A condition-informed dynamic Bayesian network framework to support severe accident management in nuclear power plants

Abstract

In the nuclear industry, Severe Accident Management Guidelines (SAMGs) are developed with the objective of providing prescriptive actions for mitigating the consequences of Nuclear Power Plant (NPP) accidents. These guidelines are defined with respect to a set of expert-based prototypical severe accident scenarios and are symptom-based, i.e., they relate to the values of specific monitored plant parameters. Expert-based SAMGs might fail to deal with non-prototypical scenarios, i.e., scenarios not considered a priori by the experts. To address this issue, in this paper we propose a methodology based on Dynamic Bayesian Networks (DBNs) to support operators decision-making for avoiding the escalation of severe accidents to non-prototypical scenarios. The methodology is based on the integration of condition monitoring data into a DBN to allow it to i) infer the current Plant Operating Condition (POC), Initiating Event (IE), and NPP safety barriers Damage State (DS), ii) predict the grace time (GT) available to carry out mitigative actions, and iii) evaluate the effectiveness of the alternative candidate mitigative actions taking into account the safety barriers estimated DS and the predicted available GT. The proposed methodology is exemplified on a Loss of Coolant Accident (LOCA) in a WWER-1000.