Recognition of partial discharge waveshape patterns for gas insulated switchgear

Lee, K.H.; Chang, Chin Sung

doi:10.1080/1448837x.2005.11464128

Cited by 6 publications

(5 citation statements)

References 7 publications

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“…It is performed by linearly mapping the pre-selection signal samples into the value range of [-1,1]. It is based on each pre-selection waveform to make sure that the distribution of the pre-selection signal portion is dependent on the waveshape only and is thus ready for the extraction of the discriminative feature [4].…”

Section: Resultsmentioning

confidence: 99%

“…However, the typical time-resolved parameters are not sufficient to reveal the multi-peak or oscillation feature of PD current pulse waveform, which shows the harmful leader discharge leading to breakdown from streamer discharge [3]. The Sampling Counting Ratio (SCR) technique [4] is arranged to reveal the oscillation characteristics of PD pulse in this paper. SCR quantifies the pre-selected and normalized PD signal waveforms to 16(4x4) partition as shown in Fig.…”

Section: Resultsmentioning

confidence: 99%

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Measurements and Classifications of PD Pulse on HDPE Specimens during Degradation

Zhang

2011

AMR

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Discharge may take place in high voltage apparatuses in the form of void discharge, corona discharge and surface discharge. Understanding of the discharge is very important to know the condition of the insulation. Partial discharge (PD) current pulse waveforms contain essentially all the available information concerning the PD generation and thus the available information concerning the physical mechanism of degradation and breakdown of insulation. In this paper, three types of discharge model for polyethelene insulation material are constructed and the PD current pulse waveforms are extracted from experiments. In order to obtain pulse waveform characteristics, the extracted pulse waveforms are pre-selected and normalized, and then a Sampling Counting Ratio (SCR) classification technique is investigated to reveal the multi-peak and oscillation information about the PD current pulse waveforms. Results show that the classification would enable us to estimate the progress and degree of the degradation.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Measurements and Classifications of PD Pulse on HDPE Specimens during Degradation

Zhang

2011

AMR

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“…In [6][7][8][9] it was presented that using wavelet or wavelet packet decomposition extraction of energy features and the classification results were satisfactory, while the methods mentioned in these papers focused on selecting energy features among the leaf nodes in WPD tree. In this paper, according to the analysis of WPD of EM signals, it is found that, if the level of decomposition is not deep enough, there would not be effective parameters for classification.…”

Section: Features Selection For Em Signalsmentioning

confidence: 99%

“…Wavelet or wavelet packet decomposition (WPD) are always treated as the common feature selection method in classification of EM signals. Some of the previous research adopted the information in different frequency band as classification features, which can be got from each tree node in WPD tree, including energy features, fractal dimension features, etc [6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Applying WPD and SVD to Classification of EM Wave Induced by Partial Discharge in Power Transformer

Zhao¹,

Cheng²,

Yu³

et al. 2013

Journal of Electrical Engineering

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Partial discharge (PD) current is an impulse signal at nanosecond level, which can generate electromagnetic (EM) wave containing broadband frequency information. The frequency band of EM signal is from MHz up to GHz. Due to different PD patterns, impulse currents with different shapes induce different EM waves containing different frequency information. Therefore, using the features extracted from frequency domain of EM signals, the classification of PD patterns can be effectively got. It is good to use wavelet or wavelet packet decomposition to select features. However, if the decomposition level is too shallow to find enough effective features, it cannot group the EM signals to the right pattern. On the contrary, although it is easier to find features to distinguish the PD pattern if the decomposition level is deep, there will be a lot of redundancy variables and it is hard to select features among so many variables. In this paper, a method is presented, which selected features in the whole decomposition tree instead of selecting among the leaf node of the tree, because more potential features can be found in the whole tree. With the present method, it is possible not only to get enough features, but also to eliminate the redundancy variables effectively. In order to validate the method, large EM signals from four PD patterns in a power transformer are acquired as the training data and testing data for feature selection and classification, and three common classification methods are introduced to classify the PD patterns using the features selected by the method. Most of the classification results are satisfactory indicating that the proposed method is effective.

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“…Using the theory of wavelet singularity detection as a powerful signal processing tool, the initial arrival of the voltage traveling wave at both terminals of the power cable and the second arrival of the traveling wave at the first terminal can be identified reliably without the need for the sign detection of these waves. Chang C.S [7] presents early warning system based on the detection of UHF SF6 partial discharge signals is a necessary means for the protection of Gas-Insulated Switchgear (GIS) in service as well as the power system to which it is connected. In order to ensure the safe and reliable operation of GIS, it is important to adopt an effective diagnosing method, which is able to identify signals of harmful defects promptly.…”

Section: Introductionmentioning

confidence: 99%

Operating state monitoring and fault diagnosis on traction power supply system

Fan

2008

2008 3rd IEEE Conference on Industrial Electronics and Applications

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How to extract fault feature and divide pattern space is very important for the fault diagnosis. The traveling wave signal characteristic of over-head contact system is analyzed on the base of electromagnetic field theory; the eigenvector is constructed by the wavelet packet energy spectrum. The tests prove that the method in which the traveling wave feature is extracted in terms of wavelet packet energy spectrum is very available. The arithmetic of the pattern recognition is simplified using the method and the accuracy of the fault diagnosis is improved obviously.

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Recognition of partial discharge waveshape patterns for gas insulated switchgear

Cited by 6 publications

References 7 publications

Measurements and Classifications of PD Pulse on HDPE Specimens during Degradation

Measurements and Classifications of PD Pulse on HDPE Specimens during Degradation

Applying WPD and SVD to Classification of EM Wave Induced by Partial Discharge in Power Transformer

Operating state monitoring and fault diagnosis on traction power supply system

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