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.
|Number of pages||7|
|Publication status||Published - 3 Sep 2003|
|Event||12th Intelligent Systems Application to Power Systems (ISAP 2003) - Lemnos, Greece|
Duration: 31 Aug 2003 → 3 Sep 2003
|Conference||12th Intelligent Systems Application to Power Systems (ISAP 2003)|
|Period||31/08/03 → 3/09/03|
- intelligent diagnosis
- partial discharge activity
- power transformers
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.