Feature-oriented de-noising of partial discharge signals employing mathematical morphology filters

Ashtiani, Mohsen Bakhshi; Shahrtash, S. Mohammad

doi:10.1109/tdei.2012.6396973

Cited by 34 publications

(16 citation statements)

References 21 publications

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“…However, this idea and its accuracy fade as the noise intensity increases. To have a successful approach, the following set of indices, named as current Nth order accumulation (CNA), is 3, 5, 7, 9, 11 (3) where only if N (the order of CNA) is chosen equal to 2, the sign of each current sample shall be multiplied by its magnitude squared (introduced by Ashtiani and Shahrtash [13] and named as differential energy). Each of these indices are later examined in determining PD pulse starting time, PD pulse ending time and apparent charges corresponding to that PD pulse.…”

Section: Feature Generation Stagementioning

confidence: 99%

Online partial discharge signal conditioning for φ – q – n representation under noisy condition in cable systems

Shahsavarian

Shahrtash

2015

IET Science, Measurement & Technology

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Almost all on-site measured partial discharge (PD) signals in cable networks suffer from the contributions of reflected PD pulses and noise which both decrease precision of measurement. This study explores an online digital signal conditioning procedure for PD signals being measured under noisy conditions in cable networks, that is, proposing a novel procedure for online preparing clean j-q-n representation of PD signals with neither directly applying de-noising methods and nor requiring voltage waveform (for calculation of j). In order to demonstrate the accuracy of the results, they are compared with noise-free conditions, where successes of the proposed method have been proved.

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Section: Feature Generation Stagementioning

confidence: 99%

Online partial discharge signal conditioning for φ – q – n representation under noisy condition in cable systems

Shahsavarian

Shahrtash

2015

IET Science, Measurement & Technology

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“…Wideband noises, sometimes called background noises, have a stochastic nature and depend on the measuring system as more sensitive measuring systems are more prone to wideband noise [3,5]. To remove such noise, digital signal processing algorithms-including mathematical morphology [6,7], empirical mode decomposition [8,9], and wavelet transform [2,10]-can be exploited. Mathematical morphology is a time-domain and effective algorithm with a low computational burden but the determination of the type and length of the structure element has always been a challenge [6].…”

Section: Introductionmentioning

confidence: 99%

“…To remove such noise, digital signal processing algorithms-including mathematical morphology [6,7], empirical mode decomposition [8,9], and wavelet transform [2,10]-can be exploited. Mathematical morphology is a time-domain and effective algorithm with a low computational burden but the determination of the type and length of the structure element has always been a challenge [6]. Empirical mode decomposition is a time-domain algorithm that is recently proposed for white noise suppression.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Soltani

El-Hag

2019

Energies

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One of the most promising techniques for condition monitoring of high voltage equipment insulation is partial discharge (PD) measurement using radio frequency (RF) antenna. Nevertheless, the accuracy of monitoring, classification, localization, or lifetime estimation could be negatively affected due to the interferences and noises measured simultaneously and contaminate the RF signals. Therefore, to achieve high accuracy of PD assessment, exploiting the denoising algorithms is inevitable. Hence, this paper seeks to introduce a new technique to suppress white noise, the most prevalent type of noise, especially for RF signals. In the proposed method, the ability of artificial neural network (ANN) in curve fitting is applied to denoising of different types of measured RF signals emitted from PD sources including ‘crack’, ‘internal void’, in the insulator discs and ‘sharp points’ from external hardware. The processes of denoising for named signals with the proposed method are carried out, and the obtained results are compared with the outputs of a wavelet transform-based method named energy conversation-based thresholding. In all tested signals, the proposed technique showed superior denoising capability.

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“…Mathematical Morphology (MM) [1][2][3] is a typical non-linear signal processing method developed in recent years. The theory was founded in the mid-1960s by Matheron and Serra, mathematicians at the Paris School of Mines.…”

Section: Introductionmentioning

confidence: 99%

Application of Mathematical Morphology in GIS PD Signal Analysis

Liu¹,

Gao²

2016

Proceedings of the 2016 International Conference on Energy, Power and Electrical Engineering

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Abstract-In order to solve the problem that with the interference of on-site GIS to PD signals, the type diagnosis and fault location of insulation defect are affected. This paper proposed a new morphological filtering algorithm based on morphological filtering theory is proposed, which combines the basic operations of erosion, dilation, morphological open and morphological close and constructed a new combined morphological filter algorithm for GIS signal de-noising. Through filtering the simulated noised PD pulse, the results showed that morphological filtering can not only remove white noise effectively, but also suppress the narrowband interference effectively. Three sets of evaluation indexes are introduced and compared with the wavelet de-noising method, and the effectiveness of morphological filtering in suppressing noise and extracting PD pulse is verified.

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Feature-oriented de-noising of partial discharge signals employing mathematical morphology filters

Cited by 34 publications

References 21 publications

Online partial discharge signal conditioning for φ – q – n representation under noisy condition in cable systems

Online partial discharge signal conditioning for φ – q – n representation under noisy condition in cable systems

Denoising of Radio Frequency Partial Discharge Signals Using Artificial Neural Network

Application of Mathematical Morphology in GIS PD Signal Analysis

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