期刊
RELIABILITY ENGINEERING & SYSTEM SAFETY
卷 94, 期 3, 页码 759-762出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.ress.2008.08.005
关键词
Artificial neural networks; Loss of coolant accident; Nuclear power plant; Operator support system
This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220MWe Indian PHWRs. The objective of this Study is to develop a methodology based on artificial neural networks (ANNs), which assists in identifying a transient quickly and suggests the operator to initiate the corrective actions during abnormal operations of the reactor. An operator support system, known as symptom-based diagnostic system (SBDS), has been developed using ANN that diagnoses the transients based on reactor process parameters, and continuously displays the status of the reactor. As a pilot study, the large break loss of coolant accident (LOCA) with and without the emergency core cooling system (ECCS) in reactor headers has been considered. Several break scenarios of large break LOCA have been analyzed. The time-dependent transient data have been generated using the RELAP5 thermal hydraulic code assuming ail equilibrium core, which conforms to a realistic estimation. The diagnostic results obtained from the ANN study are satisfactory. These results have been incorporated in the SBDS software for operator assistance. A few important outputs of the SBDS have been discussed in this paper. (C) 2008 Elsevier Ltd. All rights reserved.
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