Article Per Year (5 Year)
p-Index From 2019 - 2024
0.339
P-Index
This Author published in this journals
All Journal Perfecting a Video Game with Game Metrics Indonesian Journal of Electrical Engineering and Computer Science
Aulia Aulia
Andalas University
Computer Science & IT

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Clarification of the optimum silica nanofiller amount for electrical treeing resistance Z. Nawawi; M. A. B. Sidik; M. I. Jambak; C. L. G. Pavan Kumar; Aulia Aulia; M. H. Ahmad; A. A. Abd Jamil
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 17, No 6: December 2019
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v17i6.10605

Abstract

This paper aims to clarify the optimum amount of fumed silica (SiO2) nanofiller in resisting the initiation and propagation of electrical treeing in silicone rubber (SiR). Unlike other works, SiR/SiO2 nanocomposites containing seven different weight percentages of SiO2 nanofiller were prepared for this purpose. To achieve the objective, the electrical tree characteristics of the SiR/SiO2 nanocomposites were investigated by comparing the tree initiation voltage, tree breakdown time, tree propagation length and tree growth rate with its equivalent unfilled SiR. Moreover, the structural and morphological analyses were conducted on the SiR/SiO2 nanocomposite samples. The results showed that the SiR, when added with an appropriate amount of SiO2 nanofiller, could result in an improved electrical tree resistance. It implies that the 5 wt% of silica is the optimum amount to achieve the optimal electrical tree resistance such that above 5 wt%, the tree resistance performance has been abruptly reduced, subjected to the agglomeration issue.
Artificial Neural Network Application for Thermal Image Based Condition Monitoring of Zinc Oxide Surge Arresters Novizon Novizon; Zulkurnain Abdul-Malek; Aulia Aulia
Indonesian Journal of Electrical Engineering and Computer Science Vol 7, No 3: September 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v7.i3.pp593-605

Abstract

Manual analysis of thermal image for detecting defects and classifying of condition of surge arrester take a long time. Artificial neural network is good tool for predict and classify data. This study applied neural network for classify the degree of degradation of surge arrester. Thermal image as input of neural network was segmented using Otsu’s segmentation and histogram method to get features of thermal image. Leakage current as a target of supervise neural network was extracted and applied Fast Fourier Transform to get third harmonic of resistive leakage current. The classification results meet satisfaction with error about 3%.
Co-Authors A. A. Abd Jamil C. L. G. Pavan Kumar M. A. B. Sidik M. H. Ahmad M. I. Jambak Novizon, Novizon Z. Nawawi Zulkurnain Abdul-Malek