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 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.

Improving Space Localization Properties of the Discrete Wavelet Transform

Informatica ◽  
2013 ◽  
Vol 24 (4) ◽  
pp. 657-675
Author(s):  
Jonas Valantinas ◽  
Deividas Kančelkis ◽  
Rokas Valantinas ◽  
Gintarė Viščiūtė
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Space Localization
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