  Whereas maintenance has been recognized as an important and effective means
for risk management in power systems, it turns out to be intractable if
cascading blackout risk is considered due to the extremely high computational
complexity. In this paper, based on the inference from the blackout simulation
data, we propose a methodology to efficiently identify the most influential
component(s) for mitigating cascading blackout risk in a large power system. To
this end, we first establish an analytic relationship between maintenance
strategies and blackout risk estimation by inferring from the data of cascading
outage simulations. Then we formulate the component maintenance decision-making
problem as a nonlinear 0-1 programming. Afterwards, we quantify the credibility
of blackout risk estimation, leading to an adaptive method to determine the
least required number of simulations, which servers as a crucial parameter of
the optimization model. Finally, we devise two heuristic algorithms to find
approximate optimal solutions to the model with very high efficiency. Numerical
experiments well manifest the efficacy and high efficiency of our methodology.
