Diagnostic system for identification of accident scenarios in nuclear power plants using artificial neural networks

• Published in: Reliability engineering & system safety 2009-03, Vol.94 (3), p.759-762
• Main Authors: Santosh, T.V., Srivastava, A., Sanyasi Rao, V.V.S., Ghosh, A.K., Kushwaha, H.S.
• Format: Article
• Language: English
• Subjects: Applied sciences; Artificial neural networks; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Fission nuclear power plants; Installations for energy generation and conversion: thermal and electrical energy; Loss of coolant accident; Nuclear power plant; Operational research and scientific management; Operational research. Management science; Operator support system; Reliability theory. Replacement problems
• ISSN: 0951-8320; 1879-0836
• DOI: 10.1016/j.ress.2008.08.005

This paper presents the work carried out towards developing a diagnostic system for the identification of accident scenarios in 220 MWe 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 an 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.