Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Design and Implementation of an Improved Dynamic Response Flying Capacitor Boost Converter for Smart Grid Systems Using a Model Predictive Controller 441 470 5997 10.22060/eej.2026.24986.5808 EN Kudiyarasan Swamynathan 1PhD., DGM (Electrical), Renewable Energy Power Plants, Neyveli Lignite Corporation India Limited (NLCIL), Neyveli, Tamilnadu, India. 0000-0002-7449-2234 S Kamatchi PhD., Professor, Department of Electronics and Communication Engineering, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Saveetha University Chennai, India. 0000-0001-8781-5517 Journal Article 2025 10 30 In general, symmetrical and asymmetrical capacitor-clamped boost converters and direct current capacitor voltage, unbalancing specially with lower output current Total Harmonic Distortion are frequent problems for inverters. In order to improve voltage quality, a boost converter with a flying capacitor and grid tie inverter is suggested in this study. It manages the direct current link voltage asymmetrically. Additionally, the boost converter with flying capacitor grid tie inverter system's output voltage dynamic responses are enhanced and simulated using MATLAB Simulink which in turn benchmarked using a scaled-down hardware module. Proportional Integral and Model Predictive Controller control strategies are suggested and implemented in the built hardware. The suggested system's voltage, current, and dynamic performance are examined. The results show that a 360 Watt output power can be delivered by the suggested combination of the described converter system. Additionally, grid-connected power converters and flying capacitor boost converters have lower current harmonics and better voltage regulation direct/alternating current converters, demonstrating the developed system's great suitability for power usage in home photovoltaic systems. https://eej.aut.ac.ir/article_5997_077fd57e57aab32087b0466fe6ebcca8.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Development and Control of High-Gain Triple Winding Max Gain BOOST Converter with Intelligent Walrus-RBFFIS MPPT for Photovoltaic Applications 471 488 6018 10.22060/eej.2026.24306.5679 EN J Viswanatha Rao Department of Electrical and Electronics Engineering, VNR Vignana Jyothi Institute of Engineering & Technology, Hyderabad, India 0000-0002-8901-8789 R Sundar Associate Professor, Department of Marine Engineering, AMET Deemed to be University, India G. S. Satheesh Kumar Associate Professor, Department of Electrical and Electronics Engineering, Erode Sengunthar Engineering College, Perundurai – 638057, India G. W. Martin Professor, Department of Electrical and Electronics Engineering, Marthandam College of Engineering and Technology, Tamilnadu-629177, India Journal Article 2025 06 23 Currently, the combination of Renewable Energy Sources (RES), particularly Photovoltaic (PV) systems, into power networks has grown in importance for sustainable energy generation. Therefore, this research develops the control approach for a high gain Triple Winding Max Gain Boost (TWMGB) converter incorporated with a Maximum Power Point Tracking (MPPT) controller for PV systems. The developed converter exploits a triple winding inductor structure to attain an improved voltage gain, making it appropriate for low voltage PV system needs effective step-up capability. An innovative MPPT control approach based on Walrus Optimization Algorithm (WOA) tuned Radial Basis Function Fuzzy Inference System (RBFFIS) is utilized to extract the upmost power from the PV array in dynamic ecological conditons. It assures fast convergence to the globalMPP and enahnces tracking accuracy even in partial shading scenarios. Moreover, the coordinated interaction among the TWMGB converter and adaptive control approach assures better performance interms of diminshed voltage stress and ripple. The performance of a system is applied via MATLAB/Simulink tool, demonstrating its adaptability and robustness with converter efficacy of 97.61%. The developed system offers a consistent and scalable solution for advanced PV based power systems, contributing to sustainable energy conversion and utilization. https://eej.aut.ac.ir/article_6018_8d2a5f7d4afa5d0530789d3066945330.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 NGOA Assisted Neural Network MPPT for Grid-Connected PV System with Soft-Clamp X-Gain Boost Converter 489 508 6029 10.22060/eej.2026.25230.5872 EN Viswaprakash Babu Assistant Professor, Department of Electrical and Electronics Engineering, Kaveri University, Gouraram, Siddipet, Telangana, India, Pin- 502279 0009-0008-6312-3995 T Sridevi Assistant Professor, Department of Electrical and Electronics Engineering, Peri Institute of Technology, Chennai, 600048, India. M. J. Murali Assistant Professor, Department of Electrical and Electronics Engineering, Bharath Institute of Science and Technology, Bharath Institute of Higher Education and Research, Chennai 600 073, India. J. Gnanavel Assistant Professor, Department of Electrical and Electronics Engineering, Achariya College of Engineering Technology, Pondicherry. D. Dinesh Assistant Professor, Department of Mechatronics Engineering, Chennai Institute of Technology, Chennai 600069 M Balasubramanian Assistant Professor, Department of Electrical and Electronics Engineering, Government College of Engineering, Tirunelveli 627007, Tamilnadu, India. Journal Article 2025 12 05 This work presents an intelligent Photovoltaic (PV) grid integration system employing a Northern Goshawk Optimization Algorithm (NGOA)-based Radial Basis Function Neural Network (RBFNN) for Maximum Power Point Tracking (MPPT) and Soft Clamp X-Gain Boost (SC-XGB) converter for enhanced voltage regulation. The proposed system aims to optimize energy harvesting from PV source and ensure stable power delivery to a three-phase grid. The RBFNN is trained offline using comprehensive PV datasets and directly predicts the optimal duty cycle from measurable PV inputs during real-time operation, eliminating the need for auxiliary MPP pre-estimation algorithms, while NGOA enhances RBFNN’s learning capability by fine-tuning its weights and biases for rapid and accurate MPPT performance even under varying irradiance and temperature conditions. PV output is connected to SC-XGB, which efficiently raises the Direct Current (DC) voltage and is thus controlled by Pulse Width Modulation (PWM) signals which are generated according to MPPT output. Regulated DC output is then transformed into a three-phase Alternating Current (AC) by a Voltage Source Inverter (VSI), the output of which is taken through an LC filter to reduce harmonics before the power is fed into grid. Simulation is done in MATLAB showing the capacity of NGOA-RBFNN to track Maximum Power Point (MPP) at very high speed and accuracy. The system achieves superior voltage regulation of 95.24% efficiency, reduced Total Harmonic Distortion (THD) and enhanced dynamic performance over traditional MPPT control methods. https://eej.aut.ac.ir/article_6029_7f3ad9c65beb20ccbd34a05041b4420b.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Advanced Power Quality Improvement Using Re-Lift Sepic Converter and DSTATCOM with Neural Network Control 509 534 6019 10.22060/eej.2026.25286.5894 EN S Ramachandran Assistant Professor, Department of Electrical and Electronics Engineering, Paavai Engineering College, Namakkal, Tamil Nadu, India 0009-0006-5527-8693 K Sakthidhasan Assistant Professor, Department of Electrical and Electronics Engineering, Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College, Chennai-600062, Tamil Nadu, India M Pandikumar Associate Professor, Department of Electrical Power and Energy Conversion, Saveetha School of Engineering, SIMATS, Chennai-602105, Tamil Nadu, India L Anbarasu Associate Professor, Department of Electrical and Electronics Engineering, Erode Sengunthar Engineering College Perundurai, Erode-638057, Tamil Nadu, India Journal Article 2025 12 18 At present power system faces certain Power Quality (PQ) issues, due to large amount of power usage, fluctuations and other uncertainties in non-linear loads. Thus, Distribution Static Compensator (DSTATCOM) is deployed for mitigating PQ problems. A three-phase AC source system, supplying a non-linear load using parallel-Voltage Source Inverter (VSI) based DSTATCOM at Point of Common Coupling (PCC) is deployed to DC-Link which functions based on current sharing principle. A novel Re-lift Single Ended Primary Inductor Converter (SEPIC) converter is integrated with Photovoltaic (PV) to boost PV power generation, assuring consistent and sustainable power supply to DC-Link capacitor of DSTATCOM is correctly charged. To further enhance the system performance, D-Q theory/Neural Network-based Synchronous Reference Frame (SRF) theory is utilized for generating reference current for DSTATCOM. These control topologies enable accurate compensation of reactive power and harmonic currents in real-time, assuring improved grid voltage stability and rectifying distortions. Proposed system is executed using MATLAB simulation and acquired outcomes validate improved system functioning with better PQ mitigation at PCC under varying load conditions. Thus, demonstrating the impacts of integrating renewable energy with advanced control approaches. https://eej.aut.ac.ir/article_6019_5218f316b3f85b751c613a06aa18010d.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Enhanced Static Voltage Stability in Distribution Networks Through Coordinated DG and STATCOM Placement Using a Hybrid GWO-PSO Algorithm 535 552 6008 10.22060/eej.2026.24368.5693 EN Molla Addisu Mossie Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia 0009-0003-1068-1786 Tefera Terefe Yetayew Department of Electrical Power and Control Engineering, Adama Science and Technology University, Adama, P.O. Box 1888, Ethiopia Girmaw Teshager Bitew Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia Teketay Mulu Beza Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia Mezigebu Getinet Yenealem Faculty of Electrical and Computer Engineering, Bahir Dar Institute of Technology, Bahir Dar University, Bahir Dar, P.O. Box 26, Ethiopia Journal Article 2025 07 08 This paper addresses voltage stability challenges in distribution networks through a coordinated approach using Distributed Generation (DG) and Static Synchronous Compensator (STATCOM) placement. A novel hybrid Grey Wolf Optimization-Particle Swarm Optimization (GWO-PSO) algorithm is proposed to optimize the placement and sizing of these components with the objective of enhancing static voltage stability. The Fast Voltage Stability Index (FVSI) is employed as the primary metric for assessing voltage stability, where lower values (approaching zero) indicate improved stability. The proposed hybrid algorithm leverages GWO's exploration capabilities and PSO's exploitation strengths to overcome the limitations of individual algorithms. The methodology is validated on a 35-bus distribution system with a total load demand of 1.89 MW and 1.3455 MVAr. Results show that the hybrid GWO-PSO achieves an average FVSI reduction of 16.98%, significantly outperforming both standalone GWO (12.45%) and PSO (14.32%) implementations. The voltage profile across all buses is substantially improved, with the hybrid approach maintaining voltages closer to nominal values of 1.0 p.u. compared to the base case where many buses operate under low voltage conditions. The hybrid algorithm demonstrates faster convergence, reaching optimal solutions within 100 iterations compared to individual GWO and PSO implementations. The coordinated placement strategy determined optimal DG and STATCOM sizes and locations, effectively addressing voltage stability concerns in distribution systems experiencing rapid load growth with insufficient reactive power support. https://eej.aut.ac.ir/article_6008_569ff987c643b4bedf504efda8f786c2.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Intelligent Photovoltaic Conversion System with Cascaded Fuzzy MPPT for Efficient DC Power Transfer 553 570 6022 10.22060/eej.2026.24324.5683 EN Golla Satyanarayana Assistant Professor, Department of Electronic Engineering, Godavari Institute of Engineering Science and Technology (A), Rajahmundry 0009-0000-2305-0244 Tappeta Amai Kiran Associate Professor, Department of Electronic Engineering, Godavari Global University, Rajahmundry Rushan Kumar UG Scholar, Department of Electronic Engineering, Godavari Institute of Engineering Science and Technology (A), Rajahmundry Kodi Yohan UG Scholar, Department of Electronic Engineering, Godavari Institute of Engineering Science and Technology (A), Rajahmundry Mokim Ansari UG Scholar, Department of Electronic Engineering, Godavari Institute of Engineering Science and Technology (A), Rajahmundry Journal Article 2025 06 27 In most areas and power systems, Photovoltaic (PV) energy is rapidly becoming a significant component of the energy balance because of its rapid annual growth rate. Therefore, this research presents the PV-fed improved SEPIC-Zeta converter with a cascaded fuzzy algorithm based Maximum Power Point Tracking (MPPT) for efficient DC power transfer. At the beginning, the improved SEPIC-Zeta (ISZ) converter is exploited to enhance the PV system’s voltage. Then, the Cascaded Fuzzy MPPT algorithm is introduced for tracking the upmost power from PV system. Also, the high frequency inverter transmutes the DC to AC power and isolation is provided by the isolation transformer for ensuring safety and mitigating harmonic distortion on the source and load side. Additionally, the interleaved synchronous rectifier is exploited for converting the AC into a DC supply. The implemented research is validated in MATLAB tool, which demonstrates that the proposed work has a converter efficacy of 95.12 %, which handle fluctuations and disturbances more effectively, enhancing the reliability of overall system. https://eej.aut.ac.ir/article_6022_da54dd5a0398011cdfa50d559c2c0ef8.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Innovative Wind Energy System Featuring ANN-Controlled Pitch Regulation for Efficient Grid Integration 571 588 6009 10.22060/eej.2026.24404.5701 EN Tappeta Amar Kiran Associate Professor, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry 0000-0002-3021-1453 Dondapati Ravi Kishore Professor, Department of Electrical and Electronics Engineering, Godavari Global University, Rajahmundry 0000-0002-2567-2888 Appana Naga Pavani UG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Baswa Uma Maheswari UG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Chappa Sravani Kumari UG Scholar, Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Journal Article 2025 07 17 WECS are dynamic and intricate, considered by uncertainties and external disturbances. This research introduces an innovative pitch control strategy designed to improve energy stabilization and extraction predominantly for WT affected by unmodeled system dynamics to ensure stable operation under high wind speeds. For optimizing power capture, the ANN controller adapts dynamically to changing wind conditions by regulating the turbine blade’s pitch angle. For grid integration, a PWM rectifier transforms the variable-frequency AC power from the turbine into DC power. The simulations are conducted in MATLAB/Simulink tool to evaluate the control framework. It reveals that the superior performance of the ANN controller in reducing mechanical loads on turbines and platforms and maximizing power generation. Thus, it achieving reduced power and speed fluctuations, minimal overshoot and enhances the dynamic behaviour of WT. https://eej.aut.ac.ir/article_6009_37d7902cb2d3de686e497e31624d82e0.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 A Holistic Review of Deep Learning Methodologies for State Estimation in Lithium-Ion EV Batteries 589 608 6010 10.22060/eej.2026.24523.5752 EN SH Suresh Kumar Budi Assistant Professor, CMR Technical Campus, Kandlakoya, Medchal, 501401, Telangana, India 0000-0001-8537-8456 Journal Article 2025 09 11 Accurate estimation of battery parameters, particularly State of Charge (SOC) and State of Health (SOH), is critical for the operational reliability and safety of electric vehicles (EVs). These parameters influence driving range, charging strategy, and long-term battery lifespan. Traditional methods such as Coulomb counting, equivalent circuit models, and Kalman filters have been standard for battery state estimation but struggle with noisy data, variable loads, and nonlinear battery ageing. Recently, deep learning has shown promise in addressing these challenges by offering more robust and adaptive performance.<br>A recent review proposes a 4C framework—Correctness, Compute, Calibration, and Compliance—to evaluate deep learning models for next-generation Battery Management Systems (BMS). This scheme prioritises practical deployment aspects alongside accuracy. The review covers over 60 studies from 2019 to 2024, assessing model architectures, input features, training methods, and deployment readiness. It highlights advances such as physics-informed and uncertainty-aware models and offers a comparative evaluation of accuracy and computational efficiency on public datasets.<br>Deep learning methods consistently outperform traditional approaches, achieving SOC errors below 2% and SOH deviations within ±3%. Transformer-based and hybrid models improve accuracy by 10–20% compared to simpler recurrent models. Lightweight architectures like GRUs offer fast inference (less than 20 milliseconds), suitable for in-vehicle real-time applications.<br>Despite promising results, challenges remain around data generalizability, explainability, and real-time deployment. The 4C framework offers a roadmap for bridging laboratory advances with reliable, production-ready BMS technologies. https://eej.aut.ac.ir/article_6010_c4c455df3c54f292ae22f6791fd2553e.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 A Deep Reinforcement Learning Approach for Predictive Maintenance in Edge-Enabled Sensor Systems 609 622 5995 10.22060/eej.2026.24815.5769 EN Ladan Zahmatkeshan Department of Mining Engineering, University of Kashan, Kashan, Iran Alireza Javadi Noshabadi Department of Mining Engineering, University of Kashan, Kashan, Iran 0000-0001-6239-7160 Aliakbar Abdollahzadeh Department of Mining Engineering, Amirkabir University of Technology, Tehran, Iran Sajjad Talesh Hosseini Department of Mining Engineering, Faculty of Engineering, Imam Khomeini International University, Qazvin, Iran 0009-0008-1720-0574 Journal Article 2025 09 26 Unexpected failures in essential industrial systems can cause operational disruptions and financial losses. To mitigate unplanned downtime and maintain safe, efficient functioning of critical assets, predictive maintenance strategies are essential. However, with the rapid increase in sensor-equipped machinery, the overwhelming volume of generated data has outpaced the capabilities of traditional machine learning models to provide accurate, real-time diagnostics. This research introduces a model-free deep reinforcement learning (DRL) approach tailored for predictive maintenance within sensor-integrated equipment networks. Each machine is equipped with a sensor module that captures real-time data and detects anomalies. Unlike conventional opaque regression-based methods, the proposed framework autonomously determines optimal maintenance policies and delivers actionable insights for each individual device. Experimental evaluations indicate the potential of this adaptive learning method to extend across diverse maintenance scenarios. https://eej.aut.ac.ir/article_5995_fcd4c889d516a54d5371f00e3fdd70dc.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 LDMO: Hybrid Lemur–Dwarf Mongoose Optimisation Framework for Multi-Objective Application Mapping In 3D-NoC 623 644 6043 10.22060/eej.2026.25052.5827 EN Juliet Rose David Raj Beatrice Rajam Department of Electronics and Communication Engineering, Mar Ephraem College of Engineering and Technology, Malankara Hills, Elavuvilai, Marthandam, Tamil Nadu 0009-0000-7994-2209 Jaya Thirasamma Department of Electronics and Communication Engineering, Saveetha Engineering College, Saveetha Nagar, Kanchipuram - Chennai Rd, Sriperumbadur, Chennai Journal Article 2025 11 06 Three-dimensional Networks-on-Chip (3D-NoC) application mapping is a nondeterministic polynomial-time hard problem by nature. Under tight design restrictions, the efficient allocation of Intellectual Property (IP) cores to processing components must balance silicon area, reduce power consumption, and reduce end-to-end latency. To achieve improved mapping quality for 3D-NoC designs, this paper presents a Hybrid Lemur–Dwarf Mongoose Optimisation (LDMO) approach that addresses the cooperative exploitation abilities of Dwarf Mongoose Optimisation and the exploratory behaviour of Lemur Optimisation. To prevent premature stagnation, the Lemur Optimisation Algorithm mimics lemur cliff-leaping and tree-navigating behaviour during first stage. This produces a diversity of initial mapping candidates with high population variance. To optimise the mapping to global optima with high convergence speed, the Dwarf Mongoose Optimisation Algorithm employs adaptive leadership, sentinel–scout coordination, and foraging-based neighbourhood search during the second stage. The average communication delay (hop-dependent propagation, serialisation, and router latencies), area overhead (switch, interconnect, and core dimensions), and total power dissipation (router and interconnect power) are all minimised at the same time through a multi-objective fitness function. By adaptive switching between the two phases, the hybrid approach dynamically trades off between exploration and exploitation to ensure robustness across a wide range of communication demands and traffic patterns. Simulation outcomes demonstrate that the proposed LDMO framework consistently delivers reduced computation overhead, marked improvements in latency, and substantial energy efficiency. Furthermore, as the number of cores and communication links scale upward, the hybrid optimisation strategy maintains high-quality mapping solutions, underscoring its robust scalability across diverse NoC configurations. https://eej.aut.ac.ir/article_6043_2e9777b99786a3ef6e5d786e2bc2e16f.pdf

Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 3 2026 07 01 Design of a Hexagonal Stepped Impedance Resonator Textile Antenna for Robust Biomedical and Environmental Monitoring 645 662 6030 10.22060/eej.2026.25097.5837 EN Yeddu Eswar Vasanth Kumar M.Tech, (Ph.D), GIET University, Gunupur, Odisha, India 0000-0003-3234-4695 Gurindapalli Rajita Ph.D, Dr. G. Rajita, GIET University, Gunupur, Odisha, India Kokilagadda Phaninder Vinay Ph.D, Dr. K. P. Vinay, RAGHU Engineering College, Vskp, A.P, India Journal Article 2025 11 19 This work presents a compact, hexagon-shaped wideband antenna designed for wearable applications in the Industrial, Scientific, and Medical band. The antenna is fabricated on a flexible felt substrate with a dielectric constant of 1.22 and a loss tangent of 0.016, achieving a compact footprint of 35 × 30 × 2 mm³. A Coplanar Waveguide feed integrated with a Stepped Impedance Resonator is employed to enhance impedance matching and bandwidth performance. The felt substrate exhibits excellent mechanical durability and electromagnetic stability under repeated bending and typical wear conditions, ensuring reliable long-term operation. The proposed antenna achieves a peak gain of 7.31 dB at 5.82 GHz and maintains a Specific Absorption Rate of 1.08 W/kg for 1 g of tissue, which is well within regulatory safety limits. Beyond communication, the antenna demonstrates moisture-sensing capability, exhibiting a consistent downward shift in resonant frequency with increasing substrate humidity. While stable under bending, excessive moisture leads to detuning and impedance mismatch. The strong correlation between simulated and measured results validates the proposed design as a robust and multifunctional solution for wearable biomedical and ultra-wideband sensing applications. https://eej.aut.ac.ir/article_6030_3a24b25a7b092a252166a1641ae953e7.pdf