Due to the large size of electric power systems there is a very high computational burden when obtaining the optimum network by using classical optimization techniques. Several authors have used heuristics and/or sensisitivities in order to guide the search of optimal network investments. This paper proposes an Automatic Learning approach in order to decide whether a network change will improve the overall costs or not. more specifically, Decision Trees methods are used to identifiy a set of simple and reliable rules which combine criteria trees are integrated in a subtransmission planning tool, improving dramatically both the “optimality” of the resultant network and the computational time.
Keywords: Transmission planning, planning rules, automatic learning, decision trees, genetic algorithms, data mining.
Paper BPT99-304-16 . IEEE Power Tech '99 Conference, Budapest, Hungary, August 29-September 2, 1999.
Published: August 1999.