Prioritization of MTTC-based combinatorial evaluation for hypothesized substations outages

Exhaustive enumeration of a S-select-k problem for hypothesized substations outages can be practically infeasible due to exponential growth of combinations as both S and k numbers increase. This enumeration of worst-case substations scenarios from the large set, however, can be improved based on the initial selection sets with the root nodes and segments. In this paper, the previous work of the reverse pyramid model (RPM) is enhanced with prioritization of root nodes and defined segmentations of substation list based on mean-time-to-compromise (MTTC) value that is associated with each substation. Root nodes are selected based on the threshold values of the substation ranking on MTTC values and are segmented accordingly from the root node set. Each segmentation is then being enumerated with S-select-k module to identify worst-case scenarios. The lowest threshold value on the list, e.g., a substation with no assignment of MTTC or extremely low number, is completely eliminated. Simulation shows that this approach demonstrates similar outcome of the risk indices among all randomly generated MTTC of the IEEE 30-bus system.