Denoising Algorithm For Dual-Tree Complex Wavelet Disturbance Signal With Improved Threshold Function
Shiming Zhang, Ming Zhang, Ziqi Xu, Kai Wang · 2020 Asia Energy and Electrical Engineering Symposium (AEEES) · 2020
At present, the collection and analysis of power quality signals are more and more interfered by noise, but the traditional denoising methods have many shortcomings. Based on the improved thresholding function (ITF), a dual-tree complex wavelet transform (DTCWT) denoising algorithm is proposed in this paper. Firstly, the original power quality disturbance signal is transformed by two discrete wavelet transform parallel to each other, and the wavelet coefficients of the real part and the imaginary part are obtained by sampling at two channels. The approximate translation invariance is guaranteed and the signal distortion is reduced. Then ITF is used to reduce the noise of the corresponding wavelet coefficients, which ensures the continuity at the threshold point and preserves the characteristic information at the mutation. The experimental results of various types of power quality disturbance signals show that compared with the traditional denoising algorithm, the signal-to-noise ratio (SNR) of the proposed denoising algorithm in this paper is further improved and the mean square error (MSE) is further reduced, and the denoising effect is obviously improved.