Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib

In electrical system, there are various power quality disturbances such as harmonic, voltage sag, voltage swell, and transient which may lead to power quality problems and will give adverse effects to the electrical supply performance. In this project, the wavelet transform based artificial neural n...

Full description

Saved in:

Bibliographic Details
Main Author:	Mohamad Taib, Mohamad Hafiz
Format:	Thesis
Language:	English
Published:	2015
Subjects:	Neural networks (Computer science) Electric power failures
Online Access:	https://ir.uitm.edu.my/id/eprint/67094/2/67094.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-uitm-ir.67094
record_format	uketd_dc
spelling	my-uitm-ir.670942023-01-05T04:08:26Z Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib 2015 Mohamad Taib, Mohamad Hafiz Neural networks (Computer science) Electric power failures In electrical system, there are various power quality disturbances such as harmonic, voltage sag, voltage swell, and transient which may lead to power quality problems and will give adverse effects to the electrical supply performance. In this project, the wavelet transform based artificial neural network classifier is implemented under two types signal disturbances namely transient distortion and voltage sag to improve the classification process of the signal. The feature extraction is done by using wavelet transform and the details are collected and given to the neural network. The fifth order of Daubechies wavelet with the decomposition at level four is implemented in order to get optimum wavelet decomposition. The features from wavelet transform analysis will be an input to the neural network and the output will be two types of disturbances namely voltage sag and transient. The accuracy level and the advantage of integrating the wavelet transform with neural network will be discussed in this project by analyzing the performance measures such as accuracy, precision, sensitivity and confusion matrix specificity. The rate of classification is perfectly with 100% accuracy for two types of power quality disturbance signals. 2015 Thesis https://ir.uitm.edu.my/id/eprint/67094/ https://ir.uitm.edu.my/id/eprint/67094/2/67094.pdf text en public degree Universiti Teknologi MARA (UiTM) Faculty of Electrical Engineering Hamzah, Noraliza
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Hamzah, Noraliza
topic	Neural networks (Computer science) Electric power failures
spellingShingle	Neural networks (Computer science) Electric power failures Mohamad Taib, Mohamad Hafiz Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
description	In electrical system, there are various power quality disturbances such as harmonic, voltage sag, voltage swell, and transient which may lead to power quality problems and will give adverse effects to the electrical supply performance. In this project, the wavelet transform based artificial neural network classifier is implemented under two types signal disturbances namely transient distortion and voltage sag to improve the classification process of the signal. The feature extraction is done by using wavelet transform and the details are collected and given to the neural network. The fifth order of Daubechies wavelet with the decomposition at level four is implemented in order to get optimum wavelet decomposition. The features from wavelet transform analysis will be an input to the neural network and the output will be two types of disturbances namely voltage sag and transient. The accuracy level and the advantage of integrating the wavelet transform with neural network will be discussed in this project by analyzing the performance measures such as accuracy, precision, sensitivity and confusion matrix specificity. The rate of classification is perfectly with 100% accuracy for two types of power quality disturbance signals.
format	Thesis
qualification_level	Bachelor degree
author	Mohamad Taib, Mohamad Hafiz
author_facet	Mohamad Taib, Mohamad Hafiz
author_sort	Mohamad Taib, Mohamad Hafiz
title	Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
title_short	Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
title_full	Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
title_fullStr	Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
title_full_unstemmed	Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib
title_sort	classification of power quality disturbances using wavelet transform based artificial neural network / mohamad hafiz mohamad taib
granting_institution	Universiti Teknologi MARA (UiTM)
granting_department	Faculty of Electrical Engineering
publishDate	2015
url	https://ir.uitm.edu.my/id/eprint/67094/2/67094.pdf
_version_	1783735660780191744

Classification of power quality disturbances using wavelet transform based artificial neural network / Mohamad Hafiz Mohamad Taib

Similar Items