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All Journal Asian Journal of Science, Technology, Engineering, and Art
Abdulhafiz, Sabo
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Arts Computer Science & IT Engineering Social Sciences

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Modelling and Analysis of a Power Transformer Using Finite Element Analysis Muhammad, Sabo Sani; Abdulrazak, Sabo; Bakare, G. A.; Abdulhafiz, Sabo; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5703

Abstract

This study presents an enhanced Finite Element Method (FEM) model for comprehensive analysis of power transformers, addressing electromagnetic, thermal, and electrostatic performance aspects with improved accuracy and efficiency. Conventional analytical approaches to evaluating transformer characteristics—such as core losses, copper losses, magnetic flux distribution, and thermal behavior—are often labor-intensive and susceptible to inaccuracies. To overcome these limitations, a double discretization FEM (DD-FEM) framework was developed using ANSYS Maxwell and ANSYS Mechanical software to simulate a 30 MVA, 132/33 kV three-phase power transformer. The electromagnetic simulation yielded core and copper losses of 19.62 kW and 97.03 kW, respectively, with DD-FEM reducing absolute errors by 1.38% and 1.48% compared to standard FEM methods. Thermal modeling under normal loading conditions indicated a peak winding temperature of 94.2°C, rising to 112.9°C during overloading (33 MVA), thus justifying the need for forced cooling systems. Electrostatic analysis confirmed that electric field stresses between windings remained within safe operational limits (10.48 kV/mm²), though a localized insulation weakness was identified between the low-voltage winding and the core (3.74 kV/mm²). Across all evaluated parameters, the DD-FEM model showed superior alignment with benchmark analytical results, reducing relative errors in core loss estimation by up to 12.2%. These results affirm the efficacy of the enhanced FEM approach in optimizing transformer design, enhancing operational reliability, and reducing engineering uncertainty, particularly under varying load and fault scenarios. The study demonstrates the critical role of advanced numerical tools in modern transformer engineering and high-fidelity system simulation.
Performance and Economic Evaluation of Power Transformer Muhammad, Sabo Sani; Abdulrazak, Sabo; Haruna, Y. S.; Abdulhafiz, Sabo; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 3 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i3.5704

Abstract

This study presents a comprehensive performance and economic evaluation of a three-phase 132/33 kV delta/star power transformer using an enhanced Finite Element Method (FEM) integrated with ANSYS Maxwell software. The transformer model, developed based on operational data from the Gudum substation in Bauchi, Nigeria, was designed to assess electromagnetic and thermal characteristics under no-load, full-load, and short-circuit conditions. The FEM simulation incorporated detailed geometric configuration, material properties (M125-027S laminated steel core and copper windings), optimized meshing, and coupling with external electrical circuits. Key performance indicators—including magnetic flux density, core and copper losses, voltage and current outputs, and efficiency—were evaluated under varying load scenarios. The model exhibited peak efficiency of 80.84% at 97.10% loading, and simulated load currents demonstrated loss reductions between 8.46% and 11.05% relative to empirical measurements, validating the model’s reliability. Furthermore, a life cycle cost (LCC) analysis was conducted using present-value cash flow techniques over a projected 22-year operational period. The total LCC was estimated at ₦2,777,811,381, with no-load and load losses accounting for ₦534.2 million and ₦1.88 billion, respectively. These findings underscore the substantial economic implications of design and material decisions in transformer manufacturing and operation. The study emphasizes the value of advanced FEM-based tools in optimizing transformer performance and cost-efficiency, offering strategic guidance for procurement, maintenance planning, and long-term infrastructure investment in power systems.
Modified Cardiac Arrhythmia Classification from Electrocardiography Signals Using a Convolutional Neural Network Model Abdulhafiz, Sabo; Gital, Abdulsalam Ya’u; Mohammed, Sani Sabo; Nazif, D. M.
Asian Journal of Science, Technology, Engineering, and Art Vol 3 No 4 (2025): Asian Journal of Science, Technology, Engineering, and Art
Publisher : Darul Yasin Al Sys

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58578/ajstea.v3i4.5905

Abstract

Manual classification of cardiac arrhythmias from electrocardiogram (ECG) signals is a labor-intensive and error-prone process due to the complex and variable nature of cardiac waveforms. Convolutional Neural Networks (ConvNets), widely recognized for their success in image classification, offer a promising solution for automating this task. This study proposes an enhanced ConvNet-based approach for the classification of cardiac arrhythmias, leveraging AlexNet as a feature extractor. The features obtained from the convolutional layers are input into a backpropagation neural network for final classification. The proposed model was evaluated on four distinct arrhythmia conditions using ECG waveforms from the MIT-BIH Arrhythmia Database. Comparative analysis against traditional models revealed the superior performance of the proposed ConvNet architecture, achieving high scores across multiple evaluation metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The feature extractor demonstrated robust performance, with classification accuracies of 1.00 and 0.99 on training and testing datasets, respectively. These findings underscore the potential of ConvNet-based models to serve as efficient, accurate, and fully automated tools for arrhythmia diagnosis, contributing significantly to advancements in cardiovascular disease detection and clinical decision support systems.
Co-Authors Abdulrazak, Sabo Bakare, G. A. Gital, Abdulsalam Ya’u Haruna, Y. S. Mohammed, Sani Sabo Muhammad, Sabo Sani Nazif, D. M.