An accident diagnosis algorithm for HTR-PM based on deep learning methods. (August 2019)

• Record Type: Journal Article
• Authors: Li, Zeguang; Sun, Jun; Tong, Jiejuan; Sui, Zhe; Gang, Langming
• Is Part Of: Progress in nuclear energy. Volume 115(2019)
• Page Start: 140
• Page End: 150
• Publication Date: 2019-08
• Subjects: Accident diagnosis -- Safety analysis -- Deep learning methods -- Online diagnosis -- High temperature gas-cooled reactor; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 333.7924
• DOI: 10.1016/j.pnucene.2019.03.038
• Languages: English
• ISSNs: 0149-1970

Abstract

Abstract: The Chinese High Temperature Reactor Pebble-bed Module (HTR-PM) is the world first commercial nuclear power plant (NPP) with the characteristics of fourth generation. Accident diagnosis tasks for HTR-PM are directly associated with safe and efficient operation. Although different kinds of accident diagnosis methods have been studies on conventional NPPs, the research of accident diagnosis of HTR-PM is relatively lacking according to the different characteristics and new applications of HTR-PM. In this article, a new algorithm for HTR-PM accident diagnosis based on deep learning methods is proposed. By using the preprocessing, classification network and postprocessing techniques, the proposed algorithm could avoid over-reliance on the previous experiences and make fully use of the signals, also it can use only few number of training signal sequences to get high accuracy results, which is a significant advantage compared with traditional algorithms using deep learning methods. This algorithm is tested using the signals produced by the engineering simulator of HTR-PM for normal state and several accidents, including loss of feed water, large break depressurized loss of forced cooling, small break depressurized loss of forced cooling and inadvertent withdrawal of a single control rod. The results show the feasibility and effectiveness of the algorithm for HTR-PM accident diagnosis, and also the potentiality to use in other NPPs accident diagnosis tasks.