An improved methodology for application of wavelet transform to partial discharge measurement denoising

Recent research has shown that the wavelet transform (WT) can potentially be used to extract partial discharge (PD) pulses from severe noise. However, the method is more complex than the Fourier transform (FT), and requires expertise and experience to be applied to produce its best effect. The authors have previously published algorithms for selection of the most appropriate mother wavelet and for automatic determination of threshold values for applying the WT to PD measurement denoising. The present paper is to present an improved methodology to apply the discrete wavelet transform (DWT) with better denoising effect to PD measurement. Firstly the paper describes the structure of DWT´s filter pairs. It then analyses the frequency bands of the wavelet coefficients in approximations and details, and energy distribution of a PD signal along each of the levels following the DWT. Finally a DWT-based denoising method is proposed and justified. Results prove that, with the proposed methodology, in conjunction with the algorithms proposed by the authors to select optimal mother wavelet and threshold values, significant improvement in denoising effect can be achieved.