An improved threshold estimation technique for partial discharge signal denoising using Wavelet Transform

Vigneshwaran, B.; Maheswari, R. V.; Subburaj, P.

doi:10.1109/iccpct.2013.6528823

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…The pulse amplitudes are usually in the range of micro-to milli-Volts, which makes them very difficult to detect under noisy conditions [23,24]. The Figures 4 and 5 show the partial discharge meter and experimental setup of PD measurements.…”

Section: Measurement Of Pd Signalmentioning

confidence: 98%

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Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Velayutham

Perumal

Basharan

et al. 2014

Electric Power Components and Systems

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Section: Measurement Of Pd Signalmentioning

confidence: 98%

“…The damped exponential pulse (DEP) and damped oscillation pulse (DOP) are simulated for PD signal [23,24] using the Eqs. (1) and (2), which are shown in Figures 1 and 2.…”

Section: Simulated Pd Signalsmentioning

confidence: 99%

Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Velayutham

Perumal

Basharan

et al. 2014

Electric Power Components and Systems

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“…The result of these two transforms represents the projection of a signal based on wavelets for the WT. The FT shows an extreme efficiency in the analysis of periodic phenomena, time-invariant and stationary technique whereas WT is screening all components produced by transients, variables time and non-stationary [5].…”

Section: Numerical Processingmentioning

confidence: 99%

Numerical signal processing methods for partial discharge detection in more electrical aircraft

Abadie

Billard²,

Lebey

2016

2016 IEEE International Conference on Dielectrics (ICD)

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In forecoming "more electrical aircrafts", aircrafts will become more compact and lighter. Hydraulic, pneumatic or mechanical systems will be replaced by electrical ones thus leading to an increased demand of electric power. The network voltage is expected to increase far above Paschen's minimum, thus increasing the risk of partial discharges. Furthermore the use of inverter drives will be generalized and PD detection on square voltage powered equipments will become a mandatory challenge. The purpose of this study is to propose a method to denoise partial discharge signals embedded in the electromagnetic noise induced by switchings. This denoising is performed using the wavelet decomposition

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“…A method based on Singular Value Decomposition has been used for PD signal de-noising (Abdel-Galil et al, 2008), which, in some special cases, might be ineffective without knowing the number of selected singular values. Madhu et al propose to abate the noise of simulated PD signals using optimal wavelet threshold method (Madhu et al, 2015), and an improved threshold estimation technique (Vigneshwaran et al, 2013) based on wavelet transform has been introduced to filter noise; however, they are hardly to set a unified threshold for different complex mother wavelet at the same time. Hussein et al put forward an idea, named Power Spectral Subtraction, of using fast Fourier transform to restrain the random noise encountered in measured acoustic PD signals (Hussein et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform

Zhang

Sun

et al. 2018

COMPEL

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Purpose Noise abatement is one of the key techniques for Partial Discharge (PD) on-line measurement and monitoring. However, how to enhance the efficiency of PD signal noise suppression is a challenging work. Hence, this study aims to improve the efficiency of PD signal noise abatement. Design/methodology/approach In this approach, the time–frequency characteristics of PD signal had been obtained based on fast kurtogram and S-transform time–frequency spectrum, and these characteristics were used to optimize the parameters for the signal matching over-complete dictionary. Subsequently, a self-adaptive selection of matching atoms was realized when using Matching Pursuit (MP) to analyze PD signals, which leading to seldom noise signal element was represented in sparse decomposition. Findings The de-noising of PD signals was achieved efficiently. Simulation and experimental results show that the proposed method has good adaptability and significant noise abatement effect compared with Empirical Mode Decomposition, Wavelet Threshold and global signal sparse decomposition of MP. Originality/value A self-adaptive noise abatement method was proposed to improve the efficiency of PD signal noise suppression based on the signal sparse representation and its MP algorithm, which is significant to on-line PD measurement.

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An improved threshold estimation technique for partial discharge signal denoising using Wavelet Transform

Cited by 16 publications

References 11 publications

Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Support Vector Machine-based Denoising Technique for Removal of White Noise in Partial Discharge Signal

Numerical signal processing methods for partial discharge detection in more electrical aircraft

Partial discharge signal self-adaptive sparse decomposition noise abatement based on spectral kurtosis and S-transform

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