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Autonomous Localization of Partial Discharge Sources within Large Transformer Windings

Autonomous Localization of Partial Discharge Sources within Large Transformer Windings
Autonomous Localization of Partial Discharge Sources within Large Transformer Windings
Partial discharge (PD) condition monitoring inside a HV transformer generally and particularly along a transformer winding has become an important research area with the ultimate aim of providing asset health information that enables maintenance and replacement processes to be carried out effectively. As far as PD activity inside transformer windings is concerned, an electrical detection method has been developed based on the use of radio frequency current transducers and subsequent digital signal processing of captured measurement data. A localization approach based on the measurement of currents at the bushing tap point and neutral to earth connection has been developed, with the assumption that different PD source locations will generate unique signal profiles in terms of the distribution of measured current energies with respect to both frequency and time. Therefore the technique presented is based on analysis of measured current energies associated with different frequencies. Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the data,whilst minimizing lost information in the original dataset. This non-linear analysis of captured current data is not practicable for the field but the process can be represented through the use of three finite impulse response filters that have the ability to perform PD source localization automatically and are straightforward to implement in monitoring hardware.
Partial discharges, wavelet transform, signal processing, power transformer insulation system
1088-1098
Abd Rahman, M S
8217f865-63cf-4fa4-9f2b-19589bbdb2f0
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b
Abd Rahman, M S
8217f865-63cf-4fa4-9f2b-19589bbdb2f0
Lewin, P L
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Rapisarda, P
79efc3b0-a7c6-4ca7-a7f8-de5770a4281b

Abd Rahman, M S, Lewin, P L and Rapisarda, P (2016) Autonomous Localization of Partial Discharge Sources within Large Transformer Windings. IEEE Transactions on Dielectrics & Electrical Insulation, 23 (2), 1088-1098. (doi:10.1109/TDEI.2015.005070).

Record type: Article

Abstract

Partial discharge (PD) condition monitoring inside a HV transformer generally and particularly along a transformer winding has become an important research area with the ultimate aim of providing asset health information that enables maintenance and replacement processes to be carried out effectively. As far as PD activity inside transformer windings is concerned, an electrical detection method has been developed based on the use of radio frequency current transducers and subsequent digital signal processing of captured measurement data. A localization approach based on the measurement of currents at the bushing tap point and neutral to earth connection has been developed, with the assumption that different PD source locations will generate unique signal profiles in terms of the distribution of measured current energies with respect to both frequency and time. Therefore the technique presented is based on analysis of measured current energies associated with different frequencies. Principal Component Analysis (PCA) is then applied to reduce the dimensionality of the data,whilst minimizing lost information in the original dataset. This non-linear analysis of captured current data is not practicable for the field but the process can be represented through the use of three finite impulse response filters that have the ability to perform PD source localization automatically and are straightforward to implement in monitoring hardware.

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Accepted/In Press date: 18 November 2015
Published date: 1 April 2016
Keywords: Partial discharges, wavelet transform, signal processing, power transformer insulation system
Organisations: Vision, Learning and Control, EEE

Identifiers

Local EPrints ID: 405083
URI: https://eprints.soton.ac.uk/id/eprint/405083
PURE UUID: 1a549efc-488e-460a-80b8-83ca32c906e7

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Date deposited: 24 Jan 2017 14:43
Last modified: 19 Jul 2019 19:26

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Contributors

Author: M S Abd Rahman
Author: P L Lewin
Author: P Rapisarda

University divisions

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