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| his paper develops an approach to detect and classify power quality disturbance waveforms as well the analysis of the corresponding characteristic parameters using a novel combination of d-q conversion, artificial neural networks, the point to point comparison of ideal voltage with disturbed voltage and wavelet transform. From the results of the d-q conversion through the fictitious three-phase voltages, the classification of voltage sags, swell and interruption is realized. For other disturbances, feature extraction is carried out through the analysis of the results of the d-q conversion, and then artificial neural networks are used for the automatic classification. For the classified disturbances, the corresponding characteristic parameters can be obtained through the analysis of the results of the d-q conversion, the point to point comparison of ideal voltage with disturbed voltage and wavelet transform. Simulation results show that the proposed approach has good performance in val |
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| Keywords:power quality disturbance waveforms, automatic classification, d-q conversion, artificial neural network, wavelet transform |
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