Data mining reactor fuel grab load trace data to support nuclear core condition monitoring

A critical component of an advanced-gas cooled reactor (AGR) station is the graphite core. As a station ages, the graphite bricks that comprise the core can distort and may eventually crack. As the core cannot be replaced the core integrity ultimately determines the station life. Monitoring these distortions is usually restricted to the routine outages, which occur every few years, as this is the only time that the reactor core can be accessed by external sensing equipment. However, during weekly refueling activities measurements are taken from the core for protection and control purposes. It is shown in this paper that these measurements may be interpreted for condition monitoring purposes, thus potentially providing information relating to core condition on a more frequent basis. This paper describes the data-mining approach adopted to analyze this data and also describes a software system designed and implemented to support this process. The use of this software to develop a model of expected behavior based on historical data, which may highlight events containing unusual features possibly indicative of brick cracking, is also described. Finally, the implementation of this newly acquired understanding in an automated analysis system is described

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

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