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:
Online Access:http://shdl.mmu.edu.my/849/
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author Choong, Florence Chiao Mei
author_facet Choong, Florence Chiao Mei
author_sort Choong, Florence Chiao Mei
building MMU Institutional Repository
collection Online Access
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.
first_indexed 2025-11-14T17:59:36Z
format Thesis
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institution Multimedia University
institution_category Local University
last_indexed 2025-11-14T17:59:36Z
publishDate 2005
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spelling mmu-8492010-07-06T04:14:41Z http://shdl.mmu.edu.my/849/ Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis 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 NonPeerReviewed Choong, Florence Chiao Mei (2005) Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis. Masters thesis, Multimedia University. http://myto.perpun.net.my/metoalogin/logina.php
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Choong, Florence Chiao Mei
Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title 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_short Hardware Realization Of Fuzzy Wavelets Neural Network To Power Quality Analysis
title_sort hardware realization of fuzzy wavelets neural network to power quality analysis
topic TK Electrical engineering. Electronics Nuclear engineering
url http://shdl.mmu.edu.my/849/
http://shdl.mmu.edu.my/849/