Abstract
In the wavelet threshold denoising of power signal, the selection of wavelet has an important influence on the denoising effect, and the wavelet generating function has diversity, if not selected properly, it will directly lead to the failure of denoising. Firstly, an operator is introduced to modify the threshold of each scale to better reflect the variation of wavelet coefficients of signal and noise with scale. Then a controllable threshold function is proposed to adapt to different soft and hard characteristics, and it is used to denoise the wavelet coefficients. Based on the study of the characteristics of wavelet, such as orthogonality, vanishing moment, support length and symmetry, four principles of wavelet selection in power signal denoising are proposed. The voltage sag and harmonics model are established, and db5, coif1 and sym2 wavelets are selected to decompose the signal to the fourth scale for denoising. The signal-to-noise ratio, mean square error and the detailed features of the reconstructed signal after denoising are compared. The experimental results show that the orthogonal wavelet db5 with high vanishing moment order and long support length has better denoising effect than coif1 and sym2, which proves the correctness of the wavelet selection principles proposed in this paper.
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