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Discrimination of multiple PD sources using wavelet decomposition and principal component analysis

Discrimination of multiple PD sources using wavelet decomposition and principal component analysis
Discrimination of multiple PD sources using wavelet decomposition and principal component analysis
Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.
1702-1711
Hao, L.
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P.L.
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Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Swaffield, D.J.
d5828393-2cfb-4f1b-ace4-cd44e0ee5542
Contin, A.
662771fd-a33a-49c9-882a-38285278f2c0
Walton, C
8316028f-b52f-44e3-90d8-6747f8e5a256
Michel, M.
560e6263-d080-4126-827a-8ad1b36764d4
Hao, L.
e6006548-3fc1-4a7e-9df4-a4e9a9a05c45
Lewin, P.L.
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Hunter, J.A.
dae3e13b-a97e-4e81-a617-20ab6965da3c
Swaffield, D.J.
d5828393-2cfb-4f1b-ace4-cd44e0ee5542
Contin, A.
662771fd-a33a-49c9-882a-38285278f2c0
Walton, C
8316028f-b52f-44e3-90d8-6747f8e5a256
Michel, M.
560e6263-d080-4126-827a-8ad1b36764d4

Hao, L., Lewin, P.L., Hunter, J.A., Swaffield, D.J., Contin, A., Walton, C and Michel, M. (2011) Discrimination of multiple PD sources using wavelet decomposition and principal component analysis. IEEE Transactions on Dielectrics and Electrical Insulation, 18 (5), 1702-1711. (doi:10.1109/TDEI.2011.6032842).

Record type: Article

Abstract

Partial discharge (PD) signals generated within electrical power equipment can be used to assess the condition of the insulation. In practice, testing often results in multiple PD sources. In order to assess the impact of individual PD sources, signals must first be discriminated from one another. This paper presents a procedure for the identification of PD signals generated by multiple sources. Starting with the assumption that different PD sources will display unique signal profiles this will be manifested in the distribution of energies with respect to frequency and time. Therefore the technique presented is based on the comparison of signal energies associated with particular wavelet decomposition levels. Principal component analysis is adopted to reduce the dimensionality of the data, whilst minimizing lost information in the data concentration step. Physical parameters are extracted from individual PD pulses and projected into 3-dimensional space to allow clustering of data from specific PD sources. The density-based spatial clustering of applications with noise (DBSCAN) clustering algorithm is chosen for its ability to discover clusters of arbitrary shape in n-dimension space. PD data from individual clusters can then be further analyzed by projecting the clustered data with respect to the original phase relationship. Results and analysis of the technique are compared for experimentally measured PD data from a range of sources commonly found in three different types of high voltage (HV) equipment; ac synchronous generators, induction motors and distribution cables. These experiments collect data using varied test arrangements including sensors with different bandwidths to demonstrate the robustness and indicate the potential for wide applicability of the technique to PD analysis for a range of insulation systems.

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Published date: October 2011
Organisations: EEE

Identifiers

Local EPrints ID: 272923
URI: http://eprints.soton.ac.uk/id/eprint/272923
PURE UUID: 8be4e1fe-4888-4cd5-aa9d-d8afbbc96b1f

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Date deposited: 12 Oct 2011 16:40
Last modified: 20 Nov 2021 11:17

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Contributors

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

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