Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers

Research output: Contribution to conferencePaper

Abstract

This paper describes the application of cluster analysis and classification techniques for the diagnosis of partial discharge defects present in electrical power transformers. The subsequent implementation of an agent-based, decision support system (DSS) incorporating these intelligent techniques is also discussed. Successful defect classification of empirical partial discharge data, using neural networks and rule induction, affirms the application of these techniques as a suitable means of providing reliable decision support for partial discharge defect diagnosis, particularly where expert diagnostic knowledge may be scarce or ambiguous. Through the interaction of intelligent agents the DSS considers the effectiveness and diagnostic contribution of each agent (intelligent technique) before presenting a consolidated diagnosis.

Conference

Conference12th Intelligent Systems Application to Power Systems (ISAP 2003)
CountryGreece
CityLemnos
Period31/08/033/09/03

Fingerprint

Power transformers
Partial discharges
Intelligent agents
Decision support systems
Defects
Cluster analysis
Neural networks

Keywords

  • intelligent diagnosis
  • defects
  • partial discharge activity
  • power transformers

Cite this

Strachan, S., Jahn, G. J., McArthur, S. D. J., & McDonald, J. R. (2003). Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers. Paper presented at 12th Intelligent Systems Application to Power Systems (ISAP 2003), Lemnos, Greece.
Strachan, S. ; Jahn, G.J. ; McArthur, S.D.J. ; McDonald, J.R. / Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers. Paper presented at 12th Intelligent Systems Application to Power Systems (ISAP 2003), Lemnos, Greece.7 p.
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title = "Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers",
abstract = "This paper describes the application of cluster analysis and classification techniques for the diagnosis of partial discharge defects present in electrical power transformers. The subsequent implementation of an agent-based, decision support system (DSS) incorporating these intelligent techniques is also discussed. Successful defect classification of empirical partial discharge data, using neural networks and rule induction, affirms the application of these techniques as a suitable means of providing reliable decision support for partial discharge defect diagnosis, particularly where expert diagnostic knowledge may be scarce or ambiguous. Through the interaction of intelligent agents the DSS considers the effectiveness and diagnostic contribution of each agent (intelligent technique) before presenting a consolidated diagnosis.",
keywords = "intelligent diagnosis, defects, partial discharge activity, power transformers",
author = "S. Strachan and G.J. Jahn and S.D.J. McArthur and J.R. McDonald",
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language = "English",
note = "12th Intelligent Systems Application to Power Systems (ISAP 2003) ; Conference date: 31-08-2003 Through 03-09-2003",

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Strachan, S, Jahn, GJ, McArthur, SDJ & McDonald, JR 2003, 'Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers' Paper presented at 12th Intelligent Systems Application to Power Systems (ISAP 2003), Lemnos, Greece, 31/08/03 - 3/09/03, .

Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers. / Strachan, S.; Jahn, G.J.; McArthur, S.D.J.; McDonald, J.R.

2003. Paper presented at 12th Intelligent Systems Application to Power Systems (ISAP 2003), Lemnos, Greece.

Research output: Contribution to conferencePaper

TY - CONF

T1 - Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers

AU - Strachan, S.

AU - Jahn, G.J.

AU - McArthur, S.D.J.

AU - McDonald, J.R.

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N2 - This paper describes the application of cluster analysis and classification techniques for the diagnosis of partial discharge defects present in electrical power transformers. The subsequent implementation of an agent-based, decision support system (DSS) incorporating these intelligent techniques is also discussed. Successful defect classification of empirical partial discharge data, using neural networks and rule induction, affirms the application of these techniques as a suitable means of providing reliable decision support for partial discharge defect diagnosis, particularly where expert diagnostic knowledge may be scarce or ambiguous. Through the interaction of intelligent agents the DSS considers the effectiveness and diagnostic contribution of each agent (intelligent technique) before presenting a consolidated diagnosis.

AB - This paper describes the application of cluster analysis and classification techniques for the diagnosis of partial discharge defects present in electrical power transformers. The subsequent implementation of an agent-based, decision support system (DSS) incorporating these intelligent techniques is also discussed. Successful defect classification of empirical partial discharge data, using neural networks and rule induction, affirms the application of these techniques as a suitable means of providing reliable decision support for partial discharge defect diagnosis, particularly where expert diagnostic knowledge may be scarce or ambiguous. Through the interaction of intelligent agents the DSS considers the effectiveness and diagnostic contribution of each agent (intelligent technique) before presenting a consolidated diagnosis.

KW - intelligent diagnosis

KW - defects

KW - partial discharge activity

KW - power transformers

M3 - Paper

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Strachan S, Jahn GJ, McArthur SDJ, McDonald JR. Intelligent diagnosis of defects responsible for partial discharge activity detected in power transformers. 2003. Paper presented at 12th Intelligent Systems Application to Power Systems (ISAP 2003), Lemnos, Greece.