Search published articles


Showing 3 results for Riza
Analysis of Fault Detection and Classification in Photovoltaic Arrays Using Neural Network-Based Methods
Arizadayana Zahalan, Samila Mat Zali, Ernie Che Mid, Noor Fazliana Fadzail,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Photovoltaic (PV) systems are vital in the global renewable energy landscape because of their capability to harness solar energy efficiently. Ensuring the continuous and efficient operation of PV systems is crucial in maximizing their energy contribution. However, these systems' reliability and safety remain critical because they are prone to various faults, mainly when operating in harsh environmental conditions. This study addresses these issues by exploring fault detection and classification in PV arrays using neural network (NN) -based techniques. A PV array model, consisting of 3x6 PV modules, was simulated using MATLAB Simulink to replicate real-world conditions and analyse various fault scenarios. An open circuit, a short circuit, and a degrading fault are the three types of faults considered in this study. The NN was trained on a dataset generated from the MATLAB Simulink model, encompassing normal operating and fault conditions. This training enables the network to learn the distinctive patterns associated with each fault type, enhancing its detection accuracy and classification capabilities. Simulation results demonstrate that the NN-based approach effectively identifies and classifies the three types of faults.
Deep Learning for Identification Malaria Diseases from Microscopic Image
Edy Victor Haryanto S, Aimi Salihah Abdul Nasir, Mohd Yusoff Mashor, Bob Subhan Riza, Zeehaida Mohamed,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Malaria is a parasitic disease that causes significant morbidity and mortality worldwide. Early diagnosis and treatment are crucial for preventing complications and improving patient outcomes. Microscopic examination of blood smears remains the gold standard for malaria diagnosis, but it is time-consuming and requires skilled technicians. Deep learning has emerged as a promising tool for automated image analysis, including malaria diagnosis. In this study, we propose a novel approach for identifying malaria parasites in microscopic images using the GoogLeNet. Our method includes enhancement with the AGCS method, color transformation with grayscale, adaptive thresholding for segmentation, extraction, and GoogLeNet-based classification. We evaluated our method on a dataset of malaria blood smear images and achieved an accuracy of 95%, demonstrating the potential of GoogLeNet for automated malaria diagnosis.
Back Flashover Voltage on Transmission Tower of 275 KV Extra High Voltage Line (Case Study: Galang-Binjai)
Surya Hardi, Ferry R. A. Bukit, Irfan Nofri, Riza R. Wirasari, Muhd Hafizi Idris, Muzamir Isa,
Volume 21, Issue 2 (Special Issue on the 1st International Conference on ELECRiS 2024 Malaysia - June 2025)
Abstract
Overvoltage at the insulator terminal caused by a lightning strike can occur in two ways, i.e., a direct lightning strike on the phase line and ground wire. The insulator can be exposed to the phenomenon of back flashover (BFO) if the terminal voltage of the insulator is higher than its insulator critical voltage The lightning current characteristics are distinguished by the maximum current and the steepness. Differences in the characteristics in this study are identified as International Electrical Commission (IEC) and Conseil International des Grands Reseaux Electriques (CIGRE) impulse waveform standards. The footing-tower grounding system comes in different configurations, such as horizontal, vertical, and grid. Alternative transient program (ATP) software was used for simulating lightning strikes on ground wire and phase lines. The results exhibit that the highest critical voltage of the insulators on the footing tower through grid grounding when the surge current strikes ground wire (3308kV – 3395 kV), with the magnitude of the lightning current ranging from (48 kA – 3395 kA). For lightning direct stroke on the phase line, the critical voltage on vertical grounding is highest on (2938 kV -3021 kV).  The surge current flow footing-tower is highest on the grid. The currents magnitude flow in footing tower were influenced by impedance of grounding.
Page 1 from 1     
Creative Commons License
© 2022 by the authors. Licensee IUST, Tehran, Iran. This is an open access journal distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) license.