Probabilistic risk assessment of station blackouts in nuclear power plants

Adequate ac power is required for decay heat removal in nuclear power plants. Station blackout (SBO) accidents, therefore, are a very critical phenomenon to their safety. Though designed to cope with these incidents, nuclear power plants can only do so for a limited time, without risking core damage and possible catastrophe. Their impact on a plant's safety are determined by their frequency and duration, which quantities, currently, are computed via a static fault tree analysis that deteriorates in applicability with increasing system size and complexity. This paper proposes a novel alternative framework based on a hybrid of Monte Carlo methods, multistate modeling, and network theory. The intuitive framework, which is applicable to a variety of SBOs problems, can provide a complete insight into their risks. Most importantly, its underlying modeling principles are generic, and, therefore, applicable to non-nuclear system reliability problems, as well. When applied to the Maanshan nuclear power plant in Taiwan, the results validate the framework as a rational decision-support tool in the mitigation and prevention of SBOs.