, the final design has been presented. In the end, the comparison of the initial and final designs shows the realization of all goals. Also, economic issues in terms of the selection of damper material have been investigated.