Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 DASA-Net: A Hybrid Dual-Attention Swin-Transformer Architecture for Precise Segmentation of Brain Tumors from MRI 665 680 6106 10.22060/eej.2026.25612.5970 EN Noor Isam Abdulnabi Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran Mansoor Fateh Faculty of Computer Engineering, Shahrood University of Technology, Shahrood, Iran 0000-0003-2133-3480 Saideh Ferdowsi School of Mathematics, Statistics and Actuarial Science, University of Essex, Colchester CO4 3SQ, UK Journal Article 2026 03 02 Brain tumor segmentation from Magnetic Resonance Imaging is a critical step for diagnosis and treatment planning, yet manual delineation is time-consuming and subjective. While deep learning models like Convolutional Neural Networks and Transformers have advanced automated segmentation, they face inherent limitations. CNNs struggle with long-range dependencies, while pure Transformers are often data-inefficient. To address these challenges, we propose DASA-Net, a novel hybrid Dual-Attention Swin-Transformer Architecture for precise brain tumor segmentation. Our model integrates a pre-trained Swin Transformer as the encoder to capture rich hierarchical and global contextual features. We introduce an Enhanced Spatial Attention module with residual connections to refine spatial details in the encoder's feature maps, and incorporate Squeeze-and-Excitation blocks in the decoder for adaptive channel-wise feature recalibration. Additionally, a Dynamic Hybrid Loss function is employed to balance Binary Cross-Entropy, Dice, and Focal losses, effectively addressing class imbalance and boundary ambiguity. Extensive experiments on the BRISC and Figshare brain tumor datasets demonstrate that DASA-Net achieves state-of-the-art performance, with a Dice score of 90.3% and IoU of 82.3% on BRISC, significantly outperforming existing methods. brain tumor MRI Segmentation Attention mechanism https://eej.aut.ac.ir/article_6106_6933b5648c59d618bbb30986c84080fe.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 SaveDoc: A Robust Technique for Degradation Classification on Ancient Document Images 681 694 6118 10.22060/eej.2026.24823.5773 EN Khairun Saddami Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia 0000-0001-8344-339X Rizka Ramadhana Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia Fathurrahman Fathurrahman Eletrical Engineering Department, College of Engineering and Physics, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia Khairul Munadi Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia 0000-0002-7507-9476 Fitri Arnia Department of Electrical and Computer Engineering, Faculty of Engineering, Universitas Syiah Kuala, Banda Aceh, Indonesia 0000-0001-6020-1275 Journal Article 2025 09 30 The analysis of degraded ancient documents remains challenging because multiple degradation patterns may appear within a single document image and reduce document quality. In this paper, we propose a combination of convolutional neural networks and extreme learning machine for patch-level degradation classification in ancient document images. The dataset was constructed from 100 source document images and cropped into 700 labeled patches. Four pre-trained architectures, namely VGG19, ResNet101, MobileNetV2, and EfficientNetB0, were used as feature extractors. The number of hidden nodes in the ELM classifier was tuned, and two activation functions, namely radial basis function and sigmoid, were evaluated. The experimental results show that EfficientNetB0 features combined with the extreme learning machine classifier achieved the best performance, with a testing accuracy of 98.21% ± 2.08%. This combination also produced faster inference time compared with the other evaluated models. These results indicate that the EfficientNetB0-extreme learning machine combination is effective for patch-level degradation classification in ancient document images. Degraded Ancient Documents Degradation Classification Extreme Learning Machine Pre-trained CNN features EfficientNet https://eej.aut.ac.ir/article_6118_1755c118e8859eb000eb6eca25369407.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 An Attention-Driven Deep Reinforcement Learning Framework for Energy-Efficient and Service-Level Agreement-Aware Cloud Task Scheduling 695 718 6069 10.22060/eej.2026.25455.5936 EN Rahul Bhatt School of Engineering & Computing, Dev Bhoomi Uttarakhand University, Dehradun, Uttarakhand, India Ritika Mehra School of Engineering & Computing, Dev Bhoomi Uttarakhand University Dehradun, Uttarakhand, India Kamal Upreti Department of Computer Science, Christ University, Delhi NCR Campus, Ghaziabad, India 0000-0003-0665-530X Journal Article 2026 01 28 Dynamic cloud and edge-cloud platforms require task schedulers that can respond to stochastic workloads while minimizing energy use and preserving service-level agreement reliability. This study proposes an attention-driven deep reinforcement learning framework for energy-efficient and service-level agreement-aware cloud task scheduling. The framework combines lightweight convolutional neural network and long short-term memory-based spatial-temporal feature extraction with a multi-head self-attention actor-critic decision module. The convolutional neural network and long short-term memory components capture local virtual machine workload patterns, whereas self-attention models global virtual-machine-to-virtual-machine dependencies for parallel and context-aware scheduling. The scheduling problem is formulated as a Markov decision process using a 242-dimensional virtual-machine-level state representation, probabilistic virtual-machine-to-host assignment actions, and a multi-objective reward function covering makespan, energy consumption, operational resource cost, and service-level agreement penalties. Experiments were conducted in a heterogeneous CloudSim environment with 100 hosts and 100 virtual machines. The proposed framework achieved a normalized makespan of approximately 0.85, a 14.4% reduction in total energy consumption, consistently low service-level agreement violation behavior, and controlled migration activity. Logged analysis further showed a response time of 10.0000 milliseconds per completed task or virtual machine event, supporting interval-based real-time feasibility. Cost is treated as an operational reward component, not as a standalone billing analysis. Cloud computing Task scheduling deep reinforcement learning Multi-Head Self-Attention Actor-Critic Learning Energy Efficiency Service-Level Agreement-Aware Scheduling CloudSim https://eej.aut.ac.ir/article_6069_55312eec654a75a08dc83de96adde735.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Integrating Real Secret Sharing with Distributed MPC for Confidential and Collision-Resilient Multi Quadrotors Formations 719 732 6063 10.22060/eej.2026.25522.5949 EN Abolfazl Mokhtari Faculty of Engineering and Flight, Imam Ali University, Tehran 0000-0002-4126-9183 Journal Article 2026 02 13 This paper presents a novel framework that integrates secure, distributed computation with advanced control to enable confidential and collision-resilient formation flight for multiple quadrotors. Conventional multi-agent systems are vulnerable to cyber threats such as eavesdropping and data manipulation, which can destabilize formations and compromise missions. In this study, to address this issue, Model Predictive Control is combined with a multi‑party computation protocol based on secret sharing. In this approach, each quadrotor converts its state data into secret shares and distributes them among the agents, and the required control computations are performed collaboratively on the encrypted data without revealing any sensitive information. This decentralized approach eliminates the central controller as a single point of failure and attack. Simulation results for six quadrotors flying in a circular formation among obstacles show that the system maintains formation accuracy and collision avoidance while remaining robust against eavesdropping and data manipulation. Quantitative evaluation shows that the formation tracking error remains below 0.08 m, all inter-agent and obstacle collisions are successfully avoided (100% success rate), and the secret-sharing protocol reconstructs the optimal control signals with a normalized mean squared error of less than 10⁻⁶, ensuring cryptographic integrity without compromising formation performance. The integration fundamentally enhances operational security, preserves agent privacy, and improves system fault tolerance and scalability for real-world adversarial environments. Unmanned Aerial Vehicles (UAVs) Formation Control Model Predictive Control (MPC) Multi-Party Computation Real Secret Sharing Cybersecurity decentralized control Obstacle avoidance https://eej.aut.ac.ir/article_6063_ae2a2db40a12ec0131d48acc1218d2ef.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Hybrid RST-Direct Thrust Force Control for Enhanced Performance of Linear Induction Motor 733 744 6105 10.22060/eej.2026.25020.5820 EN Laid Khettache Department of Electrical Engineering, Kasdi Merbah Ouargla University, Algeria 0000-0002-3388-3669 Abdeslam Benmakhlouf Department of Electrical Engineering, Kasdi Merbah Ouargla University, Algeria 0000-0003-0849-7229 Mohamed Redha Rezoug Department of Electrical Engineering, Kasdi Merbah Ouargla University, Algeria 0000-0003-1849-8989 Djalal Djarah Department of Electrical Engineering, Kasdi Merbah Ouargla University, Algeria 0000-0002-2480-9731 Journal Article 2025 10 31 This paper presents a novel control strategy that combines RST-based speed control with Direct Thrust Force Control to improve the operational performance of linear induction motors. This approach specifically targets the optimization of thrust force, magnetic flux, and current control. While traditional PID controllers are commonly used in direct torque control applications for rotary motors, their effectiveness in linear induction motors is limited by sensitivity to parameters and nonlinear effects caused by end-effect phenomena. These limitations frequently result in excessive overshoot, slower rise and settling times, as well as reduced control accuracy and system stability. In response, this study proposes the RST- Direct Thrust Force Control method, which is designed to achieve robust speed regulation and precise control over thrust force and flux under various operating conditions. The performance and accuracy of the proposed approach are evaluated and validated through simulations conducted in the MATLAB/Simulink environment. Moreover, comparative simulation analyses demonstrate that the RST- Direct Thrust Force Control technique significantly reduces overshoot, shortens rise and settling times, and confirming its superiority in maintaining control precision, including excellent reference tracking and disturbance rejection. The findings highlight the RST regulator combined with the Direct Thrust Force Control technique as a robust solution for overcoming the inherent limitations of conventional direct torque control in linear induction motor applications. This integrated control presents a promising approach to optimizing linear induction motor performance and aligns with industry demands for precision and reliability in dynamic environments. Thrust Optimization RST controller linear motors speed control end-effect https://eej.aut.ac.ir/article_6105_c1d53b7a97707b5cd1815c8d228d8ef1.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Hybrid Fault-Tolerant Controller Based Nonlinear Model Predictive Control For a Flexible Satellite 745 768 6117 10.22060/eej.2026.25440.5933 EN Morteza Salehipour Department of Aerospace Engineering , University of Isfahan ,Isfahan ,Iran Maryam Malekzadeh Department of Aerospace Engineering ,Sharif University of Technology, Tehran, Iran 0000-0003-1922-4488 Mahdi Mortazavi Department of Aerospace Engineering , University of Isfahan ,Isfahan ,Iran Journal Article 2026 02 18 This brief discusses the stabilization of a flexible satellite's attitude while considering actuator dynamics. It employs a constrained Lyapunov Nonlinear Model Predictive Control (LNMPC) method combined with the Feedback Linearization Technique (FBL). FBL is chosen for its ability to cancel the system's nonlinearity, making the control law easier to implement and more effective. more over the LNMPC is not able to nullify the low frequencies so it is necessary to combining it with a nonlinear controller. This composite controller aims for high accuracy in tracking the reference trajectory and, due to the LNMPC, provides tremendous robustness against unknown disturbances. The hybrid controller is designed to steer the satellite's attitude and bring the reaction wheel's angular momentum to zero to save energy. This paper addresses various challenges faced by satellites during their missions, including uncertainties, external torque disturbances, actuator faults, and vibrations of appendages. The performance of the proposed controller is analyzed while considering all these challenges. The active set method is used as the optimization algorithm, more over a nonlinear disturbance observer named Cascade Extended State Observer ( Cascade ESO) is introduced to overcome the external disturbance and improve the performance of the controller and closed-loop stability is addressed using a candidate Lyapunov function. Lyapunov nonlinear model predictive control Feedback linearization Flexible satellite actuator fault Momentum Management Cascade ESO Vibration suppression https://eej.aut.ac.ir/article_6117_84e2d85ac232c681a641da1ec663888c.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 PV Powered Unified Active Power Filter with Intelligent ANN Controller for PQ Improvement 769 792 6120 10.22060/eej.2026.24326.5684 EN Vendoti Suresh Department of Electrical & Electronics Engineering, Godavari Global University, Rajahmundry 0009-0006-1423-9217 Chinthakula Siva Ganesh Department of Electrical & Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry. 0009-0009-3801-2975 Puppala Sri Maha Lakshmi Department of Electrical & Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry. Pilla Manikanta Department of Electrical & Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry. Simma Saibhaskar Department of Electrical & Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry. Journal Article 2025 06 27 Power quality issues have emerged as one of the most significant problems facing a power system, due to its diverse nature and frequent occurrence. This research introduces a Photovoltaic (PV) incorporated Unified Active Power Filter (PV-UAPF) system and its control approach for generation of decarbonized clean energy and improved Power Quality (PQ). The UAPF, combining shunt and series converter, which is utilized for compensating the current quality and voltage quality issues. Moreover, the PV system is incorporated with Switched Inductor (SI) based Boost-Luo converter at DC link, which is connected between series and shunt converters. For control the operation of converter, PI controller is employed and its parameters are refined by Modified Red Kite Optimization (MRKO) algorithm. For ensuring efficient voltage regulation and to manage the functioning of UAPF, an Artificial Neural Network (ANN) controller is incorporated on load side. To further demonstrate developed work's efficacy, it is implemented in MATLAB/Simulink and comparison is made with existing methods. The outcomes reveals that the developed system has a THD value lower than 5%, ensures the quality of power is enhanced. PV system SI based Boost-Luo converter Modified red kite optimized PI controller UAPF ANN controller https://eej.aut.ac.ir/article_6120_cfa258af990f9cb188d36ddb5c6eb650.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Predicting Torque Ripple and Average Torque of a Switched Reluctance Motor Using MLP and RBF Models 793 802 6033 10.22060/eej.2026.25442.5934 EN Siroos Hemmati Kermanshah University of Technology, Kermanshah, Iran 0000-0002-6056-716X Milad Goldasteh Technical National University, Kermanshah, Iran Mohsen Asadi Razi university, Kermanshah, Iran Seyed Shahrooz Hosseini Azad University, Kermanshah, Iran Sobhan Roshani Azad University, Kermanshah, Iran Journal Article 2026 01 27 The optimal design of a Switched Reluctance Motor (SRM) requires an accurate model. Analytical models presented for SRM do not meet required accuracy. Approximate models also have varying degrees of accuracy in predicting SRM characteristics. Hence, in this paper, two Neural Network (NN) models, Radial Basis Function (RBF) and Multilayer Perceptron (MLP), have been proposed to predict torque ripple and average torque, respectively. To train and test the models, 100 samples were extracted, 90% for training and 10% for testing. Furthermore, the Finite Element Method (FEM) has been used to solve the samples. The influencing parameters of the proposed NN models (number of hidden layers, number of neurons in hidden layers, bias, etc.) also have been determined to achieve the desired accuracy and minimal complexity. To evaluate the performance of the models, two criteria, Root Mean Square Error (RMSE) and Mean Relative Error (MRE), have been used. Both criteria indicate that the MLP model is successful in predicting torque ripple, while the RBF model excels in predicting average torque. Switched Reluctance Motor Torque RBF MLP Neural Network https://eej.aut.ac.ir/article_6033_317d17f10845da500bcf49780b7f35bf.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Advanced Solar Grid Integration Solution Using a High-Efficiency Coupled Inductor Multiport Converter for Optimized Power Delivery 803 820 6064 10.22060/eej.2026.25521.5948 EN Tappeta Amar Kiran Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry 0000-0002-3021-1453 Dondapati Ravi Kishore Ravi Kishore Department of Electrical and Electronics Engineering, Godavari Global University, Rajahmundry. 0000-0002-2567-2888 Kosana Bhargava Rama Chinna Vyshnav Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Thorati Ajay Bhargav Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Mahanthi Aahaj Varun Department of Electrical and Electronics Engineering, Godavari Institute of Engineering and Technology (A), Rajahmundry Journal Article 2026 02 12 Currently, Photovoltaic (PV) solar power generation is integrated into distributed generation (DG) systems is becoming popular. Therefore, this research proposes a unique Coupled inductor based multiport (CIM) converter that combines battery storage and solar PV in a grid-connected system. The PV system produces inferior voltage owing to its intermittent and sporadic nature due to the climatic conditions. A CIM converter is utilized to convert the PV output voltage to the highest level. In order to combat the intermittency and instability associated with PV, a Proportional Integral (PI) controller is added to the grid to ensure a stable and uninterrupted supply of power. Also, the battery is exploited to store surplus energy from PV systems, permitting to use this stowed energy when the panels aren’t producing electricity. A Three phase Voltage Source Inverter (3ϕ VSI) is used to transform steady DC to AC voltage and a LC filter is employed to lessen harmonics. Additionally, the grid voltage synchronization is carried out by PI controller on load side. The developed system is also tested with the simulation outcomes from MATLAB/Simulink and comparison is made with conventional approaches. The obtained outcomes show that it attains the efficiency of 97.46%, ensures the optimized power is delivered to the grid. PV systems CIM converter PI controller Battery 3ϕ VSI https://eej.aut.ac.ir/article_6064_fb5c2bc1aa847f387022607d16adc510.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 New Approach to Assembling Used 18650 Cells by the DBSCAN Clustering Algorithm 821 838 6065 10.22060/eej.2026.24892.5788 EN Mohamed Redha Rezoug Department of Electrical Engineering, Faculty of Technology, Kasdi Merbah University – Ouargla 30000, Algeria 0000-0003-1849-8989 Laid Khettache Department of Electrical Engineering, Faculty of Technology, Kasdi Merbah University – Ouargla 30000, Algeria 0000-0002-3388-3669 Abdeslam Benmakhlouf Department of Electrical Engineering, Faculty of Technology, Kasdi Merbah University – Ouargla 30000, Algeria 0000-0003-0849-7229 Djalal Djarah Department of Electrical Engineering, Faculty of Technology, Kasdi Merbah University – Ouargla 30000, Algeria 0000-0002-2480-9731 Journal Article 2025 10 17 This study presents a diagnostic and regrouping approach for used 18650 lithium-ion cells using the DBSCAN clustering algorithm. A total of 154 cells were recovered from discarded laptop batteries. After electrical testing (constant-current discharge measuring capacity, voltage, and internal resistance), 120 cells (78%) were identified as healthy and suitable for reuse. The DBSCAN algorithm (eps=0.3, min_samples=10) was applied to cluster these 120 cells based on their electrical characteristics. The algorithm successfully formed three homogeneous clusters of 40 cells each. These clusters were assembled in a 3S40P configuration (three parallel packs of 40 cells connected in series) to form a second-life battery of approximately 81 Ah capacity at 10.9 V. Comparative analysis shows that DBSCAN outperforms K-means and hierarchical clustering for this application, achieving a silhouette coefficient of 0.62 versus 0.48 for K-means. The proposed method achieves a 78% cell recovery rate, demonstrating its effectiveness for battery recycling and second-life applications. DBSCAN Reconditioning Recycling lithium battery 18650 cell clustering https://eej.aut.ac.ir/article_6065_dfd786998e082758be12670d856df755.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Grid-Integrated Solar EV Charging Station with Double Stage Coupled Inductor Boost Converter and Artic Puffin Optimized PI Control 839 860 6127 10.22060/eej.2026.24622.5737 EN Sivaprasad Kollati Department of Electrical and Electronics Engineering, Godavari Global University, Rajahmundry, India. 0000-0002-7170-7316 Saritha Kandukuri Department of Electrical and Electronics Engineering, Godavari Global University, Rajahmundry, India Singamsetti Mohan Santhi Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), India. Nishu Kumar Kumar Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), India. Santhosh Kumar Magapu Department of Electrical and Electronics Engineering Godavari Institute of Engineering and Technology (A), India. Journal Article 2025 08 22 Environmental benefits of Electric Vehicles (EVs) are associated with reduction of direct air pollutants and reduction of greenhouse gases. In this paper, a novel solar-powered EV Charging Station (EVCS) is offered with the integration of a grid-based energy management system and a high-efficiency Double-Stage Coupled Inductor Boost Converter (DSCIB). A new Arctic Puffin Optimized (APO) Proportional-Integral (PI) controller is employed for preserving a constant DC-link voltage and optimal system performance. This approach optimizes the energy transfer efficiency and enhances a stability under dynamic operating conditions. DC bus is linked to electrical grid through LC filter and three phase Voltage Source Inverter (VSI), which allows bidirectional power flow and reactive power support. PI-based control loop is employed for grid synchronization, to properly balance the active and reactive power. The charging and discharging processes of EV battery are controlled via a bidirectional DC-DC (BDC) converter, which is structured with the PI control strategy to precisely control the voltage and current. This configuration offers the capability of battery-to-grid without any interruption and fast charging of EV batteries during excess PV generation. The MATLAB simulation results shows proposed architecture offers a promising option for sustainable EV integration in grid applications because of the coordinated control framework. From simulation converter attain efficiency of 98.4% and decrease THD of 1.02% ensure effective energy flow, voltage stability, and grid support. Photovoltaic system Double Stage Coupled Inductor Boost Converter Arctic Puffin Optimization Algorithm PI controller grid application https://eej.aut.ac.ir/article_6127_9570efef719d705326f0ff817ef084e6.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Robust Provincial Prioritization of Hybrid PV–Wind–Fuel Cell Systems in Iran Using TOPSIS and Monte Carlo Robustness Analysis 861 884 6123 10.22060/eej.2026.25630.5971 EN Alireza Solat Department of Electrical Engineering, Sav.C., Islamic Azad University, Saveh, Iran. 0000-0001-8012-0851 Journal Article 2026 03 18 The growing demand for energy and the need to reduce emissions have made hybrid renewable energy systems (HRES) a strategic priority. Among these systems, the integration of solar, wind, and fuel cell resources in a complementary structure offers significant potential for reliable and low-emission energy supply. However, optimal site selection for deploying such systems – particularly in countries with diverse climatic and contextual conditions like Iran – presents a multi-criteria challenge accompanied by inherent uncertainty. This study proposes an integrated decision-making framework for prioritizing solar–wind–fuel cell hybrid systems across Iran’s 31 provincial capitals. The technical, economic, and environmental performance of the hybrid system was simulated using HOMER, generating indicators such as Cost of Energy (COE), Net Present Cost (NPC), CO2 emissions, renewable energy share, and capacity factors. These indicators, along with contextual criteria like population, natural disaster risk, and land price, were integrated into the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the provinces. To assess robustness, an uncertainty analysis based on Monte Carlo simulation (10,000 iterations) was conducted. Results indicate that Tehran Province exhibits the highest closeness coefficient (~0.69) with a greater than 90% probability of being ranked among the top five. Conversely, regions such as Kerman and Sistan and Baluchistan consistently obtained lower ranks across most scenarios. This study demonstrates that integrating TOPSIS with Monte Carlo simulation provides not only quantitative rankings but also a risk-informed decision-making tool, effectively supporting policymakers in the siting of pilot renewable energy projects at the national scale. Multi-criteria decision-making, Uncertainty analysis, Spatial prioritization Cost of energy, System sustainability https://eej.aut.ac.ir/article_6123_4ca82b2a861f70cd15d83085b000dbde.pdf
Amirkabir University of Technology AUT Journal of Electrical Engineering 2588-2910 58 4 2026 10 01 Designing a robust method to improve voltage control of DC-DC buck converters 885 902 6091 10.22060/eej.2026.25791.6005 EN Farhad Amiri Department of Electrical Engineering, Tafresh University, Tafresh, Iran 0000-0001-9335-7823 Sajad Sadr Department of Electrical Engineering, Tafresh University, Tafresh, Iran 0000-0002-6113-8930 Journal Article 2026 05 03 Given that the DC-DC buck converter (DDBC) is an example of a nonlinear system, and also considering the disturbance and changes related to its parameters, the output voltage control in this DDBC will face several challenges. In this paper, a PD-PI cascaded controller is designed to control the voltage in the DC-DC buck converter. In this cascaded controller, the PD controller, as the outer loop controller, is responsible for responding quickly to voltage changes and damping them, and the PI controller is responsible for eliminating the error caused by voltage changes. Considering that the accurate and optimal adjustment of the parameters related to the cascaded controller is crucial to its performance, the Grey Wolf Optimizer algorithm has been employed to optimize these parameters. This algorithm offers several advantages, including intelligent search, parameter fine-tuning, and stable and rapid convergence. To assess the robustness of the proposed PD-PI(Grey Wolf Optimizer) approach against disturbances and parameter variations, multiple scenarios were developed and its performance compared with that of control strategies like PD-PI (Electric Eel Foraging Optimizer) and PD-PI (Flood algorithm). According to the results, the proposed method outperforms other control methods in terms of various criteria, including Settling time, Rise time, Peak time, Overshoot, and Steady state error. DC-DC buck converters output voltage control cascaded controller performance improvement https://eej.aut.ac.ir/article_6091_a4e858c15255e55d5e1e221bd151154f.pdf