ASSESSMENT OF DYNAMIC PRA TECHNIQUES WITH INDUSTRY- AVERAGE COMPONENT PERFORMANCE DATA

摘要

In the nuclear industry, risk monitors are intended to provide a point-in-time estimate of the system risk given the current plant configuration. Current risk monitors are limited in that they do not properly take into account the deteriorating states of plant equipment, which are unit-specific. Current approaches to computing risk monitors use probabilistic risk assessment (PRA) techniques, but the assessment is typically a snapshot in time. Living PRA models attempt to address limitations of traditional PRA models in a limited sense by including temporary changes in plant and system configurations. However, information on plant component condition is not considered. This often leaves risk monitors using living PRA models incapable of conducting evaluations with dynamic degradation scenarios evolving overtime. There is a need to develop enabling approaches to solidify risk monitors to provide time- and condition-dependent risk by integrating traditional PRA models with condition monitoring and prognostic techniques. This paper presents an estimation of system risk evolution over time by integrating plant risk monitoring data with dynamic PRA methods that incorporate aging and degradation. Several online, nondestructive approaches have been developed for diagnosing plant component conditions in the nuclear industry, i.e., using a condition indication index, vibration analysis, current signatures, and operational history [1], In this work, the component performance measures at U.S. commercial nuclear power plants are incorporated within the dynamic PRA methodologies to provide better estimates of the probability of failures. Aging and degradation are modeled within the Level 1 PRA framework, are applied to several failure modes of pumps, and can be extended to a range of components, namely, valves, generators, batteries, and pipes.