A Markovian influence graph formed from utility line outage data to mitigate large cascades

Abstract

We use observed transmission line outage data to make a Markov influence graph that describes the probabilities of transitions between generations of cascading line outages, where each generation of a cascade consists of a single line outage or multiple line outages. The new influence graph defines a Markov chain and generalizes previous influence graphs by including multiple line outages as Markov chain states. The generalized influence graph can reproduce the distribution of cascade size in the utility data. In particular, it can estimate the probabilities of small, medium and large cascades. The influence graph has the key advantage of allowing the effect of mitigations to be analyzed and readily tested, which is not available from the observed data. We exploit the asymptotic properties of the Markov chain to find the lines most involved in large cascades and show how upgrades to these critical lines can reduce the probability of large cascades.

to appear in IEEE Transactions on Power Systems

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• Relaxation Based Modeling of GMD Induced Cascading Failures in PowerModelsGMD.jl
• Resilience Analysis and Cascading FailureModeling of Power Systems under Extreme Temperatures
• Revisiting and modeling power-law distributions in empirical outage data of power systems
• Dynamic Constraint-based Influence Framework and its Application in Stochastic Modeling of Load Balancing