The study of methods, techniques and tools that can aid the decision processes in power systems, in its many sections, is a subject of great interest. This decision support can be accomplished through many different techniques, particularly those based on computational intelligence, given their applicability on domains with uncertainty. In this proposal, Bayesian networks are used for the extraction of knowledge models from the available data on power systems. Moreover, given the demands of these systems and some limitations imposed to the inferences in Bayesian networks, a method is proposed, using genetic algorithms, capable of extending the power of comprehensibility of the patterns discovered; it aims at finding the optimal scenario in order to attain a given target, considering the incorporation of a priori knowledge from domain specialists, identifying the most influent variables in the domain for the maximization of the target variable.
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