An investigative study on the influence of correlation of PD statistical features on PD pattern recognition

Abduallhi Abubaker Mas'ud, Brian G. Stewart

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

1 Citation (Scopus)
12 Downloads (Pure)

Abstract

This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.
Original languageEnglish
Title of host publication2nd IEEE International Conference on Dielectrics (ICD) 2018
Place of PublicationPiscataway, N.J.
PublisherIEEE
Number of pages5
DOIs
Publication statusPublished - 1 Nov 2018
Event2nd IEEE International Conference on Dielectrics - Budapest, Hungary
Duration: 1 Jul 20185 Jul 2018
Conference number: 2nd

Conference

Conference2nd IEEE International Conference on Dielectrics
Abbreviated titleICD 2018
CountryHungary
CityBudapest
Period1/07/185/07/18

Fingerprint

Partial discharges
Pattern recognition
Neural networks

Keywords

  • partial discharge
  • ensemble neural network
  • correlation coefficients

Cite this

Mas'ud, A. A., & Stewart, B. G. (2018). An investigative study on the influence of correlation of PD statistical features on PD pattern recognition. In 2nd IEEE International Conference on Dielectrics (ICD) 2018 Piscataway, N.J.: IEEE. https://doi.org/10.1109/ICD.2018.8514755
Mas'ud, Abduallhi Abubaker ; Stewart, Brian G. / An investigative study on the influence of correlation of PD statistical features on PD pattern recognition. 2nd IEEE International Conference on Dielectrics (ICD) 2018. Piscataway, N.J. : IEEE, 2018.
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abstract = "This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.",
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Mas'ud, AA & Stewart, BG 2018, An investigative study on the influence of correlation of PD statistical features on PD pattern recognition. in 2nd IEEE International Conference on Dielectrics (ICD) 2018. IEEE, Piscataway, N.J., 2nd IEEE International Conference on Dielectrics , Budapest, Hungary, 1/07/18. https://doi.org/10.1109/ICD.2018.8514755

An investigative study on the influence of correlation of PD statistical features on PD pattern recognition. / Mas'ud, Abduallhi Abubaker ; Stewart, Brian G.

2nd IEEE International Conference on Dielectrics (ICD) 2018. Piscataway, N.J. : IEEE, 2018.

Research output: Chapter in Book/Report/Conference proceedingConference contribution book

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N2 - This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.

AB - This paper investigates the influence of correlation coefficients of partial discharge (PD) statistical fingerprints on the classification performance of the ensemble neural network (ENN). PD measurements were carried out according to the IEC 60270 standard. Independent statistical parameters of skewness, kurtosis, cross-correlation, discharge factor and modified crosscorrelation were analyzed and utilized as inputs to the ENN. The ENN was applied to classify 2 PD datasets. One with PD statistical features and the other a combination of PD statistical features and their correlation coefficients. The results indicate that the ENN appears to show a statistically better performance using the statistical features mixed with their correlation coefficients as compared to the other dataset. This clearly shows that the correlation coefficients of statistical features can provide an improved classification and discrimination of PD patterns.

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Mas'ud AA, Stewart BG. An investigative study on the influence of correlation of PD statistical features on PD pattern recognition. In 2nd IEEE International Conference on Dielectrics (ICD) 2018. Piscataway, N.J.: IEEE. 2018 https://doi.org/10.1109/ICD.2018.8514755