Abstract
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.
Highlights
Insulation breakdown amounts to most power systems equipment failure
The results show that the maximal overlap discrete wavelet transform (MODWT)-based algorithm is superior in denoising partial discharges (PD)
In order to judge the performance of the MODWT, both input and output signal-tonoise ratios (SNR) were calculated for each contaminated signal using (6) and (7)
Summary
Insulation breakdown amounts to most power systems equipment failure. In high voltage power cables, the failure rate due to insulation breakdown increases to 83% [1]. The EBWT is chosen to be compared to the MODWT, as it is considered a very wide form of DWT and applies most of the improved denoising methods to the signal (i.e., empirical Bayes, false discovery rate, minimax estimation, Stein’s unbiased risk estimate, block James–Stein, and universal threshold rule). It handles most of the thresholding rules (i.e., soft thresholding, hard thresholding, mean, and median), which are varied repeatedly until the best output SNR is reached.
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