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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| Format: | Thesis |
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2005
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| Summary: | 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. |
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