Paper Title
Level Dependent Wavelet Selection For Denoising Of Partial Discharge Signals Simulated By DEP And DOP Models

Wavelet Transform methods are effective for de-noising of partial discharge (PD) signals. Base wavelets are related to distortion of PD signals de-noised by wavelet methods. This paper presents a level dependent wavelet selection scheme for de-noising of PD and called the energy based wavelet selection (EBWS) scheme, because an energy criterion is proposed for the scheme. In the proposed energy criterion, a base wavelet is selected as an optimal base wavelet if it can generate an approximation with the largest energy among all base wavelets for selection at each level. The simulated damped exponential pulse (DEP) and damped oscillatory pulse (DOP) has been used to check the performance of the proposed method. In comparison with the scale independent scheme, the wavelet method, based on the EBWS, generates significantly smaller waveform distortion and magnitude errors in the de-noised PD signals. The results of the tests carried out show clearly that this technique can produce excellent results when applied to simulated PD data.