Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

Chan, Jacob Yuichung; Ma, Hui; Saha, Tapan Kumar; Ekanayake, Chandima

doi:10.1109/tdei.2014.6740752

Cited by 57 publications

(44 citation statements)

References 23 publications

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“…The sieving control of EMD is usually realized by limiting standard deviation [8][9][10] .However, the selection of sieving thresholds has great influence on the results. …”

Section: The Basic Theory and Improvement Of Eemdmentioning

confidence: 99%

“…Cohen's class distribution can be seen as the distribution of signal energy in time domain and frequency domain with a clear physical meaning, and it can take into account the panorama and localized characteristics of time domain and frequency domain except the problem of cross-term interference [4][5][6][7] . Empirical Mode Decomposition (EMD) is a key step of Hilbert-Huang Transform [8][9][10] .It has the advantage of fully adaptive, but the algorithm itself has the problem of modal aliasing and end effect .Wu. Z et al [11] proposed Ensemble Empirical Mode Decomposition (EEMD) based on EMD by adding Gaussian white noise auxiliary.…”

Section: Introductionmentioning

confidence: 99%

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Xu¹,

Yang²,

Shiyang³

et al. 2015

Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015

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Abstract. The feature extraction and pattern recognition of partial discharge signal are key steps of equipment condition assessment and fault diagnosis. Time-frequency analysis on PD pulse can make up for deficiencies of traditional phase statistical method by extracting more comprehensive and effective information from waveform. Cohen's class distribution is a commonly used time-frequency analysis method except for influence of cross interference terms. This paper presents a method of joint time-frequency analysis on PD pulse signal based on EEMD and Cohen's class. The end effect of EEMD is studied and an extending technology based on SVR-regression fitting method is proposed as well. The exponential attenuation oscillating function added with Gaussian white noise and narrow band interference is used to simulate the high frequency current PD signal of power equipment. The results show that this method can accurately identify the characteristic PD pulse. This method can not only guarantee the time-frequency concentration of effective signal, but also inhibit the influence of IMFs' cross interference terms. Finally, we prove the effectiveness and practicality of this method by applying it on PD signal field measured in substation.

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“…The sieving control of EMD is usually realized by limiting standard deviation [8][9][10] .However, the selection of sieving thresholds has great influence on the results. …”

Section: The Basic Theory and Improvement Of Eemdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Untitled

Xu¹,

Yang²,

Shiyang³

et al. 2015

Proceedings of the 3rd International Conference on Advances in Energy and Environmental Science 2015

View full text Add to dashboard Cite

show abstract

“…The basis of the EMD is evaluation of local oscillations in the signal. This method studies the trend of variations in the signal between two sequential local extrema so that the details and features of original signal are extracted [18,19].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

Bahador¹,

Namdari²,

Matinfar³

2017

PIER B

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Abstract-One of the faults in medium voltage (MV) overhead power line is a high impedance fault (HIF) owing to low-current discharge to a tree (THIF). This type of fault generates signals in wide frequency bandwidth which may lead to electromagnetic interference (EMI) with neighboring devices and consequently results in degradation in the performance of nearby systems. This problem becomes more critical when MV power lines path is located in a wooded area in which there will be frequent transient conflicts between trees and power lines especially in the windy conditions. Given the importance of this issue, the ability of THIF to generate EMI is first demonstrated in this paper. Thereafter, a hybrid technique based on combination of quantile regression (QR) and empirical mode decomposition (EMD) is proposed to perform a feature extraction from THIF signals. By comparing the QR results of different samples of THIF signal with other similar signals, the validation of proposed method is depicted. In summary, the original contributions of current research include 1) assessing EMI due to THIFs, 2) using EMD in pre-processing of THIFs signals and extracting their main components, 3) recommending QR for the feature definition of THIF.

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“…To address the above issues in applying WT and EMD to PD signal de-noising, an ensemble EMD (EEMD)-based de-noising method is proposed in this chapter [128]. …”

Section: Field Pd Measurement Setupmentioning

confidence: 99%

“…The purpose of using MM in this chapter is for defining adaptive threshold values in positive and negative sides of a signal by the signal itself [128]. The theory of MM is based on mathematical operators, which are applied between signals and structure elements.…”

Section: Mathematical Morphology (Mm) For Thresholdingmentioning

confidence: 99%

Advanced signal processing techniques for online power transformer insulation diagnosis

Chan¹

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Power transformer is one of the most important assets in an electric utility. However, a large number of existing power transformers worldwide have already approached or even exceeded their designed lifetimes. Any failure of a transformer can be disastrous. Therefore, the conditions of transformers need to be continuously monitored and evaluated. Since a transformer's condition is largely dependent on its insulation system, a number of diagnostic methods have been developed for assessing transformer insulation conditions over the past decades. Among these methods, partial discharge (PD) measurement is widely adopted due to its capability of providing continuously online monitoring and diagnosis of a transformer without disturbing its normal operation.PD is a rather complicated phenomenon and stochastic in nature. Properly performing online PD measurements of a transformer, effectively analysing the measured PD signals, and subsequently making an informed condition assessment on a transformer's insulation system are still challenging.This thesis is aimed at developing advanced signal processing techniques for online PD monitoring and diagnosis of power transformer insulation systems.PD signals acquired at substation environments are always coupled with extensive noise, which exhibits different distribution properties. Therefore, PD signal de-noising is an essential process for accurately extracting PD signals from the acquired noise-corrupted signals before further analysis. In this thesis, advanced signal processing techniques, such as wavelet transform (WT), empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), blind equalization (BE) and pre-whitening, have been investigated for removing discrete spectral interference (DSI) that exhibits sinusoidal behaviour at various frequencies. Mathematical morphology (MM) has also been investigated for suppressing white noise by adaptively selecting threshold values. To remove stochastic noise, fractal dimension and entropy analyses have been investigated. Based on these techniques, several adaptive PD signal de-noising methods have been proposed in this thesis for removing different types of noise. A number of case studies using PD signals acquired from both laboratory experiments and online PD measurements of field transformers are presented. These case studies demonstrate advantages of the proposed PD signal de-noising methods over conventional methods in PD signal de-noising.In this thesis, phase-resolved pulse sequence (PRPS) diagrams and kurtograms have also been proposed for consistent representations of PD patterns after the noise has been removed. Case studies have been provided to prove a PRPS diagram's capability for accurately and consistently ii representing PD patterns. This representation can minimize influences of different types of PD sensors and measurement systems on PD pattern construction. Results are presented to demonstrate that kurtograms can be used to represent PD patterns even in the presence of extensive w...

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Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding

Abstract: ABSTRACT

Cited by 57 publications

References 23 publications

Untitled

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Feature Extraction of Tree-Related High Impedance Faults as a Source of Electromagnetic Interference Around Medium Voltage Power Lines' Corridors

Advanced signal processing techniques for online power transformer insulation diagnosis

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