A Framework for Power Recovery Probability Quantification in Nuclear Power Plant Station Blackout Sequences



George-Williams, H ORCID: 0000-0002-9316-3911, Lee, M and Patelli, E ORCID: 0000-0002-5007-7247
(2016) A Framework for Power Recovery Probability Quantification in Nuclear Power Plant Station Blackout Sequences. In: International Conference on Probabilistic Safety Assessment and Management (PSAM), 2016-10-2 - 2016-10-7, Seoul, South Korea.

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Abstract

The safety of Generations II and III nuclear power plants relies on the availability of AC power, which is required for decay heat removal. This AC power (designated offsite power) is provided by sources outside the power plant via a grid that is susceptible to both random and induced failures. When offsite power is lost, alternative emergency sources on-site are started to drive the plant's safety systems. If, however, a situation arises where these sources are also unavailable or unable to provide the required power for the entire period the offsite sources are unavailable, a complete loss of power to the safety buses ensues. This phenomenon is known as Station Blackout (SBO), and its severity depends on its duration as well as, the plant's initial status. Consequently, the time-dependent non-recovery probability of AC power is a key parameter in the risk assessment and management of nuclear power plants. In this work, an easy-to-use and generally applicable reliability framework is proposed to model power recovery in station blackout sequences. It employs a load flow technique integrated into an efficient event-driven Monte-Carlo simulation algorithm. The resulting framework quantifies the probability of power recovery as a function of both time and power level, including other relevant indices. It, therefore, serves the purpose of a rational decision support tool in the mitigation of station blackout accidents. The proposed framework is used to analyse station blackouts emanating from grid and switchyard failures at the Maanshan nuclear power plant in Taiwan.

Item Type: Conference or Workshop Item (Unspecified)
Depositing User: Symplectic Admin
Date Deposited: 06 Oct 2016 16:09
Last Modified: 19 Jan 2023 07:29
URI: https://livrepository.liverpool.ac.uk/id/eprint/3003644