Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin

Power Quality poses an important issue in power system industry since three decades ago. Numerous researches relating in solving the power quality disturbances have been actively undertaken by researches both in academia and industry. This project focuses on one of the many important issues, which i...

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Main Authors:	Hamzah, Noraliza, Zakaria, Zuhaina, Shahbudin, Shahrani
Format:	Research Reports
Language:	English
Published:	Research Management Institute (RMI) 2010
Online Access:	http://ir.uitm.edu.my/id/eprint/17286/ http://ir.uitm.edu.my/id/eprint/17286/2/LP_NORALIZA%20HAMZAH%20RMI%2010_5.pdf

id	uitm-17286
recordtype	eprints
spelling	uitm-172862019-11-18T02:13:48Z http://ir.uitm.edu.my/id/eprint/17286/ Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin Hamzah, Noraliza Zakaria, Zuhaina Shahbudin, Shahrani Power Quality poses an important issue in power system industry since three decades ago. Numerous researches relating in solving the power quality disturbances have been actively undertaken by researches both in academia and industry. This project focuses on one of the many important issues, which is to classify the cause of the power quality problem. Two major disturbances are considered, voltage sag and transient which constitutes major power quality problems worldwide. Both of these problems are caused by mother nature and the equipment in power industry. The proposed technique employed in this project to classify the causes of voltage sag and transient is by using a statistical method, Support Vector Machine. A wavelet technique based on the Daubechies mother wavelet has been used as the feature extraction method. Each type of disturbances is extracted using the chosen dB mother wavelet. Sampling data are taken from the simulated IEEE 13 and 30 bus distribution data. The data are simulated using PSCAD and the SVM employed to classify the cause of each disturbance is written using Matlab script code. The results show that, the wavelet technique can be used to detect voltage sag and transient accurately. The support vector machine has been proven to be able to classify the cause of transient and voltage sag with 80% and 93% accuracy respectively. Research Management Institute (RMI) 2010 Research Reports NonPeerReviewed text en http://ir.uitm.edu.my/id/eprint/17286/2/LP_NORALIZA%20HAMZAH%20RMI%2010_5.pdf Hamzah, Noraliza and Zakaria, Zuhaina and Shahbudin, Shahrani (2010) Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin. [Research Reports] (Unpublished)
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Teknologi MARA
building	UiTM Institutional Repository
collection	Online Access
language	English
description	Power Quality poses an important issue in power system industry since three decades ago. Numerous researches relating in solving the power quality disturbances have been actively undertaken by researches both in academia and industry. This project focuses on one of the many important issues, which is to classify the cause of the power quality problem. Two major disturbances are considered, voltage sag and transient which constitutes major power quality problems worldwide. Both of these problems are caused by mother nature and the equipment in power industry. The proposed technique employed in this project to classify the causes of voltage sag and transient is by using a statistical method, Support Vector Machine. A wavelet technique based on the Daubechies mother wavelet has been used as the feature extraction method. Each type of disturbances is extracted using the chosen dB mother wavelet. Sampling data are taken from the simulated IEEE 13 and 30 bus distribution data. The data are simulated using PSCAD and the SVM employed to classify the cause of each disturbance is written using Matlab script code. The results show that, the wavelet technique can be used to detect voltage sag and transient accurately. The support vector machine has been proven to be able to classify the cause of transient and voltage sag with 80% and 93% accuracy respectively.
format	Research Reports
author	Hamzah, Noraliza Zakaria, Zuhaina Shahbudin, Shahrani
spellingShingle	Hamzah, Noraliza Zakaria, Zuhaina Shahbudin, Shahrani Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
author_facet	Hamzah, Noraliza Zakaria, Zuhaina Shahbudin, Shahrani
author_sort	Hamzah, Noraliza
title	Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
title_short	Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
title_full	Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
title_fullStr	Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
title_full_unstemmed	Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin
title_sort	novel techniques for intelligent power quality diagnosis system / noraliza hamzah , zuhaina zakaria and shahrani shahbudin
publisher	Research Management Institute (RMI)
publishDate	2010
url	http://ir.uitm.edu.my/id/eprint/17286/ http://ir.uitm.edu.my/id/eprint/17286/2/LP_NORALIZA%20HAMZAH%20RMI%2010_5.pdf
first_indexed	2023-09-18T22:57:58Z
last_indexed	2023-09-18T22:57:58Z
_version_	1777417981490364416

Novel techniques for intelligent power quality diagnosis system / Noraliza Hamzah , Zuhaina Zakaria and Shahrani Shahbudin

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