scholarly journals Denoising of Heavily Contaminated Partial Discharge signals in High-Voltage Cables Using Maximal Overlap Discrete Wavelet Transform

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6540
Author(s):  
Mohammed A. Shams ◽  
Hussein I. Anis ◽  
Mohammed El-Shahat
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
High Voltage ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Gaussian White Noise ◽  
Discrete Wavelet ◽  
Power Cable ◽  
Power Cables ◽  
Algorithm Performance

Online detection of partial discharges (PD) is imperative for condition monitoring of high voltage equipment as well as power cables. However, heavily contaminated sites often burden the signals with various types of noise that can be challenging to remove (denoise). This paper proposes an algorithm based on the maximal overlap discrete wavelet transform (MODWT) to denoise PD signals originating from defects in power cables contaminated with various levels of noises. The three most common noise types, namely, Gaussian white noise (GWN), discrete spectral interference (DSI), and stochastic pulse shaped interference (SPI) are considered. The algorithm is applied to an experimentally acquired void-produced partial discharge in a power cable. The MODWT-based algorithm achieved a good improvement in the signal-to-noise ratio (SNR) and in the normalized correlation coefficient (NCC) for the three types of noises. The MODWT-based algorithm performance was also compared to that of the empirical Bayesian wavelet transform (EBWT) algorithm, in which the former showed superior results in denoising SPI and DSI, as well as comparable results in denoising GWN. Finally, the algorithm performance was tested on a PD signal contaminated with the three type of noises simultaneously in which the results were also superior.

A method to capture and de-noise partial discharge pulses using discrete wavelet transform and ANFIS

2014 ◽  
Vol 25 (11) ◽  
pp. 2696-2712 ◽  
Author(s):  
Hamid Jahangir ◽  
Ehsan Hajipour ◽  
Mehdi Vakilian ◽  
Asghar Akbari ◽  
Trevor Blackburn ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Partial Discharge ◽  
Discrete Wavelet

scholarly journals An Image Steganography Algorithm using Fractional Discrete Wavelet Transform with Advanced Encryption System

2020 ◽  
Vol 9 (5) ◽  
pp. 541-546
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Mean Square Error ◽  
Signal To Noise Ratio ◽  
Image Steganography ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Signal To Noise ◽  
Noise Ratio

The research constitutes a distinctive technique of steganography of image. The procedure used for the study is Fractional Random Wavelet Transform (FRWT). The contrast between wavelet transform and the aforementioned FRWT is that it comprises of all the benefits and features of the wavelet transform but with additional highlights like randomness and partial fractional value put up into it. As a consequence of the fractional value and the randomness, the algorithm will give power and a rise in the surveillance layers for steganography. The stegano image will be acquired after administrating the algorithm which contains not only the coated image but also the concealed image. Despite the overlapping of two images, any diminution in the grade of the image is not perceived. Through this steganographic process, we endeavor for expansion in surveillance and magnitude as well. After running the algorithm, various variables like Mean Square Error (MSE) and Peak Signal to Noise ratio (PSNR) are deliberated. Through the intended algorithm, a rise in the power and imperceptibility is perceived and it can also support diverse modification such as scaling, translation and rotation with algorithms which previously prevailed. The irrefutable outcome demonstrated that the algorithm which is being suggested is indeed efficacious.

scholarly journals MODELLING OF PARTIAL DISCHARGE ANALYSIS SYSTEM USING WAVELET TRANSFORM DENOISING TECHNIQUE IN LABVIEW ENVIRONMENT

2019 ◽  
Vol 81 (6) ◽  
Author(s):  
A. Nazifah Abdullah ◽  
S. H. K. Hamadi ◽  
M. Isa ◽  
B. Ismail ◽  
A. N. Nanyan ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Virtual Instrument ◽  
Signal To Noise Ratio ◽  
Partial Discharge ◽  
Discrete Wavelet ◽  
Mean Square ◽  
Mother Wavelet ◽  
Signal To Noise ◽  
Labview Software ◽  
Analysis System

Partial discharge (PD) measurement is an essential to detect and diagnose the existence of the PD. However, this measurement has faced noise disturbance in industrial environments. Thus, PD analysis system using discrete wavelet transform (DWT) denoising technique via Laboratory Virtual Instrument Engineering Workbench (LabVIEW) software is proposed to distinguish noise from the measured PD signal. In this work, the performance of denoising process is analyzed based on calculated mean square error (MSE) and signal to noise ratio (SNR). The result is manipulated based on Haar, Daubechies, Coiflets, Symlets and Biorthogonal type of mother wavelet with different decomposition levels. From the SNR results, all types of the mother wavelet are suitable to be used in denoising technique since the value of SNR is in large positive value. Therefore, further studies were conducted and found out that db14, coif3, sym5 and bior5.5 wavelets with least MSE value are considered good to be used in the denoising technique. However, bior5.5 wavelet is proposed as the most optimum mother wavelet due to consistency of producing minimum value of MSE and followed by db14.

Auto-detection of partial discharges in power cables by discrete wavelet transform

2005 ◽  
Vol 152 (1) ◽  
pp. 24-30
Author(s):  
Yoh Yasuda ◽  
Takehisa Hara ◽  
Koji Urano ◽  
Min Chen
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Partial Discharges ◽  
Discrete Wavelet ◽  
Power Cables

scholarly journals Analysis of the Quantization Noise in Discrete Wavelet Transform Filters for Image Processing

Electronics ◽  
2018 ◽  
Vol 7 (8) ◽  
pp. 135 ◽  
Author(s):  
Nikolay Chervyakov ◽  
Pavel Lyakhov ◽  
Dmitry Kaplun ◽  
Denis Butusov ◽  
Nikolay Nagornov
Keyword(s):  
Image Processing ◽  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Integrated Circuit ◽  
Filter Banks ◽  
Signal To Noise Ratio ◽  
Quantization Noise ◽  
Discrete Wavelet ◽  
Field Programmable ◽  
Application Specific Integrated Circuit

In this paper, we analyze the noise quantization effects in coefficients of discrete wavelet transform (DWT) filter banks for image processing. We propose the implementation of the DWT method, making it possible to determine the effective bit-width of the filter banks coefficients at which the quantization noise does not significantly affect the image processing results according to the peak signal-to-noise ratio (PSNR). The dependence between the PSNR of the DWT image quality on the wavelet and the bit-width of the wavelet filter coefficients is analyzed. The formulas for determining the minimal bit-width of the filter coefficients at which the processed image achieves high quality (PSNR ≥ 40 dB) are given. The obtained theoretical results were confirmed through the simulation of DWT for a test image using the calculated bit-width values. All considered algorithms operate with fixed-point numbers, which simplifies their hardware implementation on modern devices: field-programmable gate array (FPGA), application-specific integrated circuit (ASIC), etc.

scholarly journals Fast Compressed Sensing MRI Based on Complex Double-Density Dual-Tree Discrete Wavelet Transform

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Shanshan Chen ◽  
Bensheng Qiu ◽  
Feng Zhao ◽  
Chao Li ◽  
Hongwei Du
Keyword(s):  
Wavelet Transform ◽  
Compressed Sensing ◽  
Discrete Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Discrete Wavelet ◽  
Translation Invariance ◽  
Signal To Noise ◽  
Reconstruction Methods ◽  
Noise Ratio

Compressed sensing (CS) has been applied to accelerate magnetic resonance imaging (MRI) for many years. Due to the lack of translation invariance of the wavelet basis, undersampled MRI reconstruction based on discrete wavelet transform may result in serious artifacts. In this paper, we propose a CS-based reconstruction scheme, which combines complex double-density dual-tree discrete wavelet transform (CDDDT-DWT) with fast iterative shrinkage/soft thresholding algorithm (FISTA) to efficiently reduce such visual artifacts. The CDDDT-DWT has the characteristics of shift invariance, high degree, and a good directional selectivity. In addition, FISTA has an excellent convergence rate, and the design of FISTA is simple. Compared with conventional CS-based reconstruction methods, the experimental results demonstrate that this novel approach achieves higher peak signal-to-noise ratio (PSNR), larger signal-to-noise ratio (SNR), better structural similarity index (SSIM), and lower relative error.

scholarly journals Extraction of Partial Discharge Pulses from the Complex Noisy Signals of Power Cables Based on CEEMDAN and Wavelet Packet

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3242 ◽  
Author(s):  
Sun ◽  
Zhang ◽  
Shi ◽  
Gou
Keyword(s):  
White Noise ◽  
Arrival Time ◽  
Wavelet Packet ◽  
Partial Discharge ◽  
Gaussian White Noise ◽  
Power Cable ◽  
Sampling Point ◽  
Power Cables ◽  
Threshold Method ◽  
Narrowband Noise

While both periodic narrowband noise and white noise are significant sources of interference in the detection and localization of partial discharge (PD) signals in power cables, existing research has focused nearly exclusively on white noise suppression. This paper addresses this issue by proposing a new signal extraction method for effectively detecting random PD signals in power cables subject to complex noise environments involving both white noise and periodic narrowband noise. Firstly, the power cable signal was decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the periodic narrowband noise and frequency aliasing in the obtained signal components were suppressed using singular value decomposition. Then, signal components contributing significantly to the PD signal were determined according to the cross-correlation coefficient between each component and the original PD signal, and the PD signal was reconstructed solely from the obtained significant components. Finally, the wavelet packet threshold method was used to filter out residual white noise in the reconstructed PD signal. The performance of the proposed algorithm was demonstrated by its application to synthesized PD signals with complex noise environments composed of both Gaussian white noise and periodic narrowband noise. In addition, the time-varying kurtosis method was demonstrated to accurately determine the PD signal arrival time when applied to PD signals extracted by the proposed method from synthesized signals in complex noise environments with signal-to-noise ratio (SNR) values as low as −6 dB. When the SNR was reduced to −23 dB, the arrival time error of the PD signal was only one sampling point.

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