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...
Saved in:
| 主要作者: | |
|---|---|
| 格式: | Thesis |
| 出版: |
2005
|
| 主題: | |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 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 |