  The size distribution of planned and forced outages and following restoration
times in power systems have been studied for almost two decades and has drawn
great interest as they display heavy tails. Understanding of this phenomenon
has been done by various threshold models, which are self-tuned at their
critical points, but as many papers pointed out, explanations are intuitive,
and more empirical data is needed to support hypotheses. In this paper, the
authors analyze outage data collected from various public sources to calculate
the outage energy and outage duration exponents of possible power-law fits.
Temporal thresholds are applied to identify crossovers from initial short-time
behavior to power-law tails. We revisit and add to the possible explanations of
the uniformness of these exponents. By performing power spectral analyses on
the outage event time series and the outage duration time series, it is found
that, on the one hand, while being overwhelmed by white noise, outage events
show traits of self-organized criticality (SOC), which may be modeled by a
crossover from random percolation to directed percolation branching process
with dissipation, coupled to a conserved density. On the other hand, in
responses to outages, the heavy tails in outage duration distributions could be
a consequence of the highly optimized tolerance (HOT) mechanism, based on the
optimized allocation of maintenance resources.
