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Abstract

This project uses Artificial Neural Networks (ANNs) to develop a prototype computer based Operator’s Advisory System for the early detection and diagnosis of plant transients.

Three transient monitors were developed two of which are ANN based. Each of the independently developed ANN classifiers was then integrated into a multi-level operator advisory system OAS. The first level of diagnosis provides information to the plant operator of the presence of a major transient. Should a transient be detected a corresponding module provides more detailed information on the size of the transient. To validate the diagnosis two methods are used in the OAS: User confirmation and a comparison with simulated plant data. The diagnosis is reproduced in an independently developed PWR simulator and the plant parameters compared. If in agreement, a high level of confidence was attached to the diagnoses, a poor match would suggest that the transient is not one that the diagnostic module had been trained on. The OAS was evaluated on a wide range of scenarios. The results of the tests were encouraging with the OAS successfully identifying a range of standard transients. However, tests on the robustness of the OAS proved inconclusive.

The project concludes with suggestions for future work.

Publication Type:	Thesis (Doctoral)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QP Physiology
Departments:	School of Science & Technology > Computer Science > Human Computer Interaction Design School of Science & Technology > School of Science & Technology Doctoral Theses Doctoral Theses

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