Probabilistic Non-Suppression Model of Electrical Fire in Nuclear Power Plant Using Maximum Likelihood Estimation Method

Abstract

Electrical fire is the most significant fire-induced risk in nuclear power plants. In this study, we developed a probabilistic non-suppression model of electric fire by estimating the relevant parameters using the Maximum Likelihood Estimation (MLE) method to secure and improve the realism in Fire PRA in application to the Risk Informed Fire Protection Program. Based on the results of fitting 32 continuous probability distributions into fire incident data, 3-Parameter Weibull probability model was selected as the best fitted and adequate model and was compared with the existing exponential probability model. The comparison shows that the level of model adequacy is improved with decreases in BIC by 17.7%, in RSS by 24.4%, and in MSE by 58.2%. The 3-Parameter Weibull probability model drawn in this study is expected to contribute to the enhancement of the Fire PRA realism for supporting risk-based decision by reflecting real fire event experiences.