Identification of Cascading Failure Scenarios of Infrastructure Systems using Multi-Group Non-Dominant Sorting Algorithm

Power transmission networks are critical infrastructure systems of urban communities, but are prone to cascading failures due to their high level of interconnectivity. Therefore, it is of great interest to identify critical components of the network that may trigger cascading failures. However, existing approaches to identify critical cascading failures focus on topological effect for a limited number of initial component failures. Meanwhile, identification based on load flow analysis without a limit on the number of triggering component failures has not been extensively studied. In this study, we simulate the overload-induced cascading failures to find the most critical scenarios of initial failure events in a power grid. The proposed approach uses the multi-group non-dominant sorting algorithm (Choi and Song, 2017) with two objective functions, i.e. network impact measure, and the number of initial component failures. Numerical experiments on a 30-bus network demonstrate that the identified critical cascading scenarios, triggered by single and multiple component failures, may not share common components necessarily. The proposed approach is expected to identify a group of critical components, which may be neglected by existing approaches.The research was supported by the National Research Foundation of Korea (NRF) Grant (No. 2018M2A8A4052), funded by the Korean Government (MSIP)

Development of a Two-Stage DQFM to Improve Efficiency of Single- and Multi-Hazard Risk Quantification for Nuclear Facilities

The probabilistic safety assessment (PSA) of a nuclear power plant (NPP) under single and multiple hazards is one of the most important tasks for disaster risk management of nuclear facilities. To date, various approaches—including the direct quantification of the fault tree using the Monte Carlo simulation (DQFM) method—have been employed to quantify single- and multi-hazard risks to nuclear facilities. The major advantage of the DQFM method is its applicability to a partially correlated system. Other methods can represent only an independent or a fully correlated system, but DQFM can quantify the risk of partially correlated system components by the sampling process. However, as a sampling-based approach, DQFM involves computational costs which increase as the size of the system and the number of hazards increase. Therefore, to improve the computational efficiency of the conventional DQFM, a two-stage DQFM method is proposed in this paper. By assigning enough samples to each hazard point according to its contribution to the final risk, the proposed two-stage DQFM can effectively reduce computational costs for both single- and multi-hazard risk quantification. Using examples of single- and multi-hazard threats to nuclear facilities, the effectiveness of the proposed two-stage DQFM is successfully demonstrated. Especially, two-stage DQFM saves computation time of conventional DQFM up to 72% for multi-hazard example