Seismic vulnerability assessment of electrical substation system based on the hybrid fragility functions and Bayesian network

Abstract

Substations function as neural hubs within power systems and play pivotal roles in the aggregation, transformation, and distribution of electrical energy. Previous experiences indicate that substation systems are highly susceptible to damage under earthquakes, resulting in a subsequent decrease in power supply functionality. To mitigate the risk of earthquake-induced damage, a novel approach based on Bayesian theory is proposed to assess the seismic vulnerability of complex engineering systems. The proposed method initially obtains the prior distribution of seismic fragility parameters for electrical equipment through numerical simulations of coupled finite element models. Subsequently, seismic damage survey data and Bayesian updating rules are applied to update the prior probability, obtaining a hybrid fragility function for electrical equipment. The Bayesian network was constructed using logical relations among internal electrical components in the substation, aiming to quantify the seismic vulnerability of the system across different functionality indicators. Finally, the causal inference technique was employed to quantify the importance of various components and equipment. A realistic case study on a typical 220/110/35 kV substation system was performed using the proposed method. The results demonstrate that the method improves the confidence level of the equipment fragility curves, reduces the computational workload of the system vulnerability analysis, and provides a theoretical basis for improving substation performance and formulating post-disaster maintenance plans.