International Journal of Modern Electrical Engineering and Intelligent Automation

Three-Phase AC-DC Converter for High-to-Low Voltage Applications

Mohammed Ahnaf Ali — Thu, 21 May 2026 00:00:00 +0000

This work describes the design and modeling of a three-phase AC-DC converter for high-to-low-voltage applications that uses thyristor-based rectification. The research compares the effectiveness of 6-pulse and 12-pulse converter topologies, with an emphasis on total distortion of harmonics (THD), current stress, semiconductor losses, and output DC voltage profiles. The converter is meant to provide a regulated DC output by adjusting the firing angle of thyristors. A mathematical technique is used to calculate the firing angle required to achieve the desired output voltage. To assess operational performance under identical input circumstances, the system is modeled and simulated in MATLAB/Simulink. The simulation results show that both topologies can produce the necessary DC output; nevertheless, there are considerable disparities in waveform quality and ripple content. Compared with the 6-pulse converter, the 12-pulse arrangement performs better, with less ripple and a more stable output. In comparison, the 6-pulse converter has higher ripple and requires additional filtering for reliable operation. The results demonstrate that increasing the number of pulses enhances the quality of the DC output and decreases harmonic distortion, making the 12-pulse arrangement more appropriate for high-performance applications.

Advanced Strategies for Energy Efficiency Enhancement and Performance Optimization in Distribution Transformers

Md. Ali, Syed Tohabbul Murshed, ASM Shamim Hasan, Md. Sumon Ali, Md. Sohel Rana — Wed, 03 Jun 2026 00:00:00 +0000

This work focuses on enhancing the energy efficiency and operational performance of distribution transformers, which serve as essential components in electrical power distribution networks because they step down high transmission voltages to levels suitable for residential, commercial, and industrial consumers. As the final stage before electricity reaches end users, their efficiency has a direct impact on system reliability, stability, and economic performance. However, because these transformers operate continuously under fluctuating load conditions, they experience significant energy losses that reduce overall efficiency and increase operational costs. These losses mainly occur as core (iron) losses and copper (winding) losses. Core losses arise from hysteresis and eddy currents within the magnetic core. Hysteresis loss is caused by repeated magnetization cycles, while eddy current loss results from circulating currents induced by alternating magnetic fields. In contrast, copper losses depend on the resistance of the windings and increase with the square of the load current, making them more pronounced during peak demand periods. Improving transformer efficiency is therefore essential in modern power systems, where energy conservation and sustainability are key priorities. Even small efficiency gains can lead to substantial energy savings across large networks. This study explores advanced techniques such as optimized winding design, improved cooling systems, and the use of amorphous metal cores. Optimized windings reduce resistance and heat generation, while enhanced cooling maintains stable operating temperatures and minimizes thermal stress. Additionally, amorphous cores significantly lower core losses due to their superior magnetic properties. The findings demonstrate that while each method improves performance individually, their combined application produces a greater overall reduction in energy losses.

Automatic Detection of Surface Damage and Discoloration in Transmission-line Insulators using YOLOv4

Kazi Firoz Ahmed, Shuvo Dip Roy, Abu Hena Md Shatil, Tanbir Ibne Anowar — Thu, 04 Jun 2026 00:00:00 +0000

Surface deterioration of overhead transmission-line insulators poses a well-documented threat to grid reliability, yet timely identification of cracks, broken sheds, and contamination-induced discoloration remains operationally challenging because conventional visual inspection is slow, subjective, and hazardous at tower height. This study describes an end-to-end deep-learning inspection framework built on the YOLOv4 (You Only Look Once version 4) single-stage detector, targeting three semantically distinct classes: intact insulator body, mechanically damaged region, and discolored surface patch. The discoloration class captures an early-stage degradation indicator that precedes structural fracture and is routinely missed by binary defect classifiers. A purpose-assembled dataset of 1,247 field images was gathered from pole-mounted cameras and UAV (Unmanned Aerial Vehicle) platforms, preprocessed with per-channel z-score normalization, and augmented through mosaic composition, random scale jitter, color-space perturbation, and Gaussian noise injection to mitigate class imbalance and viewpoint variability. Anchor clusters were derived by k-means on the annotated bounding-box population to replace the ImageNet-tuned defaults, and training was conducted on CSPDarknet-53 (Cross Stage Partial Darknet-53) with the complete YOLOv4 bag-of-freebies schedule for 300 epochs. On the held-out test partition, the detector achieved an average precision of 96.47% for the insulator class, 99.17% for the damaged-part class, and an overall mean average precision of 97.82% at an IoU (Intersection over Union) threshold of 0.50, while sustaining 43.2 frames per second on the training hardware. A structured comparison against Faster R-CNN (region-based convolutional neural network), SSD (single shot multibox detector), and YOLOv3 baselines confirms a 4.1-percentage-point mAP (mean average precision) gain relative to the nearest single-stage competitor at comparable latency. These findings establish YOLOv4 as a practically viable baseline for automated insulator health monitoring and motivate further work toward edge-device deployment and multi-degradation severity scoring.

Smart Water Waste Management using an Autonomous Cleaning Robot

J. Roscia Jeya Shiney, A. Allwyn Clarence Asis — Fri, 05 Jun 2026 00:00:00 +0000

Marine plastic pollution poses a significant threat to ecosystems, biodiversity, and global sustainability. Existing cleanup methods, such as manual collection and large-scale passive systems, are often costly, inefficient, and limited in scope. This study explores the development of an economical robotic boat designed to autonomously collect plastic waste while integrating anti-pollutant technologies to enhance marine environmental protection. The proposed work examines cost-effective designs that leverage automation to ensure efficient waste collection with minimal environmental impact. Additionally, the integration of filtration mechanisms to address microplastics and chemical pollutants is also considered. By addressing key challenges such as affordability, energy efficiency, and scalability, this work aims to contribute to the advancement of sustainable and autonomous solutions for marine waste management. The proposed robotic system has the potential to revolutionize ocean cleanup efforts and support long-term ecological conservation. Furthermore, the integration of renewable energy sources, such as solar panels, can reinforce the sustainability of the boat by enabling prolonged deployment without reliance on fossil fuels. Collectively, these features position the robotic boat as a forward-thinking solution for addressing the growing crisis of marine plastic pollution.