Abstract
Acquired electrical waveforms can be affected by white noise. The 1-st part of the paper analysis deals with the denoising of multi-period steady signals by using 3 methods: mean signal method, an original method relying on wavelet packet trees and the method implemented by the wavelet-based Matlab function wden. The signal length influence over the mean signal method’s accuracy is studied. The results yielded by the other 2 methods are also analyzed considering signals with 7 periods. Afterward the wavelet-based methods are used to denoise segments of 7 periods with linearly variable magnitudes (ascending or descending) for 3 different slopes. Artificial test signals, with rich harmonic content, were used. They were polluted by sets of 10 white noises with different powers. Maximum absolute deviations and mean square root deviations were computed considering the original signals, before pollution, versus the corresponding denoised signal. The metrics were computed relative to the maximum absolute value of the noise and allowed to determine the most accurate method.
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