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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS) Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Nur Ashida Salim
Universiti Teknologi MARA
Control & Systems Engineering Electrical & Electronics Engineering

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Implementation of graphical user interface to observe and examine the frequency and rotor angle stability of a power system due to small disturbances Nur Ashida Salim; Mohamad Salehan Ab. Samah; Hasmaini Mohamad; Zuhaila Mat Yasin; Nur Fadilah Ab Aziz
Indonesian Journal of Electrical Engineering and Computer Science Vol 17, No 2: February 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v17.i2.pp606-614

Abstract

The aim of this research is to anticipate the stability status of a power system when the system is exposed to a change in frequency and rotor angle due to small disturbances. The proposed study was implemented on the IEEE Reliability Test System 1979 (IEEE RTS-79) which contains 24 buses, 38 transmission lines and 32 generators. Steady state stability limit of a system refers to the maximum amount of power that is permissible through the system without loss of its steady state stability. This research proposes the development of a Graphical User Interface (GUI) to observe the frequency and rotor angle stability due to the effect of small disturbances using the One Machine Infinite Bus (OMIB) technique. This proposed technique could ease the power system utility especially the power system operation to observe and examine the system frequency and rotor angle stability due to small disturbances. The findings from this research has proven that the proposed technique to observe the frequency and rotor angle stability due to small disturbances has successfully been developed using a GUI.
Design of a Small Renewable Resource Model based on the Stirling Engine with Alpha and Beta Configurations Faisal Zahari; Muhammad Murtadha Othman; Ismail Musirin; Amirul Asyraf Mohd Kamaruzaman; Nur Ashida Salim; Bibi Norasiqin Sheikh Rahimullah
Indonesian Journal of Electrical Engineering and Computer Science Vol 8, No 2: November 2017
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v8.i2.pp360-367

Abstract

This paper presents the conceptual design of Stirling engine based Alpha and Beta configurations. The performances of Stirling engine based Beta configuration will be expounded elaborately in the discussion. The Stirling engines are durable in its operation that requires less maintenance cost.  The methodology for both configurations consists of thermodynamic formulation of Stirling Cycle, Schmidt theory and few composition of flywheel and Ross-Yoke dimension. Customarily, the Stirling engine based Beta configuration will operate during the occurrence of low and high temperature differences emanating from any type of waste heat energy. A straightforward analysis on the performance of Stirling engine based Beta configuration has been performed corresponding to the temperature variation of cooling agent. The results have shown that the temperature variation of cooling agent has a direct effect on the performances of Stirling engine in terms of its speed, voltage and output power. 
Fault classification in smart distribution network using support vector machine Ong Wei Chuan; Nur Fadilah Ab Aziz; Zuhaila Mat Yasin; Nur Ashida Salim; Norfishah A. Wahab
Indonesian Journal of Electrical Engineering and Computer Science Vol 18, No 3: June 2020
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v18.i3.pp1148-1155

Abstract

Machine learning application have been widely used in various sector as part of reducing work load and creating an automated decision making tool. This has gain the interest of power industries and utilities to apply machine learning as part of the operation. Fault identification and classification based machine learning application in power industries have gain significant accreditation due to its great capability and performance. In this paper, a machine-learning algorithm known as Support Vector Machine (SVM) for fault type classification in distribution system has been developed. Eleven different types of faults are generated with respect to actual network. A wide range of simulation condition in terms of different fault impedance value as well as fault types are considered in training and testing data. Right setting parameters are important to learning results and generalization ability of SVM. Gaussian radial basis function (RBF) kernel function has been used for training of SVM to accomplish the most optimized classifier. Initial finding from simulation result indicates that the proposed method is quick in learning and shows good accuracy values on faults type classification in distribution system. The developed algorithm is tested on IEEE 34 bus and IEEE 123 bus test distribution system. 
Co-Authors Aainaa Mohd Arriffin Amirul Asyraf Mohd Kamaruzaman Arvind Raj Bibi Norasiqin Sheikh Rahimullah Faisal Zahari Hasmaini Mohamad Hasmaini Mohamad Hazlie Mokhlis Ismail Musirin Izni Nadhirah Sam’ón Kanendra Naidu Mohamad Salehan Ab. Samah Mohd Danish Irfan Mohd Sufian Muhammad Murtadha Othman Nofri Yenita Dahlan Norfishah A. Wahab Norfishah Ab Wahab Norziana Aminudin Nur Azzammudin Rahmat Nur Fadilah Ab Aziz Ong Wei Chuan Shahrani Shahbudin Syed Mohamad Hisyam Wan Dawi W Muhammad Faizol bin W Mustapha Wan Iqmal Faezy Wan Zalnidzam Wan Iqmal Faezy Wan Zalnidzham Zuhaila Mat Yasin Zuhaila Mat Yasin Zulkiffli Abdul Hamid