CLASSIFICATION OF POWER QUALITY DISTURBANCE WITH NEURAL PATTERN RECOGNITION TECHNIQUE
The proposed work presents a novel approach using Discrete Wavelet Transform (DWT) and Neural Pattern Recognition (NPR) technique for the detection and classification of the Power Quality (PD) disturbances. Various PQ related events were simulated including single and combined events and the generated signals were treated with DWT for feature extraction. For classification purpose the signal parameters were trained with Neural Pattern Recognition (NPR) tool. Eleven types of PQ disturbances were considered for classification. The simulation results depicted that the combined process of DWT and NPR can effectively detect and classify different PQ disturbances effectively. Compared to the conventional methods available on the literature this method needs less computations and works faster.
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