Partial discharge image recognition using a new group of features

Li, Jian; Sun, Caixin; Grzybowski, S.; Taylor, C.D.

doi:10.1109/tdei.2006.258196

Cited by 33 publications

(23 citation statements)

References 14 publications

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“…The PD repetition rate N=44.45 can be calculated by Eqs. (3) and (8), which was consistent with the theoretical analysis. The phase-resolved distributions of the positive and negative half cycles turned out to be asymmetrical.…”

Section: Results and Analysissupporting

confidence: 91%

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Partial Discharge Process and Characteristics of Oil-Paper Insulation under Pulsating DC Voltage

Bao¹,

Li²,

Zhang³

et al. 2016

Journal of Electrical Engineering and Technology

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-Oil-paper insulation of valve-side windings in converter transformers withstand electrical stresses combining with AC, DC and strong harmonic components. This paper presents the physical mechanisms and experimental researches on partial discharge (PD) of oil-paper insulation at pulsating DC voltage. Theoretical analysis showed that the phase-resolved distributions of PDs generated from different insulated models varied as the increase of the applied voltages following a certain rule. Four artificial insulation defect models were designed to generate PD signals at pulsating DC voltages. Theoretical statements and experimental results show that the PD pulses first appear at the maximum value of the applied pulsating DC voltage, and the resolved PD phase distribution became wider as the applied voltage increased. The PD phase-resolved distributions generated from the different discharge models are also different in the phase-resolved distributions and development progress. It implies that the theoretical analysis is suitable for interpretation of PD at pulsating DC voltage.

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Section: Results and Analysissupporting

confidence: 91%

“…Furthermore, some evaluations of PD patterns and oil-paper aging were presented in [6][7]. Researches about pattern recognition for PD under AC voltage were studied in publication [8][9]. PDs of oil-paper insulation under DC voltage were different from that under AC voltage.…”

Section: Introductionmentioning

confidence: 99%

Partial Discharge Process and Characteristics of Oil-Paper Insulation under Pulsating DC Voltage

Bao¹,

Li²,

Zhang³

et al. 2016

Journal of Electrical Engineering and Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…As suggested in [6][7][8][9][10][11][12][13], a partial discharge phenomenon can be characterized in most cases by the partial discharge phase, mean discharge number, mean discharge, mean discharge frequency, and so on, and the commonly seen approaches for defect pattern recognition include Fourier transform, Discrete Fourier Transform, Hilbert-Huang Transform (HHT), etc. Although the embedded characteristics can be extracted directly from partial discharge signals, a large database is required when statistics are performed on the characteristics of interest over a long time span.…”

Section: Open Accessmentioning

confidence: 99%

Chaos Synchronization Error Technique-Based Defect Pattern Recognition for GIS through Partial Discharge Signal Analysis

Chen

Yau

Chen

2014

Entropy

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Abstract:The work is aimed at using the chaos synchronization error dynamics (CSED) technique for defect pattern recognition in gas insulated switchgear (GIS). The radiated electromagnetic waves generated due to internal defects were measured by the self-made ultrahigh frequency (UHF) micro-strip antenna, so as to determine whether partial discharge will occur. Firstly, a data pretreatment is performed on the measured raw data for the purpose of computational burden reduction. A characteristic matrix is then constructed according to dynamic error trajectories in a chaos synchronization system, subsequent to which characteristics are extracted. A comparison with the existing Hilbert-Huang Transform (HHT) method reveals that the two characteristics extracted from the CSED results presented herein using the fractal theory were recognized at a higher rate pattern.

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“…The first is based on the statistical regularities of the discharge on power cycles, such as figures in two or three dimensions [2][3][4][5], while the second is based on the real-time characteristics of PD signals [6]. The former reflects better on PD's physical features.…”

Section: Introductionmentioning

confidence: 99%

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

Tang¹,

Zhuo²,

Wang³

et al. 2016

Journal of Electrical Engineering and Technology

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-With changes in insulated defects, the environment, and so on, new partial discharge (PD) data are highly different from the original samples. It leads to a decrease in on-line recognition rate. The UHF signal and pulse current signal of four kinds of typical artificial defect models in gas insulated switchgear (GIS) are obtained simultaneously by experiment. The relationship map of ultrahigh frequency (UHF) cumulative energy and its corresponding apparent discharge of four kinds of typical artificial defect models are plotted. UHF cumulative energy and its corresponding apparent discharge are used as inputs. The support vector machine (SVM) incremental method is constructed. Examples show that the PD SVM incremental method based on simulated annealing (SA) effectively speeds up the data update rate and improves the adaptability of the classifier compared with the original method, in that the total sample is constituted by the old and new data. The PD SVM incremental method is a better pattern recognition technology for PD on-line monitoring.

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Partial discharge image recognition using a new group of features

Cited by 33 publications

References 14 publications

Partial Discharge Process and Characteristics of Oil-Paper Insulation under Pulsating DC Voltage

Partial Discharge Process and Characteristics of Oil-Paper Insulation under Pulsating DC Voltage

Chaos Synchronization Error Technique-Based Defect Pattern Recognition for GIS through Partial Discharge Signal Analysis

Application of SA-SVM Incremental Algorithm in GIS PD Pattern Recognition

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