The securement and improvement of realism in a probabilistic fire risk assessment (Fire PRA) are important in risk-informed performance-based regulations and decision support. In this study, a probabilistic fire brigade non-suppression model is developed for electrical fires using the Organization for Economic Cooperation and Development fire incident data on operating nuclear power plants collected from various countries by applying a non-negative continuous probability distribution with the maximum likelihood estimation method. The result of fitting 15 types of a non-negative continuous probability distributions shows that the log-normal probability model is the best fitting and most adequate model, and can best represent the actual fire suppression time by a fire brigade. The selected log-normal probability model was compared with the exponential probability model being used in an existing Fire PRA, which shows that the level of adequacy of the log-normal probability model is improved with a decrease in the bayesian information criteria by 7.9%, residual sum of squares by 100.0%, and mean squared error by 57.6%. The log-normal probability model selected from this study is expected to contribute to an enhancement of the Fire PRA realism in support of risk-informed decision making by reflecting actual fire suppression experience.

概率火灾风险评估（Fire PRA）中现实性的保障和改进在基于风险的基于绩效的法规和决策支持中非常重要。在这项研究中，使用经济合作与发展组织（OECD）从各个国家收集的有关运行中的核电厂的火灾事件数据，通过应用具有最大可能性的非负连续概率分布，开发了针对电气火灾的概率消防队非抑制模型估计方法。对15种非负连续概率分布进行拟合的结果表明，对数正态概率模型是最佳拟合和最充分的模型，并且可以最好地代表消防队的实际灭火时间。将所选的对数正态概率模型与现有Fire PRA中使用的指数概率模型进行比较，这表明对数正态概率模型的充分性水平随着贝叶斯信息标准的降低而提高了7.9％，残差平方和为100.0％，均方误差为57.6％。从本研究中选择的对数正态概率模型有望通过反映实际的灭火经验，有助于增强Fire PRA的现实性，以支持基于风险的决策。