Detection and Recognition of Power Quality Disturbances using Lifting Wavelet Transform

Author(s):  
Edwin S. Jose ◽  
Titus S. Sigamony
2013 ◽  
Vol 378 ◽  
pp. 335-339
Author(s):  
Xin Liang Yin ◽  
Gui Tang Wang ◽  
Zhi Wen Feng ◽  
Xiong Hui Lai

Voltage sag belongs to a kind of transient power quality problems, it possesses short mutations, non-stationary characteristics and the detection on the interference is difficult. Wavelet transform is a better signal analysis method and it is very suitable for analysis of mutations in the signal. Compared with traditional wavelet transform algorithm, Wavelet transform algorithm does not depend on the ascension of the Fourier transform and it reduces the computation complexity, so it is very suitable for hardware implementation. This paper introduces a design based on 9/7 of lifting wavelet transform of the voltage sag detection algorithm, and realized in the FPGA and modeling the transient power quality signal in the Matlab. To make hardware implementation easier, a series of optimization to the coefficient of ascension of 9/7 of lifting wavelet transform was carried out. The results show that 9/7 of the lifting wavelet transform algorithm can effectively test the end time of the voltage sag happens.


2015 ◽  
Vol 10 (11) ◽  
pp. 1127
Author(s):  
Nidaa Hasan Abbas ◽  
Sharifah Mumtazah Syed Ahmad ◽  
Wan Azizun Wan Adnan ◽  
Abed Rahman Bin Ramli ◽  
Sajida Parveen

Author(s):  
Mourad Talbi ◽  
Med Salim Bouhlel

Background: In this paper, we propose a secure image watermarking technique which is applied to grayscale and color images. It consists in applying the SVD (Singular Value Decomposition) in the Lifting Wavelet Transform domain for embedding a speech image (the watermark) into the host image. Methods: It also uses signature in the embedding and extraction steps. Its performance is justified by the computation of PSNR (Pick Signal to Noise Ratio), SSIM (Structural Similarity), SNR (Signal to Noise Ratio), SegSNR (Segmental SNR) and PESQ (Perceptual Evaluation Speech Quality). Results: The PSNR and SSIM are used for evaluating the perceptual quality of the watermarked image compared to the original image. The SNR, SegSNR and PESQ are used for evaluating the perceptual quality of the reconstructed or extracted speech signal compared to the original speech signal. Conclusion: The Results obtained from computation of PSNR, SSIM, SNR, SegSNR and PESQ show the performance of the proposed technique.


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