An investigative study on the influence of correlation of PD statistical features on PD pattern recognition
Mas'ud, Abduallhi Abubaker and Stewart, Brian 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. IEEE, HUN. (https://doi.org/10.1109/ICD.2018.8514755)
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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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Item type: Book Section ID code: 64874 Dates: DateEvent1 November 2018Published28 April 2018AcceptedNotes: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Subjects: Technology > Electrical engineering. Electronics Nuclear engineering Department: Faculty of Engineering > Electronic and Electrical Engineering Depositing user: Pure Administrator Date deposited: 26 Jul 2018 12:02 Last modified: 11 Nov 2024 15:14 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/64874