  The area of Smart Power Grids needs to constantly improve its efficiency and
resilience, to pro-vide high quality electrical power, in a resistant grid,
managing faults and avoiding failures. Achieving this requires high component
reliability, adequate maintenance, and a studied failure occurrence. Correct
system operation involves those activities, and novel methodologies to detect,
classify, and isolate faults and failures, model and simulate processes with
predictive algorithms and analytics (using data analysis and asset condition to
plan and perform activities). We show-case the application of a
complex-adaptive, self-organizing modeling method, Probabilistic Boolean
Networks (PBN), as a way towards the understanding of the dynamics of smart
grid devices, and to model and characterize their behavior. This work
demonstrates that PBNs are is equivalent to the standard Reinforcement Learning
Cycle, in which the agent/model has an inter-action with its environment and
receives feedback from it in the form of a reward signal. Differ-ent reward
structures were created in order to characterize preferred behavior. This
information can be used to guide the PBN to avoid fault conditions and
failures.
