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A new method for automatic Multiple Partial Discharge Classification

A new method for automatic Multiple Partial Discharge Classification
A new method for automatic Multiple Partial Discharge Classification
A new wavelet based feature parameter have been developed to represent the characteristics of PD activities, i.e. the wavelet decomposition energy of PD pulses measured from non-conventional ultra wide bandwidth PD sensors such as capacitive couplers (CC) or high frequency current transformers (HFCT). The generated feature vectors can contain different dimensions depending on the length of recorded pulses. These high dimensional feature vectors can then be processed using Principal Component Analysis (PCA) to map the data into a three dimensional space whilst the first three most significant components representing the feature vector are preserved. In the three dimensional mapped space, an automatic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then applied to classify the data cluster(s) produced by the PCA. As the procedure is undertaken in a three dimensional space, the obtained clustering results can be easily assessed. The classified PD sub-data sets are then reconstructed in the time domain as phase-resolved patterns to facilitate PD source type identification. The proposed approach has been successfully applied to PD data measured from electrical machines and power cables where measurements were undertaken in different laboratories.
Hao, L
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Contin, A.
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Hunter, Jack
096a7372-1f46-49b9-88a0-42527088eeab
Swaffield, D.J.
d5828393-2cfb-4f1b-ace4-cd44e0ee5542
Lewin, P.L.
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Walton, C
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Michel, M
f7ee8155-05eb-439f-9015-752de1831459
Hao, L
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Contin, A.
662771fd-a33a-49c9-882a-38285278f2c0
Hunter, Jack
096a7372-1f46-49b9-88a0-42527088eeab
Swaffield, D.J.
d5828393-2cfb-4f1b-ace4-cd44e0ee5542
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Walton, C
8316028f-b52f-44e3-90d8-6747f8e5a256
Michel, M
f7ee8155-05eb-439f-9015-752de1831459

Hao, L, Contin, A., Hunter, Jack, Swaffield, D.J., Lewin, P.L., Walton, C and Michel, M (2011) A new method for automatic Multiple Partial Discharge Classification. 17th International Symposium on High Voltage Engineering, Hannover, Germany. 21 - 25 Aug 2011.

Record type: Conference or Workshop Item (Paper)

Abstract

A new wavelet based feature parameter have been developed to represent the characteristics of PD activities, i.e. the wavelet decomposition energy of PD pulses measured from non-conventional ultra wide bandwidth PD sensors such as capacitive couplers (CC) or high frequency current transformers (HFCT). The generated feature vectors can contain different dimensions depending on the length of recorded pulses. These high dimensional feature vectors can then be processed using Principal Component Analysis (PCA) to map the data into a three dimensional space whilst the first three most significant components representing the feature vector are preserved. In the three dimensional mapped space, an automatic Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is then applied to classify the data cluster(s) produced by the PCA. As the procedure is undertaken in a three dimensional space, the obtained clustering results can be easily assessed. The classified PD sub-data sets are then reconstructed in the time domain as phase-resolved patterns to facilitate PD source type identification. The proposed approach has been successfully applied to PD data measured from electrical machines and power cables where measurements were undertaken in different laboratories.

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More information

Published date: 22 August 2011
Additional Information: CD ROM
Venue - Dates: 17th International Symposium on High Voltage Engineering, Hannover, Germany, 2011-08-21 - 2011-08-25
Organisations: EEE

Identifiers

Local EPrints ID: 272720
URI: http://eprints.soton.ac.uk/id/eprint/272720
PURE UUID: 43d49cc3-0365-461c-9dd0-b89a3c2abbe4

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Date deposited: 25 Aug 2011 07:46
Last modified: 20 Nov 2021 11:12

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Contributors

Author: L Hao
Author: A. Contin
Author: Jack Hunter
Author: D.J. Swaffield
Author: P.L. Lewin
Author: C Walton
Author: M Michel

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