{'fraction_non_alphanumeric': 0.024539877300613498, 'fraction_numerical': 0.0015337423312883436, 'mean_word_length': 4.9363636363636365, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Wavelet denoising of partial discharge images

This paper presents application of wavelet based de-noising to partial discharge (PD) phase resolved images. Simulations are discussed in first part while in the second one results obtained during on-line measurements on a 6 kV motor. The basic principles of wavelet de-noising analysis, as well as examples of hard- and soft de-noising thresholding are reported. For the purposes of decomposition, the Deabuchies wavelet and wavelet packets at different levels were applied. The efficiency of the enhancement was measured by using a mean square signal-to-noise ratio and standard deviation to mean ratio.