Estimating Diagnosis Time of Emergency Situations in Digitalized Control Rooms

Kim, Yochan; Choi, Jeong-Hun; Park, Jinkyun; Jung, Woo‐Sik; Lee, Seung Jun

doi:10.1007/978-3-319-94391-6_28

Cited by 1 publication

(1 citation statement)

References 7 publications

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“…Here, accident diagnosis depends on the process parameter values or their trends [33]. In terms of the time required for accident diagnosis, a safety report published by the International Atomic Energy Agency recommends that operators complete the diagnosis of an accident within 15 min after the first indication of the accident [34].…”

Section: Operator Actions In Emergency Situationsmentioning

confidence: 99%

Consistency Index-Based Sensor Fault Detection System for Nuclear Power Plant Emergency Situations Using an LSTM Network

Choi

Lee

2020

Sensors

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A nuclear power plant (NPP) consists of an enormous number of components with complex interconnections. Various techniques to detect sensor errors have been developed to monitor the state of the sensors during normal NPP operation, but not for emergency situations. In an emergency situation with a reactor trip, all the plant parameters undergo drastic changes following the sudden decrease in core reactivity. In this paper, a machine learning model adopting a consistency index is suggested for sensor error detection during NPP emergency situations. The proposed consistency index refers to the soundness of the sensors based on their measurement accuracy. The application of consistency index labeling makes it possible to detect sensor error immediately and specify the particular sensor where the error occurred. From a compact nuclear simulator, selected plant parameters were extracted during typical emergency situations, and artificial sensor errors were injected into the raw data. The trained system successfully generated output that gave both sensor error states and error-free states.

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Section: Operator Actions In Emergency Situationsmentioning

confidence: 99%