Amirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Analysis of a Simplified 13-Level Inverter using Reduced Switched Capacitor Technology227246592210.22060/eej.2025.24349.5690ENKandukuriSarithaAssistant Professor, Department of Electrical and Electronics Engineering, Godaveri Institute of Engineering and Technology(A), Rajahmundry0009-0003-5485-209XKuncheGowthamiAssistant Professor, Department of Electrical and Electronics Engineering, Godaveri Institute of Engineering and Technology(A), RajahmundryDasariKusumaUG Scholar, Department of Electrical and Electronics Engineering, Godaveri institute of Engineering and Technology(A), RajahmundryGrandhi SaiLaasiyaUG Scholar, Department of Electrical and Electronics Engineering, Godaveri institute of Engineering and Technology(A), RajahmundryPeddninti Srinivas RajeevDikshitUG Scholar, Department of Electrical and Electronics Engineering, Godaveri institute of Engineering and Technology(A), RajahmundryJournal Article20250704A novel Multi-level Inverter (MLI) with reduced Switched Capacitor (SC) is designed due to its aids of low component count and low-cost implementation. This paper introduces a novel SC-MLI to achieve increased number of output voltage levels with reduced component requirements. Henceforth, 13 level inverter is deployed using 14 switches, 3 capacitors and a single diode, in which voltage through capacitors is continued at required magnitude during the switching operation by utilizing Pulse Width Modulation (PWM) generator. By inducing 13 level reduced SC enables to attain improved voltage boosting with high voltage balancing ability thereby, providing better quality output to load. Thereby, the proposed approach concentrates on designing a reduced SC-MLI for achieving higher output voltage with desired output quality by utilizing very small number components, thereby reducing the size and cost of implementation. Furthermore, system is executed using MATLAB/Simulink and obtained result depicts that proposed system attained enhanced system performance with efficiency (98.50%) with increased amount of output voltage levels.https://eej.aut.ac.ir/article_5922_d76d8deea9c19cc9aaf2237d2bf2f785.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401High Gain Modified Reboost-Luo Converter with Optimized PI Controller for Effectual Integration of PV in Grid System247262594310.22060/eej.2026.24345.5687ENKosuriSravaniAssistant Professor, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, India0009-0009-1500-1851Challa LeelaKumariAssistant Professor, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaPuttaguntaManishaUG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaRayudu SowmyaDeepikaUG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaYadlaManojUG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaJournal Article20250703In recent years, Renewable Energy Sources (RES) have gained substantial importance in the global energy generation landscape, offering numerous environmental and economic benefits. This paper presents a highly efficient Photovoltaic (PV) grid-tied system utilizing a novel High-Gain Modified Re-Boost Luo (HG-MRBL) converter, designed to optimize energy generation. For attaining optimal energy production from solar panels, Maximum Power Point Tracking (MPPT) is employed using Jellyfish Search Algorithm, by an Adaptive Neuro-Fuzzy Inference System (JSA-ANFIS). An HG-MRBL converter processes the PV system's output to raise Direct Current (DC) voltage to the necessary levels for grid interaction. A three-phase Voltage Source Inverter (VSI) applied for converting this DC supply into AC. To minimize harmonics and guarantee seamless grid integration, the VSI is connected to an LC filter. Proportional Integral (PI) controller utilized for controlling injection of both active and reactive power into grid to manage flow of power. The system's efficiency is validated using MATLAB simulation, revealing the highest converter efficiency of 94.31%, which surpasses the performance of state-of-the-art approaches.https://eej.aut.ac.ir/article_5943_9a3f54913bf27e648d1759c18d007165.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Optimized Battery Charging System for Electric Vehicle with Proportional Integral Controlled High Frequency Resonant Bridgeless Power Factor Correction Converter263280597010.22060/eej.2026.24491.5713ENChalla LeelaKumariAssistant Professor, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry, India0009-0000-3161-5938Dondapati RaviKishoreProfessor, Department of Electrical and Electronics Engineering, Godavari Global University, Rajahmundry, IndiaKailashKumarUG Scholar, Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaNikhilKumarUG Scholar, Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaMdShahilUG Scholar, Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), Rajahmundry, IndiaJournal Article20250730The goal of research is to develop an optimized Electric Vehicle (EV) charging system with enhanced efficiency and cost-effectiveness by integrating a High Frequency Resonant Bridgeless (HFRB) Power Factor Correction (PFC) converter and a Proportional Integral (PI) controller. To attain the goals, the subsequent tasks are proficient: a novel Bridgeless PFC converter is developed to improve power factor correction and power quality; a PI controller is exploited to control output voltage with reduced implementation and maintenance costs and a Pulse Width Modulation (PWM) generator within a Hysteresis Current Controller (HCC) is deployed to enhance current protection, system stability, and fault tolerance. The system is validated using MATLAB/Simulink-based simulations. The significant outcomes are the accomplishment of a high charging efficiency of 97%, improved power quality, reduced heat generation, and enhanced system control operations. The significance of obtained results is that the proposed system ensures optimized EV battery charging with increased reliability, reduced implementation costs, and robust fault tolerance, contributing to the development of effective EV charging infrastructure.https://eej.aut.ac.ir/article_5970_857c41bc36ba2d1be4e16d321e3f15b7.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Classifying sensitive overhead power lines according to their impact on cost functions to enhance their protection through dandelion optimization281296593210.22060/eej.2025.24280.5669ENMohamedBEYProfessor, L2GEGI Laboratory, Applied sciences Faculty, IBN Khaldoun university of Tiaret, Algeria0009-0007-5751-962XAminaTamerProfessor, Higher School of Electrical and Energetic Engineering of Oran, AlgeriaJournal Article20250612This article examines the objective function of power generation, focusing on the impact of overhead line failures (line outages) on its value, which may either decrease or increase depending on the line's criticality. This study proposes the Dandelion Optimization algorithm to evaluate system performance and identify the most critical transmission lines that require careful maintenance to reduce high generation costs. A novel performance index is established to assess the impact of line failure and identify the lines that warrant the most attention. This research uses the IEEE 30-bus power network as a case study to validate the proposed concept, which demonstrates that overhead electric transmission lines do not hold equal significance within the power network.https://eej.aut.ac.ir/article_5932_3d191ef6e236bd1b9bdb9ff4743c47fe.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Renewable Energy-Fed EV Charging Station Utilizing Z-Source Quadratic Improved Boost Zeta Converter with Optimized RBFNN MPPT Control297318593110.22060/eej.2025.24346.5688ENRSathishResearch scholar, Department of Electrical and Electronics Engineering, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, 621112, India.0009-0002-3619-783XVSekarDean, School of Engineering and Technology, Dhanalakshmi Srinivasan University, Samayapuram, 621112, India.Journal Article20250703<span lang="EN-IN" style="font-size: 11.0pt; line-height: 200%;">PV-based EV charging stations offer economic advantages, including decreased operational costs, reduced grid dependency during peak hours, and enhanced consistency through the addition of local energy storage systems. This work presents a novel approach for integrating Renewable Energy Sources (RESs), specifically Photovoltaic (PV) systems, into Electric Vehicle (EV) charging stations. Proposed research utilizes a Z-source Quadratic Improved Boost Zeta (Z-SQ-IBZ) converter coupled with a Crayfish Optimization Algorithm based Radial Basis Function Neural Network (COA-RBFNN) Maximum Power Point Tracking (MPPT) algorithm for enhancing power extraction efficiency from PV arrays. Energy management and power balancing between PV energy generation and storage system are enabled through bidirectional DC-DC converter interfacing battery storage and DC link. Proposed PV-based EV charging system is implemented in MATLAB Simulink, and novel Z-SQ-IBZ converter attains enhanced efficiency of 96.63% with improved voltage and minimized voltage stress. The novel COA optimised RBFNN MPPT effectively tracks optimal power with improved tracking efficiency of 99.81%. MATLAB simulation results demonstrate that proposed topology effectively manages energy flow, optimises PV power utilisation, and constantly supports EV charging demands, making it appropriate for efficient EV charging station applications. </span>https://eej.aut.ac.ir/article_5931_aa1b6b26d690368d6f74a35a7daa0916.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Economic operation based sizing of hybrid Microgrid considering Battery Energy Storage System319336594610.22060/eej.2026.24659.5743ENV SSandeep Kumar ReddyDepartment of Electrical Engineering, Assistant Professor, ANITS, Visakhapatnam0000-0001-8875-5915JVijaya KumarDepartment of Electrical Engineering, Professor, ANITS, VisakhapatnamCH.V.N.RajaDepartment of Electrical Engineering, Associate Professor, ANITS, VisakhapatnamSeerapuVaralakshmiDepartment of Electrical Engineering, Assistant Professor, Dr. LBCE, VisakhapatnamJournal Article20250903The microgrid is described as a localized low-voltage power distribution system integrating DG units and ESS to supply electricity to some small or remote communities. In this respect, the ESS stores energy when demand is low and releases the stored energy during peak hours. Real-time power balancing remains a major issue for isolated micro-grids using intermittent renewable DG sources. Battery Energy Storage Systems (BESS) can solve the problem as they can offer reserve capacity to meet the load changes. However, battery degradation significantly affects the BESS lifetime performance because degradation depends upon the cumulative energy throughput which has units in terms of kilowatt-hours (kWh) or megawatt-hours (MWh). When there is degradation affecting capacity reduction, there is a direct impact on the energy delivered to the load, and therefore it must be considered in system optimization. To reduce the operation costs and to make the electricity prices affordable for the consumers, the degradation effects must be included while optimizing the microgrid operation. For this, more detailed simulation on an hourly basis of battery discharge profile needs to be performed so as to assess the degradation effects based on actual discharge patterns. Then degradation costs and life estimations are included in the optimization. It is observed that higher average kWh and actual MWh throughput parameters increase operation costs in general while lower the electricity cost for the end user. This study presents an optimization method for minimizing microgrid operating costs and customer electricity expenses over the 24-hour period under consideration, with explicit modeling of BESS degradation. Accelerated Particle Swarm Optimization (APSO), the Modified Jaya (M-JAYA) algorithm, and the Linear Programming Interior-Point (LP-IP) method are implemented to optimize parameters related to degradation. Comparative results showcase the ability of these algorithms with respect to BESS lifetime, degradation cost, system operating cost, and customer electricity cost.https://eej.aut.ac.ir/article_5946_926ffc0ca56636b9e73c565cf994ea5a.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401From Sun and Wind to Thermal Comfort: A Techno-Economic Optimization Framework for Renewable Electrification of Heating and Cooling in Iran337360599410.22060/eej.2026.24208.5662ENSeyed MohsenHashemiPower System Operation and Planning Research Group, Niroo Research Institute, Tehran, Iran0000-0002-5054-8766FarhadFallahiSmart Control Systems Research Department, Niroo Research Institute (NRI), Tehran, Iran0000-0002-5054-8766Journal Article20250601This study develops an optimization-based planning model for the deployment of renewable energy resources to meet electrified space heating and cooling demands. The model integrates building heat transfer characteristics, renewable energy potentials, and electricity transmission network constraints. Its objective is to minimize total investment and operational costs of generation, storage, and transmission, while capturing seasonal variability in supply and demand. The framework also evaluates the effects of finer temporal resolution and multiple operational periods through scenario-based simulations. Heating and cooling demands are estimated using degree-day metrics combined with building thermal transfer equations, assuming full electrification by renewable sources. Two strategies for heating electrification are investigated: assigning a predetermined share to each province, and optimizing the spatial distribution of electrification across provinces to meet overall targets. The model is applied to real-world data from Iran, which features diverse climates and substantial solar and wind potential. Results suggest widespread deployment of solar PV across most provinces, while wind development is concentrated in eastern regions such as Khorasan. Sensitivity analysis on storage system costs shows a balanced solar-wind combination when the storage prices is in the range $100-150/kWh. For the prices below or above this range respectively the solar or wind power plants dominate the generation mix. Transmission network expansion is most beneficial in provinces that serve as renewable hubs or major demand centers, though local supply is generally prioritized. Overall, findings indicate that the priority of heating electrification projects varies significantly among provinces, highlighting the importance of spatially differentiated planning strategies.https://eej.aut.ac.ir/article_5994_edb446b67d69adbfe9a21068982000c2.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401An Improved P300-based Brain-Computer Interface using Tensor Methods for Patients with Amyotrophic Lateral Sclerosis361382594410.22060/eej.2026.24519.5721ENMojhganSanjaraniFaculty of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranMohammadPooyanBiomedical Engineering Department, Engineering Faculty, Shahed University, Tehran, IranFarzadFatehiNeuromuscular Research Center, Shariati Hospital, Tehran University of Medical Sciences, Tehran, IranJournal Article20250808Amyotrophic Lateral Sclerosis (ALS) is a progressive neurological disorder with no fully effective treatment currently available. In this study, a novel tensor-based feature reduction method, Higher Order Spectral Regression Discriminant Analysis (HOSRDA), is proposed to enhance Brain-Computer Interface (BCI) performance in individuals with ALS. HOSRDA extends the principles of Spectral Regression Discriminant Analysis (SRDA) to handle multi-dimensional EEG data, effectively addressing the challenges of high-dimensionality and ill-conditioned scatter matrices in the analysis of P300 speller data. This method reduces the dimensionality of EEG signals while preserving class separability, enabling efficient classification using Linear Discriminant Analysis (LDA). Furthermore, HOSRDA leverages a regression framework to address the computationally expensive eigenvalue decomposition of scatter matrices, a challenge faced by traditional methods like HODA, significantly improving computational efficiency. Experiments conducted on EEG data from five ALS patients show that the HOSRDA-LDA model achieves an average character detection accuracy of 84.04%, demonstrating its potential for real-time BCI applications. Compared to traditional methods such as LDA without feature reduction and Support Vector Machine (SVM), HOSRDA outperforms in terms of classification accuracy and computational efficiency, with significantly reduced training times. The HOSRDA method converges in an average of 2.04 seconds over three repetitions, making it highly suitable for online BCI systems. These findings suggest that HOSRDA can improve the accessibility and usability of BCIs for ALS patients, with potential applications extending to broader clinical and real-world settings, without the need for time-consuming training sessions or considering factors like literacy.https://eej.aut.ac.ir/article_5944_90f4760fcc9b69c13da7368c5c2917f3.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Attention-Guided Dehazing: A New Architecture with Low-Level and Multi-Level Channel Attention383404596010.22060/eej.2026.24359.5691ENHosseinNooriAssistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, Iran0000-0001-5820-5613Mohammad HosseinGholizadehAssistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, IranGholamrezaMemarzadehAssistant Professor, Department of Electrical Engineering, Vali-e-Asr University of Rafsanjan, Rafsanjan, IranJournal Article20250706Single-image dehazing has become increasingly vital in recent years due to its foundational role in enhancing high-level vision tasks such as object detection, remote sensing, and autonomous driving. While numerous deep learning-based approaches have been proposed, many still fall short in fully preserving image details and capturing complex haze patterns. In this paper, a novel end-to-end architecture for single-image dehazing is presented that addresses key limitations of existing methods. The proposed network integrates two innovative modules: the Low-Level Feature Attention (LLFA) module, which emphasizes the retention of fine-grained details often lost in deeper layers, and the Multi-Level Channel Attention (MLCA) module, which dynamically fuses low- and high-level features to improve the network’s representational capacity. By leveraging these complementary modules across multiple resolution scales, the architecture achieves more effective feature extraction and superior haze removal. Extensive experiments on both synthetic and real-world datasets demonstrate that our method consistently outperforms state-of-the-art algorithms in both qualitative visual clarity and quantitative evaluation metrics. The results confirm the robustness and efficiency of the proposed approach in producing clean, detail-rich dehazed images.https://eej.aut.ac.ir/article_5960_233f1dd0f3f537bcb7a338ea74d63483.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401A Centralized Machine Learning Intrusion Detection System Against Distributed Denial-of-Service Attacks in Wireless Sensor Networks405420599810.22060/eej.2026.24224.5664ENMinaMalekzadehAssociate Professor, Electrical and Computer Engineering Faculty, Hakim Sabzevari University, Sabzevar, Iran0000-0002-0733-3970AlirezaHosseiniElectrical and Computer Engineering Faculty, Hakim Sabzevari University, Sabzevar, IranJournal Article20250608Wireless sensor networks (WSNs) are vulnerable to distributed denial-of-service (DDoS) attacks, which can severely degrade overall performance and compromise system availability and reliability. To effectively protect against such attacks, this work introduces a centralized intrusion detection system (IDS) framework utilizing machine learning (ML) techniques. The IDS integrates six different ML models to accurately classify malicious traffic and distinguish it from legitimate network traffic. However, developing and validating a robust ML-based defense solution requires a comprehensive understanding of the attack’s behavior and impact. Therefore, we initially simulate a baseline WSN architecture and conduct different DDoS attacks, focusing specifically on two critical architectural layers: Cluster Heads and the Base Station. To identify vulnerabilities introduced by DDoS traffic saturation and resource exhaustion, the severity of the attacks is further quantified through network-level metrics. This empirical analysis provides four labeled datasets necessary to train the ML models in the IDS framework across multiple operational phases, including the baseline phase before the attacks, the active attack phase during DDoS attacks, and the recovery phase after the attacks. Experimental results demonstrate that the IDS achieves high detection performance and significantly reduces the adverse effects of the attacks. Furthermore, based on the findings, the IDS facilitates rapid network recovery, restoring performance to levels close to normal operations.https://eej.aut.ac.ir/article_5998_b98a3773ecf715751d3cf0fb6dcba424.pdfAmirkabir University of TechnologyAUT Journal of Electrical Engineering2588-291058220260401Electromagnetic Shielding Effectiveness of an Arbitrary-Shape Multilayered Anisotropic Composite Enclosure421438597310.22060/eej.2026.25436.5932ENMonaKalantariDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran0009-0001-6502-6127Seyed Hossein HesamedinSadeghiDepartment of Electrical Engineering, Amirkabir University of Technology, Tehran, Iran0000-0003-4266-5978MitraKalantariDepartment of Computer Engineering, Islamic Azad University, Arak, IranJournal Article20260124This paper uses an efficient surface integral equation-based MoM (SIE-MoM) scheme to study the electromagnetic shielding properties of arbitrary-shape multilayered anisotropic composite enclosures. In this scheme, each anisotropic composite layer is modeled by an equivalent homogeneous anisotropic medium with tensorial characteristics. The method treats each layer, separately, using the surface equivalence theorem. After applying the suitable boundary conditions, the SIEs solved by the Galerkin’s-MoM. This computation includes, the expansion of equivalent surface current densities by proper basis functions, the rotation of dyadic Green’s functions of the infinite space filled with the equivalent anisotropic material used for each layer, and finally the inner product of both sides of integral equations to express the SIEs in the form of matrix equations solved through an efficient inversion process of a sparse block-tridiagonal impedance matrix. The rotation angle of dyadic Green’s functions is the angular deviation between the specified global coordinate system and the local principal one for anisotropic materials, which is determined by diagonalizing the permittivity tensor of the equivalent anisotropic layer defined in the global coordinate system. The validity and efficiency of the presented method are demonstrated for a three-layer irregular-shaped anisotropic composite enclosure by comparing the obtained results with those obtained using FE-solver of the commercial CST studio. Finally, a set of sensitivity analysis is done to examine the various parameters that affect the shielding properties of a two-layer cubic anisotropic composite enclosure.https://eej.aut.ac.ir/article_5973_6de59d960d3bb8a6346c058930f3cd28.pdf