Showing 13 results for UDDIN
𝐒𝐢𝐫𝐚𝐣𝐮𝐬 𝐒𝐚𝐥𝐞𝐡𝐢𝐧, Shakila Rahman, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐍𝐮𝐫, 𝐀𝐡𝐦𝐚𝐝 𝐀𝐬𝐢𝐟, 𝐌𝐨𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐢𝐧 𝐇𝐚𝐫𝐮𝐧, Jia Uddin,
Volume 20, Issue 4 (Special Issue on ADLEEE - December 2024)
Abstract
Abnormal activity detection is crucial for video surveillance and security systems, aiming to identify behaviors that deviate from normal patterns and may indicate threats or incidents such as theft, vandalism, accidents, and aggression. Timely recognition of these activities enhances public safety across various environments, including transportation hubs, public spaces, workplaces, and homes. In this study, we focus on detecting violent and non-violent activities of humans using a YOLOv9-based deep learning model considering the above issues. A diverse dataset has been built of 9,341 images from various platforms, and then the dataset has been pre-processed, i.e., augmentation, resizing, and annotating. After pre-processing, the proposed model has been trained which demonstrated strong performance, achieving an F1 score of 95% during training for 150 epochs. It was also trained for 200 epochs, but early stopping was applied at 148 epochs as there was no significant improvement in the results. Finally, the results of the YOLOv9-based model have been analyzed with other baseline models (YOLOv5, YOLOv7, YOLOv8, and YOLOv10) and it performed better compared with others.
Shuvojit Kundu, Tuhel Ahmed, Jia Uddin,
Volume 21, Issue 1 (March 2025)
Abstract
This study aims to evaluate a cantilever beam type piezoelectric energy harvester operating on train-induced vibrations for powering Wireless Sensor Networks (WSNs) used in railway track monitoring systems. Harvester's behaviors under different conditions are simulated in MATLAB using the analytical model. Natural frequency, maximum deflection, and stress are calculated with greater precision using eigen frequency and stationary analysis using COMSOL Multiphysics. At a base excitation of 2 g and a resonant frequency of 4.38 Hz, the simulated results showed that the developed energy harvester prototype could generate up to 14 V of AC output voltage and 550 mW of output power. These findings highlight the promising potential of the proposed energy harvester for transforming train mechanical energy into electrical power. This energy harvester's viability and dependability for real-world applications in monitoring railway tracks are supported by developed analytical and simulation models.
Hanim Suraya Mohd Mokhtar, Aimi Salihah Abdul Nasir, Mohammad Faridun Naim Tajuddin, Muhammad Hafeez Abdul Nasir, Kumuthawathe Ananda Rao,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The rapid growth of photovoltaic (PV) systems has highlighted the need for efficient and reliable defect detection to maintain system performance. Electroluminescence (EL) imaging has emerged as a promising technique for identifying defects in PV cells; however, challenges remain in accurately classifying defects due to the variability in image quality and the complex nature of the defects. Existing studies often focus on single image enhancement techniques or fail to comprehensively compare the performance of various image enhancement methods across different deep learning (DL) models. This research addresses these gaps by proposing an in-depth analysis of the impact of multiple image enhancement techniques on defect detection performance, using various deep learning models of low, medium, and high complexity. The results demonstrate that mid-complexity models, especially DarkNet-53, achieve the highest performance with an accuracy of 94.55% after MSR2 enhancement. DarkNet-53 consistently outperformed both lower-complexity models and higher-complexity models in terms of accuracy, precision, and F1-score. The findings highlight that medium-depth models, enhanced with MSR2, offer the most reliable results for photovoltaic defect detection, demonstrating a significant improvement over other models in terms of accuracy and efficiency. This research provides valuable insights for optimizing defect detection systems in photovoltaic applications, emphasizing the importance of both model complexity and image enhancement techniques for robust performance.
Wan Ismail Ibrahim, Nasiruddin Sadan, Noorlina Ramli , Mohd Riduwan Ghazali Riduwan Ghazali , Ilham Fuad,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Hydrokinetic energy harnessing has emerged as a promising renewable energy that utilizes the kinetic energy of moving water to generate electricity. Nevertheless, the variation and fluctuation of water velocity and turbulence flow in a river is a challenging issue, especially in designing a control system that can harness the maximum output power with high efficiency. Besides, the conventional Hill-climbing Search (HCS) MPPT algorithm has weaknesses, such as slow tracking time and producing high steady-state oscillation, which reduces efficiency. In this paper, the Variable-Step Hill Climbing Search (VS-HCS) MPPT algorithm is proposed to solve the limitation of the conventional HCS MPPT. The model of hydrokinetic energy harnessing is developed using MATLAB/Simulink. The system consists of a water turbine, permanent magnet synchronous generator (PMSG), passive rectifier, and DC-DC boost converter. The results show that the power output achieves a 28 % increase over the system without MPPT and exhibits the lowest energy losses with a loss percentage of 0.9 %.
Yanawati Yahya, Nor Shafiqin Shariffuddin, Muhammad Khairul Hisyam Jarail, Dina Maizana, Phd Ibrahim Alhamrouni, Mohd Khairil Rahmat,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Induction motors are highly favored in industrial applications for their ease of operation, compactness, lightweight, efficiency, low maintenance, and cost-effectiveness. They are widely used in conveyors, compressors, crushers, drills, fans, escalators, refrigerators, and electric vehicles. In Malaysia, industrial motors account for about 48% of energy consumption. This research introduces an improved rotor design with optimized rotor bars. Using MotorSolve (IM) software and theoretical calculations, the study found that the new design boosts energy efficiency. The new rotor bar design achieved an energy efficiency of 76.92%, compared to 74% for the current design. In terms of energy efficiency, this research found that adopting high-efficiency motors in industrial applications can save a significant amount of energy. These motors can also be used in a variety of horsepower ranges. The research suggests a maintenance plan for malfunctioning motors that attempts to reduce energy consumption, motor losses, and CO2 emissions in any apparatus. These results offer valuable insights for policymakers to refine energy policies for induction motors. In the future, real-time estimation of the motor's actual operating loss will be required to properly predict the trend in motor efficiency loss under various failure scenarios, which is consistent with the research goal of reducing energy losses in induction motors.
Malik Khalid , Baharuddin Ismail , Chanuri Charin, Arnawan Hasibuan , Abd Alazeez Almaleeh,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This paper presents a comprehensive research endeavor focused on evaluating the influence of renewable energy, particularly wind power, on power quality within the context of Jordan's electrical grid. The escalating global demand for energy, coupled with the imperative to curb greenhouse gas emissions, has propelled the rapid adoption of renewable energy sources. Against this backdrop, the study aims to meticulously analyze the effects of wind energy projects on power quality parameters such as voltage fluctuations, harmonics, and power factor. Through an extensive methodology comprising data collection, rigorous analysis, and advanced simulation techniques, actionable insights are provided into the seamless integration of renewable energy into existing grid infrastructures. In this work, power quality parameters like Total Harmonic Distortion, flickers, power frequency, Crest factor, and voltage unbalance are measured at Al-Tafilah Governorate, Jordan. The significance of this study lies in its contribution to the development of strategies and guidelines essential for policymakers, engineers, and stakeholders. By fostering a deeper understanding of the interplay between renewable energy and power quality, the findings aim to facilitate the establishment of a sustainable and resilient energy system in Jordan. Beyond mitigating climate change and enhancing energy security, this research underscores the pivotal role of renewable energy in ushering in a greener, cleaner future for generations to come.
Mohamad Haniff Junos, Anis Salwa Mohd Khairuddin, Elmi Abu Bakar, Ahmad Faizul Hawary,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Vehicle detection in satellite images is a challenging task due to the variability in scale and resolution, complex background, and variability in object appearance. One-stage detection models are currently state-of-the-art in object detection due to their faster detection times. However, these models have complex architectures that require powerful processing units to train while generating a large number of parameters and achieving slow detection speed on embedded devices. To solve these problems, this work proposes an enhanced lightweight object detection model based on the YOLOv4 Tiny model. The proposed model incorporates multiple modifications, including integrating a Mix-efficient layer aggregation network within its backbone network to optimize efficiency by reducing parameter generation. Additionally, an improved small efficient layer aggregation network is adopted in the modified path aggregation network to enhance feature extraction across various scales. Finally, the proposed model incorporates the Swish function and an extra YOLO head for detection. The experimental results evaluated on the VEDAI dataset demonstrated that the proposed model achieved a higher mean average precision value and generated the smallest model size compared to the other lightweight models. Moreover, the proposed model achieved real-time performance on the NVIDIA Jetson Nano. These findings demonstrate that the proposed model offers the best trade-offs in terms of detection accuracy, model size, and detection time, making it highly suitable for deployment on embedded devices with limited capacity.
Ying Foo Leong, Nizaruddin M. Nasir, Suliana Ab-Ghani, Norazila Jaalam, Nur Huda Ramlan,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This paper focuses on the application of a cascaded multilevel inverter, specifically the 5-level multilevel inverter, utilizing a proposed controller known as the FLC-PSO-PI controller. The primary challenge addressed in this research is the precise regulation of output voltage in the multilevel inverter during load variations while meeting voltage harmonic and transition requirements as per industry standards, which are the 10 % voltage limit recommended by IEC and 8 % of total harmonic distortion (THD) by IEEE. An innovative solution is proposed by integrating PSO and FLC to dynamically adapt the controller in real-time, ensuring stable and accurate output voltage regulation. The proposed controller is designed and simulated using MATLAB/Simulink, and its performance is compared with PSO-PI and no controller under various load conditions. The results demonstrate that the FLC-PSO-PI controller significantly enhances output voltage regulation were achieving the desired peak voltage and low THD across different load scenarios, including half load to full load (0.8 %) and no load to full load (0.89 %). Furthermore, the FLC-PSO-PI controller exhibits superior transient response characteristics, such as reduced overshooting (2.89 %), faster rise time at 36.946 µs, and satisfactory settling time at 151.014 µs. This research contributes to the advancement of multilevel inverter technology and its potential applications in renewable energy systems, motor drives, and grid-connected devices. The proposed FLC-PSO-PI controller offers a promising solution for precise voltage regulation in multilevel inverters, enhancing their performance and enabling widespread adoption in various industrial sectors.
Mohd Zulhisham Mohd Radzi, Baharuddin Ismail, Muhammad Mokhzaini Azizan,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
The rise of nonlinear and unbalanced loads in modern electrical systems poses challenges to power quality management. These loads, prevalent in electronic devices and industrial equipment, induce harmonic distortions and unbalance, adversely affecting the neutral conductor in three-phase systems. This study investigates these effects through modeling and simulation using MATLAB/Simulink and symmetrical components theory for detailed power quality analysis. The research focuses on three scenarios: nonlinear loads, unbalanced loads, and combined nonlinear-unbalanced loads. Simulation results show that nonlinear loads significantly increase harmonic content, while unbalanced loads lead to notable power quality deviations. When combined, these conditions exacerbate harmonic distortions and unbalance, resulting in higher neutral current magnitudes. Key findings highlight the severe impact of combined load conditions on the neutral conductor, emphasizing the need for accurate modeling and analysis. This research provides valuable insights and practical recommendations for addressing the challenges of nonlinear and unbalanced loads, contributing to improved power system design and management.
Murni Nabila Mohd Zawawi, Zainuddin Mat Isa, Baharuddin Ismail, Mohd Hafiz Arshad, Ernie Che Mid, Md Hairul Nizam Talib, Muhammad Fitra Zambak,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
This study introduces a pioneering method to enhance the efficiency and effectiveness of three-phase five-level reduced switch cascaded H-bridge multilevel inverters (CHB MLI) by employing the Henry Gas Solubility Optimization (HGSO) algorithm. Targeting the selective harmonic elimination (SHE) technique, the research emphasizes the optimization of switching angles to significantly reduce total harmonic distortion (THD) and align the fundamental output voltage closely with the reference voltage. Central to this exploration are three distinct objective functions (OFs), meticulously designed to assess the HGSO algorithm’s performance across various modulation indices. Simulation results, facilitated by PSIM software, illustrate the impactful role these objective functions play in the optimization process. OF1 demonstrated a superior ability in generating low OF values and maintaining a consistent match between reference and fundamental voltages across the modulation index spectrum. Regarding the reduction of THD, it is crucial to emphasize that all OFs can identify the most effective switching angle to minimize THD and eliminate the fifth harmonic to a level below 0.1%. The findings highlight the potential of HGSO in solving complex optimization challenges within power electronics, offering a novel pathway for advancing modulation strategies in CHB MLIs and contributing to the development of more efficient, reliable, and compact power conversion systems.
Nurul Husna Abd Wahab, Mohd Hafizuddin Mat, Norezmi Md Jamal, Nur Hidayah Ramli,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
In islanded microgrids, circulating currents among parallel inverters pose significant challenges to system stability and efficient power distribution. Traditional droop control methods often struggle to manage these currents effectively, leading to inefficiencies and potential system damage. This study introduces an advanced fuzzy-robust droop control strategy that integrates fuzzy logic with robust droop control to address these challenges. By incorporating fuzzy logic, the proposed strategy enhances the adaptability of droop control to varying system conditions, improving the management of circulating currents and ensuring more accurate power sharing among inverters. Comprehensive mathematical modeling and extensive simulation analyses validate the performance of this control strategy. The results show that the fuzzy-robust droop control method significantly outperforms conventional approaches, achieving up to a 70% reduction in circulating currents. This improvement leads to a substantial reduction in power losses and enhances the dynamic response under varying load conditions. Additionally, the strategy improves voltage and frequency regulation, contributing to the overall stability and reliability of the microgrid. The findings provide a robust solution to the longstanding issue of circulating currents, optimizing microgrid operations, and paving the way for more efficient and resilient distributed energy systems. The advanced control strategy presented in this study not only addresses critical challenges but also demonstrates the potential for innovative methodologies to meet the growing demands of future energy infrastructures, where reliability and efficiency are essential.
Ahmad Syukri Abd Rahman, Mohamad Nur Khairul Hafizi Rohani, Nur Dini Athirah Gazata, Afifah Shuhada Rosmi, Ayob Nazmi Nanyan, Aiman Ismail Mohamed Jamil, Mohd Helmy Halim Abdul Majid, Normiza Masturina Samsuddin,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Partial discharge (PD) is a significant concern in the operation of rotating machines such as generators and motors, as it can lead to insulation degradation over time, reducing the reliability and lifespan of the machines. To monitor PD activity, coupling capacitors (CC) are widely used as sensors for online PD detection, as they can effectively capture PD pulses in high-voltage (HV) rotating machines. The primary objective of this research is to measure and analyze PD signals using a CC sensor for HV rotating machines under varying input voltages and frequencies, following the guidelines of the IEC 60270 standard and utilizing the MPD 600 device. The experimental setup includes performing insulation resistance (IR) testing, PD calibration, and PD measurement. Additionally, this paper provides a detailed study of PD signal characteristics, specifically focusing on phase-resolved partial discharge (PRPD) patterns, to understand the behavior of PD in HV rotating machines, enhancing fault diagnosis and preventive maintenance strategies.
Ahmad Syukri Abd Rahman, Mohamad Nur Khairul Hafizi Rohani, Nur Dini Athirah Gazata, Afifah Shuhada Rosmi, Ayob Nazmi Nanyan, Aiman Ismail Mohamed Jamil, Mohd Helmy Halim Abdul Majid, Normiza Masturina Samsuddin,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Partial discharge (PD) is a critical phenomenon in electrical systems, particularly in high-voltage (HV) equipment like transformers, cables, switchgear, and rotating machines. In rotating machines such as generators and motors, PD is a significant concern as it leads to insulation degradation, potentially resulting in catastrophic failure. Effective and reliable diagnostic techniques are essential for detecting and analyzing PD to ensure the operational safety and longevity of such equipment. Various PD detection methods have been developed, including coupling capacitor (CC), high-frequency current transformer (HFCT), and ultra-high frequency (UHF) techniques, each offering unique advantages in assessing the condition of HV electrical systems. Among these, coupling capacitors have gained significant attention due to their ability to improve the accuracy, sensitivity, and efficiency of PD detection in rotating machines. This study focuses on the advancements in coupling capacitor-based techniques and their critical role in enhancing PD diagnostics for monitoring and maintaining high-voltage rotating machinery.
