Level dependent partial discharge signal de-noising using stationary wavelet transform
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Abstract
PD monitoring is an effective tool to evaluate the insulation condition of power electrical equipment. However, the major challenge during PD measurement at site is that PD signals are severely affected by external noises and disturbances like white noise, random noise, Discrete Spectral Interferences (DSI), which are generated due to broadcasting stations, stochastic noise and pulses from power electronics at site conditions. Extracting PD signals from these noises is a challenging task.This paper proposes a new method for selecting the mother wavelet based on the energy of the approximation coefficients. The coefficients are obtained using SWT by decomposing the extracted noisy signal to the maximum decomposition level which depends only on the length of the noisy signal. Hard thresholding is used as the threshold function and range dependent threshold estimator is used for obtaining the threshold value. For reconstruction of de-noised signal, the last level approximation coefficient and the thresholded ‘details coefficient’ are used. As most of the lower level details coefficients comprise of noises it can be discarded during reconstruction. A method for discarding noises during reconstruction is also proposed in this paper.
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How to Cite
Jayakrishnan, M., & Nageshwar Rao, B. (2017). Level dependent partial discharge signal de-noising using stationary wavelet transform. Power Research - A Journal of CPRI, 31–36. Retrieved from https://node6473.myfcloud.com/~geosocin/CPRI/index.php/pr/article/view/133
References
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