Accuracy enhancement in estimation of the initiating event frequencies in risk monitor application on Kuosheng NPP. (February 2015)

• Record Type: Journal Article
• Authors: Lee, Yun-Ju Becker; Lin, Tsu-Jen; Liang, Sheau-Farn Max
• Is Part Of: Annals of nuclear energy. Volume 76(2015:Feb.)
• Page Start: 40
• Page End: 47
• Publication Date: 2015-02
• Subjects: Probabilistic Risk Assessment (PRA) -- On-Line Maintenance (OLM) -- Maintenance Integrated Risk Utilities (MIRU) -- Fault Tree Analysis (FTA) -- Degraded Fault Tree (DFT); Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2014.09.034
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: We create a new fault tree analysis method named Degraded Fault Tree (DFT). This study uses DFT on Kuosheng Nuclear Power Plant (NPP) in Taiwan. DFT is a simple method that can enhance reliability of risk estimation. The results reveal weakness of NPPs after using DFT. Using DFT can alert NPPs not to enter any maintenance with potential hazards. Abstract: Risk-Informed Regulation (RIR) based on the technology of the Probabilistic Risk Assessment (PRA) is an important issue in the safety management of Nuclear Power Plants (NPPs). With the advancement of the state-of-art PRA, On-Line Maintenance (OLM) has been widely used by NPPs in the USA. A new risk monitor for OLM, Maintenance Integrated Risk Utilities (MIRU), was developed by the Institute of Nuclear Energy Research (INER), in Taiwan, to facilitate the daily management and risk evaluation of maintenance activities. The initiating event frequencies (IEFs) estimated in MIRU was requested to be changed with the plant status to reflect the effects of varied maintenance activities. However, few researches have empirically documented the link between IEFs and maintenance. This article attempts to explore a way to enhance the accuracy of IEFs estimated in MIRU. A new fault tree analysis method named Degraded Fault Tree (DFT) was developed. The results have shown that use of DFT can estimate the IEFs correctly. The DFT can also alert NPPs not to enter any maintenance configurations with high potential of causing initiating events, which can further ensure the safety of OLMs.