Risk Assessment Of Nuclear Power Plants Research Articles

A time-reliability correlation for estimating the diagnosis error probability of a nuclear power plant with up-to-date Human–Machine interfaces

Despite being developed more than four decades ago based on expert judgment, the THERP time-reliability correlation (TRC) remains widely employed for calculating diagnosis human error probabilities in human reliability analysis for nuclear power plant risk assessment. However, with numerous advancements in nuclear plant equipment and operations, as well as the emergence of plants featuring advanced interfaces, there's a growing need to validate the THERP TRC. The objective of this study is to establish a TRC for the diagnosis human error probability in a modern reference nuclear power plant equipped with up-to-date human–machine interfaces and compare it with the median of the THERP TRC. To achieve this goal, we devised a method to gather event diagnosis times from a simulator and developed procedures to derive diagnosis TRCs using this data. Our findings indicate that while the median of the THERP TRC offers a conservative diagnosis human error probability for up to 25 min, it becomes overly optimistic beyond this threshold.

Estimating the frequency of cyber threats to nuclear power plants based on operating experience analysis

The necessity and importance of cyber risk assessments at nuclear power plants is increasingly being recognized in recent times. However, the cyber risk assessment methods developed thus far have focused on engineering evaluations and expert judgments. Moreover, there is no available probabilistic database of cyber threats for nuclear power plants. In this study the frequencies of cyber threats were estimated based on operating experience analysis. The operating experiences are grouped by their characteristics to suggest the list of cyber threats, and the frequency of each cyber threat is calculated by two-stage Bayesian updates. This research is significant because it is the first study on a probabilistic approach to estimate cyber threats. Furthermore, this research offers the advantage of being able to update the threat frequencies with additional data accumulation. We expect that this approach can also be applied to probabilistic safety assessment, which is the most widely used method for nuclear power plant risk assessment.

Development of logical structure for multi-unit probabilistic safety assessment

Abstract Site or multi-unit (MU) risk assessment has been a major issue in the field of nuclear safety study since the Fukushima accident in 2011. There have been few methods or experiences for MU risk assessment because the Fukushima accident was the first real MU accident and before the accident, there was little expectation of the possibility that an MU accident will occur. In addition to the lack of experience of MU risk assessment, since an MU nuclear power plant site is usually very complex to analyze as a whole, it was considered that a systematic method such as probabilistic safety assessment (PSA) is difficult to apply to MU risk assessment. This paper proposes a new MU risk assessment methodology by using the conventional PSA methodology which is widely used in nuclear power plant risk assessment. The logical failure structure of a site with multiple units is suggested from the definition of site risk, and a decomposition method is applied to identify specific MU failure scenarios.

Risk-Based Inspection of Pressure Components in Power and Process Plants

An important application of reliability and risk assessment in pressure vessels and piping engineering is risk-based ranking of pressure components for inservice inspection. The higher-ranked components may then be inspected more often or by more rigorous nondestructive examination procedures than the lower-ranked components in the plants. The development of risk-based ranking of components has its origin in the nuclear power industry. More recently, it has been employed in fossil power and process plants. One major differences between nuclear power plant risk assessment and fossil-power/process plant risk assessment is that the former has only one dominant risk (undesired event), namely, core melt accident, whereas the latter may have more than one dominant risk, for example, explosion, catastrophic rupture, leakage of toxic chemicals, etc. This paper presents a ranking procedure that can be used in cases of possible multiple undesired events, and thus applicable to fossil power and process plants.

Assessment of the potential applicability of fuzzy set theory to accident progression event trees with phenomenological uncertainties

Abstract Expert opinion is frequently used in the risk analysis of nuclear power plant systems to assess, in particular, the probabilities of rare events. However, this procedure is always accompanied by imprecision and uncertainty that characterise the experts judgment. Since the fuzzy set theory provides a framework for dealing with such judgmental imprecision and uncertainty, the potential applicability of the fuzzy set theory to the uncertainty analysis of accident progression event trees with imprecise and uncertain branch probabilities and/or with a number of phenomenological uncertainty issues are examined as a possible alternative procedure to that used in the current probabilistic risk assessments. The main purpose of this paper is to demonstrate a potential use of fuzzy set theory and provide its formal procedure in the quantification of the uncertainties of accident progression event trees. First, an example application of the fuzzy set theory is made to the simple portion of a given accident progression event tree with typical imprecise and uncertain type of input data, and thereby computational algorithms suitable for application of the fuzzy set theory to the accident progression event tree analysis are identified and illustrated. Secondly, to show the merits of the fuzzy set theory model in real application, the procedure used in the simple example is extended to extremely complex accident progression event trees with a number of phenomenological uncertainty issues, i.e. a typical plant damage state ‘SEC’ of the Zion nuclear power plant risk assessment, and the results are compared with the one obtained by current probabilistic methods. In addition, discussions and answers for five major questions on its real application are given.