Improved methods of online monitoring and prediction in condensate and feed water system of nuclear power plant. (April 2016)

• Record Type: Journal Article
• Authors: Wang, Hang; Peng, Min-jun; Wu, Peng; Cheng, Shou-yu
• Is Part Of: Annals of nuclear energy. Volume 90(2016:Apr.)
• Page Start: 44
• Page End: 53
• Publication Date: 2016-04
• Subjects: Online trends prediction -- Artificial immunity system -- Quantitative simulation models; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2015.11.037
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Different methods for online monitoring and diagnosis are summarized. Numerical simulation modeling of condensate and feed water system in nuclear power plant are done by FORTRAN programming. Integrated online monitoring and prediction methods have been developed and tested. Online monitoring module, fault diagnosis module and trends prediction module can be verified with each other. Abstract: Faults or accidents may occur in a nuclear power plant (NPP), but it is hard for operators to recognize the situation and take effective measures quickly. So, online monitoring, diagnosis and prediction (OMDP) is used to provide enough information to operators and improve the safety of NPPs. In this paper, distributed conservation equation (DCE) and artificial immunity system (AIS) are proposed for online monitoring and diagnosis. On this basis, quantitative simulation models and interactive database are combined to predict the trends and severity of faults. The effectiveness of OMDP in improving the monitoring and prediction of condensate and feed water system (CFWS) was verified through simulation tests.