Wavelet analysis for classification of multi-source PD patterns

Multi-resolution signal decomposition (MSD) technique of wavelet transforms has interesting properties of capturing the embedded horizontal, vertical and diagonal variations within an image in a separable form. This feature was exploited to identify individual partial discharge (PD) sources present in multi-source PD patterns, usually encountered during practical PD measurements. Employing the Daubechies wavelet, features were extracted from the third level decomposed and reconstructed horizontal and vertical component images. These features were found to contain the necessary discriminating information corresponding to the individual PD sources. Suitability of these extracted features for classification was further verified using a radial basis function neural network (NN). Successful recognition was achieved, even when the constituent sources produced partially and fully overlapping patterns, thus demonstrating the applicability of the proposed novel approach for the task of multi-source PD classification.