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Comparison of Clustering Techniques of Multiple Partial Discharge Sources in High Voltage Transformer Windings

Comparison of Clustering Techniques of Multiple Partial Discharge Sources in High Voltage Transformer Windings
Comparison of Clustering Techniques of Multiple Partial Discharge Sources in High Voltage Transformer Windings
Modern high voltage (HV) insulation systems consist of multiple dielectric media - multi-source partial discharge (PD) data discrimination is required. The ability to accurately distinguish between the PD signals generated from different sources is seen as a critical function of future diagnostic systems. Two model PD sources were utilized in this investigation to replicate void and surface charges. The proposed processing technique relies on the assumption that the PD pulses generated from different sources exhibit unique waveform characteristics. Several clustering techniques have been employed to identify and separate multiple PD sources recently. However, for further analysis, the techniques used must produce a significant separation between the clustered data as the phase resolved patterns produced by multiple PD sources overlap - inhibiting automatic classification. An experiment has been designed to activate a pair of PD sources and inject the signals simultaneously into an HV transformer winding. After the PD pulses were extracted from the measurement data recorded using two wideband radio frequency current transformers (RCFTs) positioned at the neutral to earth point and the bushing tap-point to earth. Principle Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) were applied to the Wavelet Energy (WE) data and their performance at discriminating between the two PD sources assessed.
clustering, multiple partial discharge, transformer winding
978-1-4799-8903-4
Nik Ali, N H
91f9aa04-0cd9-4d62-896f-97584753886d
Goldsmith, W
3c697784-c5fd-46da-bcce-4be7d6e6123e
Hunter, J A
01b60e7a-55eb-4609-a26c-0701c39f5f08
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Nik Ali, N H
91f9aa04-0cd9-4d62-896f-97584753886d
Goldsmith, W
3c697784-c5fd-46da-bcce-4be7d6e6123e
Hunter, J A
01b60e7a-55eb-4609-a26c-0701c39f5f08
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b

Nik Ali, N H, Goldsmith, W, Hunter, J A, Lewin, P L and Rapisarda, P (2015) Comparison of Clustering Techniques of Multiple Partial Discharge Sources in High Voltage Transformer Windings. 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials, Australia. 19 - 22 Jul 2015. 4 pp .

Record type: Conference or Workshop Item (Paper)

Abstract

Modern high voltage (HV) insulation systems consist of multiple dielectric media - multi-source partial discharge (PD) data discrimination is required. The ability to accurately distinguish between the PD signals generated from different sources is seen as a critical function of future diagnostic systems. Two model PD sources were utilized in this investigation to replicate void and surface charges. The proposed processing technique relies on the assumption that the PD pulses generated from different sources exhibit unique waveform characteristics. Several clustering techniques have been employed to identify and separate multiple PD sources recently. However, for further analysis, the techniques used must produce a significant separation between the clustered data as the phase resolved patterns produced by multiple PD sources overlap - inhibiting automatic classification. An experiment has been designed to activate a pair of PD sources and inject the signals simultaneously into an HV transformer winding. After the PD pulses were extracted from the measurement data recorded using two wideband radio frequency current transformers (RCFTs) positioned at the neutral to earth point and the bushing tap-point to earth. Principle Component Analysis (PCA) and t-Distributed Stochastic Neighbour Embedding (t-SNE) were applied to the Wavelet Energy (WE) data and their performance at discriminating between the two PD sources assessed.

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Published date: 19 July 2015
Venue - Dates: 2015 IEEE 11th International Conference on the Properties and Applications of Dielectric Materials, Australia, 2015-07-19 - 2015-07-22
Keywords: clustering, multiple partial discharge, transformer winding
Organisations: EEE

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Local EPrints ID: 380138
URI: http://eprints.soton.ac.uk/id/eprint/380138
ISBN: 978-1-4799-8903-4
PURE UUID: f3d0734f-4b98-4c33-a352-9ec3cdffc8b5

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Date deposited: 06 Aug 2015 16:20
Last modified: 19 Jul 2019 20:37

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Contributors

Author: N H Nik Ali
Author: W Goldsmith
Author: J A Hunter
Author: P L Lewin
Author: P Rapisarda

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