Utility Outage Data Driven Interaction Networks for Cascading Failure Analysis and Mitigation

In this paper, real outage data from utilities are analyzed to gain better understanding of cascading outage propagation. In order to accurately estimate the interactions between component outages, two mechanisms are introduced: the evolution of interactions over generations and the memory between consecutive generations. A metric, the expected number of outages following one component outage, is calculated based on the estimated interaction networks by solving a set of carefully formulated linear equations, considering loops due to the complex component interactions. Existence of unique positive solution for the linear equations is mathematically proved. Components that are critical for outage propagation are further identified based on the developed metric. Besides, the outage propagation properties are revealed by the interaction networks estimated from real outage data. Further, the cascades simulated from a highly probabilistic generation-dependent interaction model using the estimated interactions well capture the properties of the original outage data in terms of the distribution of the number of line outages, the offspring mean of the branching process, the distribution of the component metrics, and the identified critical components. Cascading failure risks are greatly mitigated by reducing the probability that the identified critical components fail.