Separation of Corona Using Wavelet Packet Transform and Neural Network for Detection of Partial Discharge in Gas-Insulated Substations

Jin, Jaehyeok; Chang, Chin Sung; Hoshino, T.; Hanai, Masahiro; Kobayashi, N.

doi:10.1109/tpwrd.2004.839187

Cited by 67 publications

(46 citation statements)

References 8 publications

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“…Chang et al presented a separation of corona from the PD signal by wavelet packet transform and a neural network method. The parameters including node energy, kurtosis, and skewness were calculated and used for characterizing PD signal and corona [16].…”

Section: Introductionmentioning

confidence: 99%

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Wang

Kil

et al. 2017

Energies

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With the increasing demand for precise condition monitoring and diagnosis of gas-insulated switchgears (GISs), it has become a challenge to improve the detection sensitivity of partial discharge (PD) induced in the GIS spacer. This paper deals with the elimination of the capacitive component from the phase-resolved partial discharge (PRPD) signal generated in GIS spacers based on discrete wavelet transform (WT). Three types of typical insulation defects were simulated using PD cells. The single PD pulses were detected and were further used to determine the optimal mother wavelet. As a result, the bior6.8 was selected to decompose the PD signal into 8 levels and the signal energy at each level was calculated. The decomposed components related with capacitive disturbance were discarded, whereas those associated with PD were de-noised by a threshold and a thresholding function. Finally, the PRPD signals were reconstructed using the de-noised components.

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Section: Introductionmentioning

confidence: 99%

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Wang

Kil

et al. 2017

Energies

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“…To preserve both time and frequency information in PD signal representation, WT has been adopted [70,184]. It decomposes a signal into different coefficients embedding different frequency components (refer to Section 4.2.1).…”

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

Section: Time-frequency (Tf) Sparsity Map On Pd Source Separationmentioning

confidence: 99%

“…Thus, it is also used for dimension reduction to ease the subsequent clustering or pattern identification of PD sources [177]. However, frequency representation of PD pulses may not be sufficient for multiple PD source separation since transient characteristics of PD pulses in time domain are neglected.To preserve both time and frequency information in PD signal representation, WT has been adopted [70,184]. It decomposes a signal into different coefficients embedding different frequency components (refer to Section 4.2.1).…”

mentioning

confidence: 99%

“…WT may be able to reveal intrinsic components of PD pulses in the coefficients. Therefore, energy values and/or statistical parameters derived from these coefficients have been used to represent PD pulses for multiple PD source separation [70,184].Partial Discharge Source Separation and Classification 113 7.2.1.2. TF map A TF map by means of equivalent time length and bandwidth of a PD pulse is a widely adopted approach for separating multiple PD sources [51][52][53][54].…”

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confidence: 99%

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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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Multiscale statistical process control using wavelet packets

2008

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in Wiley InterScience (www.interscience.wiley.com).An approach is presented for conducting multiscale statistical process control (MSSPC), based on a library of basis functions provided by wavelet packets. The proposed approach explores the improved ability of wavelet packets in extracting features with arbitrary locations, and having different localizations in the time-frequency domain, in order to improve the detection performances achieved with wavelet-based MSSPC. A novel approach is also developed for adaptively selecting the best decomposition depth. Such an approach is described in detail and tested using artificial simulated signals, employed to compare average run length (ARL) performance against other SPC methodologies. Furthermore, its performance under real world situations is also assessed, for two industrial case studies using datasets containing process upsets, through the construction of receiver operating characteristic (ROC) curves. Both univariate and multivariate cases are covered. ARL results for a step perturbation show that the proposed methodology presents a steady good performance for all shift magnitudes, without significantly changing its relative scores, as happens with other current methods, whose relative performance depends on the shift magnitude being tested. For artificial disturbances, with features localized in the time/frequency domain, multiscale methods do present the best performance, and for the particular case of detecting a decrease in autocorrelation they are the only ones that can detect such a perturbation. In the examples using industrial datasets, where disturbances exhibit more complex patterns, multiscale approaches also present the best results, in particular in the range of low false alarms, where monitoring methods are aimed to operate.

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Separation of Corona Using Wavelet Packet Transform and Neural Network for Detection of Partial Discharge in Gas-Insulated Substations

Cited by 67 publications

References 8 publications

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Disturbance Elimination for Partial Discharge Detection in the Spacer of Gas-Insulated Switchgears

Advanced signal processing techniques for online power transformer insulation diagnosis

Multiscale statistical process control using wavelet packets

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