Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis

This thesis presents a new approach aimed at automating the analysis of power quality disturbances. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network a...

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Main Author:	Choong, Florence Chiao Mei
Format:	Thesis
Published:	2005
Subjects:	TK Electrical engineering Electronics Nuclear engineering
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id	my-mmu-ep.849
record_format	uketd_dc
spelling	my-mmu-ep.8492010-07-06T04:14:41Z Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis 2005-11 Choong, Florence Chiao Mei TK Electrical engineering. Electronics Nuclear engineering This thesis presents a new approach aimed at automating the analysis of power quality disturbances. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. As there exists uncertainty in the training set and in the subsequent pattern recognition, fuzzy logic is used to determine the final output rather than taking the output of the neural network as the final classification, improving robustness in the system. The disturbances of interest include sag, swell, transient, fluctuation, interruption and normal waveform. Each power quality disturbance has unique deviations from the pure sinusoidal wave form and this is adopted to provide a reliable classification of disturbance. 2005-11 Thesis http://shdl.mmu.edu.my/849/ http://myto.perpun.net.my/metoalogin/logina.php masters Multimedia University Research Library
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Choong, Florence Chiao Mei Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
description	This thesis presents a new approach aimed at automating the analysis of power quality disturbances. The approach focuses on the application of discrete wavelet transform technique to extract features from disturbance waveforms and their classification using a powerful combination of neural network and fuzzy logic. As there exists uncertainty in the training set and in the subsequent pattern recognition, fuzzy logic is used to determine the final output rather than taking the output of the neural network as the final classification, improving robustness in the system. The disturbances of interest include sag, swell, transient, fluctuation, interruption and normal waveform. Each power quality disturbance has unique deviations from the pure sinusoidal wave form and this is adopted to provide a reliable classification of disturbance.
format	Thesis
qualification_level	Master's degree
author	Choong, Florence Chiao Mei
author_facet	Choong, Florence Chiao Mei
author_sort	Choong, Florence Chiao Mei
title	Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_short	Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_full	Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_fullStr	Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_full_unstemmed	Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_sort	hardware realization of fuzzy wavelets neural network to power quality analysis
granting_institution	Multimedia University
granting_department	Research Library
publishDate	2005
_version_	1747829231152267264

Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis

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