| Title |
Fault Detection Sensitivity of a Data-driven Empirical Model for the Nuclear Power Plant Instruments |
| Authors |
허섭(Hur, Seop) ; 김재환(Kim, Jae-Hwan) ; 김정택(Kim, Jung-Taek) ; 오인석(Oh, In-Sock) ; 박재창(Park, Jae-Chang) ; 김창회(Kim, Chang-Hwoi) |
| DOI |
https://doi.org/10.5370/KIEE.2016.65.5.836 |
| Keywords |
Data-driven empirical model ; NPP instruments ; Fault detection ; Normal operation ; Accident conditions |
| Abstract |
When an accident occurs in the nuclear power plant, the faulted information might mislead to the high possibility of aggravating the accident. At the Fukushima accident, the operators misunderstood that there was no core exposure despite in the processing of core damage, because the instrument information of the reactor water level was provided to the operators optimistically other than the actual situation. Thus, this misunderstanding actually caused to much confusions on the rapid countermeasure on the accident, and then resulted in multiplying the accident propagation. It is necessary to be equipped with the function that informs operators the status of instrument integrity in real time. If plant operators verify that the instruments are working properly during accident conditions, they are able to make a decision more safely. In this study, we have performed various tests for the fault detection sensitivity of an data-driven empirical model to review the usability of the model in the accident conditions. The test was performed by using simulation data from the compact nuclear simulator that is numerically simulated to PWR type nuclear power plant. As a result of the test, the proposed model has shown good performance for detecting the specified instrument faults during normal plant conditions. Although the instrument fault detection sensitivity during plant accident conditions is lower than that during normal condition, the data-drive empirical model can be detected an instrument fault during early stage of plant accidents. |