Method for Fire Risk Assessment of Urban Power Substation Considering Data Uncertainty

Abstract

As an important infrastructure, urban power substation contributes to human life, economy and society. However, once a fire occurs, it may bring catastrophic casualties, economic losses and adverse social impacts. Due to the low frequency of fire and data scarcity with great uncertainty, weak attention is given to fire risk assessment of urban power substations. For this problem, combining Bayesian network and fuzzy set theory, a novel fuzzy Bayesian network (FBN) based on an improved similarity aggregation method and fuzzy analytic hierarchy process is proposed in this work for fire risk assessment of urban power substations considering data uncertainty. This work systematically identifies the potential accident causes of urban power substations and related firefighting behavior for mitigating fire consequences. Based on the identified causes, the probability of the urban power substation accident is estimated through FBN, considering data uncertainty caused by insufficient historical data and knowledge. This work also studies the consequences of urban power substation accidents, taking into account the effectiveness of firefighting behavior and the around ambient characteristics including distribution characteristics of people, buildings, and important property. The performance of the developed methodology has been demonstrated through case studies. The proposed method has the ability to evaluate the probability of the urban power substation accident, obtain the most likely types of accidents, predict the likelihood of varying degrees of fire consequences, and identify critical events and firefighting failure behavior that lead to fire accidents in urban power substations.