  Because failures in distribution systems caused by extreme weather events
directly result in consumers' outages, this paper proposes a state-based
decision-making model with the objective of mitigating loss of load to improve
the distribution system resilience throughout the unfolding events. The
sequentially uncertain system states, e.g., feeder line on/off states, driven
by the unfolding events are modeled as Markov states, and the probabilities
from one Markov state to another Markov state throughout the unfolding events
are determined by the component failure caused by the unfolding events. A
recursive optimization model based on Markov decision processes (MDP) is
developed to make state-based actions, i.e., system reconfiguration, at each
decision time. To overcome the curse of dimensionality caused by enormous
states and actions, an approximate dynamic programming (ADP) approach based on
post-decision states and iteration is used to solve the proposed MDP-based
model. IEEE 33-bus system and IEEE 123-bus system are used to validate the
proposed model.
