PD Pattern Recognition Using ANFIS

Chalashkanov, N. M.; Kolev, Nikolai; Dodd, S.J.; Fothergill, J.C.

doi:10.1109/ceidp.2008.4772865

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…Correlations between them were determined, which have to be taken into account when PD pattern recognition is considered. The importance in identifying and removing the redundancy in the input feature vector for reliable PD recognition was demonstrated in [10].…”

Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Partial Discharges from Electrical Trees Grown in a Flexible Epoxy Resin

Dodd

Chalashkanov

Fothergill

2008

2008 Annual Report Conference on Electrical Insulation and Dielectric Phenomena

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Abstract-Electrical treeing is a long-term degradation mechanism in polymeric insulation, which can lead to electrical failure of HV insulation systems. The rate at which trees grow across the insulation depends on the PD activity occurring within them and hence the detection of the onset of electrical treeing could be established by PD monitoring. In this paper, a statistical analysis of the partial discharges detected during the growth of trees in an epoxy resin will be reported. The aim of this work was to provide additional insight into the physical mechanisms that lead to the observed fluctuations in the partial discharge activity. The results demonstrate interesting correlations between a number of statistical parameters, such as average discharge magnitude and standard deviation in the partial discharge amplitudes. These correlations could also be related to physical parameters such as the applied voltage magnitude and the measured power dissipation due to the partial discharges occurring during tree growth. The implications of this work for deterministic methodologies for the simulation of tree growth as well as for condition monitoring using feature recognition strategies for the early detection of tree growth will be discussed. Permanent repository link

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Section: Introductionmentioning

confidence: 99%

Statistical Analysis of Partial Discharges from Electrical Trees Grown in a Flexible Epoxy Resin

Dodd

Chalashkanov

Fothergill

2008

2008 Annual Report Conference on Electrical Insulation and Dielectric Phenomena

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“…Pattern recognition using 15 statistical parameters have been developed as ANFIS input comprising a discharge fingerprint to discriminate between internal PD pulses [27]. II.…”

Section: Annmentioning

confidence: 99%

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

Halim

MohdRazaly

Rohani

et al. 2023

ARASET

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According to partial discharge (PD) damage in the electrodes that are not entirely bridging, the presence of PD in the high voltage (HV) power cable might lead to insulation failure. PD defects can damage cross-linked polyethylene (XLPE) cables directly, which is one of the most critical electrical issues in the industry. Poor workmanship during cable jointing, aging, or exposure to the surrounding environment is the most common cause of PD in HV cable systems. As a result, the location of the PD signals that occur cannot be classified without identifying the multiple PD signals present in the cable system. In this study, the artificial neural network (ANN) based feedforward back propagation classification technique is used as a diagnostic tool thru MATLAB software in which the PD signal was approached to determine the accuracy of the location PD signal. In addition, statistical feature extraction was added to compare the accuracy of classification with the standard method. The three-point technique is also an approach used to locate PD signals in a single line 11 kV XLPE underground power cable. The results show that the statistical feature extraction had been successful classify the PD signal location with the accuracy of 80% compared to without statistical feature extraction. The distance between PD signals and the PD source affected the result of the three-point technique which proved that a lower error means a near distance between them.

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“…Lastly, all output signals from the previous layers are summarised by a fixed node in the fifth layer [68]. Examples of the rules: Rule 1: If x is A1 and y is B1, then f 1 = p 1 x + q 1 y + r 1 Rule 2: If x is A2 and y is B2, then f 2 = p 2 x + q 2 y + r 2 Chalashkanov et al [87] developed pattern recognition by using 15 statistical parameters for ANFIS input comprising a discharge fingerprint to discriminate between internal PD pulses. The number of input features becomes six after the discriminant analysis.…”

Section: Adaptive Neuro-fuzzy Inference Systemmentioning

confidence: 99%

Partial Discharges Classification Methods in XLPE Cable: A Review

et al. 2021

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Partial discharge (PD) signal classification analysis on cross-linked polyethylene (XLPE) cables is complex, requiring a comprehensive understanding of the characteristics of PD patterns. In the realm of high-voltage electrical insulation, PD pattern characteristics, such as PD charge and inception voltage, are essential as assessment criteria in diagnostics systems using PD classifiers. This paper provides a review of various PD patterns and classifiers used by previous researchers, specifically for XLPE cables. In addition, the differences of the research on various sensor development based on PD detection in the past 27 years are also discussed. The repeatability, recognition accuracy, recognition speed, and effect of feature sizes on each PD classification method are reviewed and explained. The review indicates that the pattern recognition for PD signal using artificial neural network (ANN) exhibits better performance in terms of accuracy and repeatability than the other methods, and the reduction of feature size does not affect the accuracy of ANN.INDEX TERMS Partial discharge (PD), cross-linked polyethylene (XLPE) cable, solid insulator, pattern recognition, feature extraction, artificial neural network (ANN)

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PD Pattern Recognition Using ANFIS

Cited by 7 publications

References 19 publications

Statistical Analysis of Partial Discharges from Electrical Trees Grown in a Flexible Epoxy Resin

Statistical Analysis of Partial Discharges from Electrical Trees Grown in a Flexible Epoxy Resin

Multiple Partial Discharge Signal Classification Using Artificial Neural Network Technique in XLPE Power Cable

Partial Discharges Classification Methods in XLPE Cable: A Review

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