Article Per Year (5 Year)
p-Index From 2020 - 2025
0.23
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics
M. Isa
Universiti Malaysia Perlis
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering

Published : 3 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 3 Documents
Search

 1 
Evaluation of earth fault location algorithm in medium voltage distribution network with correction technique N. S. B. Jamili; M. R. Adzman; S. R. A. Rahim; S. M. Zali; M. Isa; H. Hanafi
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (744.303 KB) | DOI: 10.11591/ijece.v9i3.pp1987-1996

Abstract

This paper focused on studying an algorithm of earth fault location in the medium voltage distribution network. In power system network, most of the earth fault occurs is a single line to ground fault. A medium voltage distribution network with resistance earthing at the main substation and an earth fault attached along the distribution network is modeled in ATP Draw. The generated earth fault is simulated, and the voltage and current signal produced is recorded. The earth fault location algorithm is simulated and tested in MATLAB. The accuracy of the earth fault location algorithm is tested at several locations and fault resistances. A possible correction technique is explained to minimize the error. The results show an improvement fault location distance estimation with minimum error.
Ride through testing of variable speed drive due to voltage sag types (types I, II and III) Surya Hardi; R. Harahap; S. Ahmad; M. Isa
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 10, No 2: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (363.796 KB) | DOI: 10.11591/ijpeds.v10.i2.pp690-696

Abstract

Variable speed drives (VSDs) are widely used in various applications mainly in process industry need constant rotational speed. It is developed from power electronic components thus saving energy in its operation. Unfortunately it is susceptible against power quality problem for example voltage sags. The VSD may be disruption or drop out when it is supplied by voltage sags and it is determined by sag characteristics. This study is to investigate effect of voltage sags Types I, II and III on VSD through laboratory testing. The voltage sags characteristics are generated from voltage sag generator (Shaffner 2100 EMC).  The effects are presented in susceptibility curves in disruption and drop out conditions. The curves resulted are evaluated by standard curve recommended. Test results show that voltage sag Type I cause the VSD disruption only, whereas two types sag other result in the VSD disruption and also drop out. Evaluation results explain  a few test points are in operation area for disruption condition whereas test points for dropping out far below the threshold recommended. Hence the VSD has good quality to voltage sags.
A smart partial discharge classification SOM with optimized statistical transformation feature Z. H. Bohari; M. Isa; A. Z. Abdullah; P. J. Soh; M. F. Sulaima
Bulletin of Electrical Engineering and Informatics Vol 10, No 2: April 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i2.2751

Abstract

Condition-based monitoring (CBM) has been a vital engineering method to assess high voltage (HV) equipment and power cables conditions or health levels. One of the effective CBM methods is partial discharge (PD) measurement or detection. PD event is the phenomenon that always associated with insulation healthiness. PD has been measured and evaluated in this paper to discriminate PD signals from a good signal. A mixed-signal being fed at an AI technique with statistical modified input data to do fast classification (less than five seconds) with nearly zero error. In this paper, an unsupervised neural network is applied for PD classification. The methods combine the self-organizing maps (SOMs) and feature statistical transformation. By the combination of these methods, the ‘range’ normalization method produced the best classification outcomes. This development decided that PD information was effectively correlated and grouped by means of MATLAB’s SOM Toolbox and transformation device to discriminate the normal signal from the PD signal.
Co-Authors A. Z. Abdullah H. Hanafi Hardi, Surya M. F. Sulaima M. R. Adzman N. S. B. Jamili P. J. Soh R. Harahap S. Ahmad S. M. Zali S. R. A. Rahim Z. H. Bohari