  Cascading failure of a power transmission system are initiated by an
exogenous event that disable a set of elements (e.g., lines) followed by a
sequence of interrelated failures (or more precisely, trips) of overloaded
elements caused by the combination of physics of power flows in the changed
system topology, and controls. Should this sequence accelerate it can lead to a
large system failure with significant loss of load. In previous work we have
analyzed deterministic algorithms that in an online fashion (i.e., responding
to observed data) selectively shed load so as to minimize the amount of lost
load at termination of the cascade. In this work we present a rigorous
methodology for incorporating noise and model errors, based on the Sample
Average Approximation methodology for stochastic optimization.
