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.
IEEE Power Tech '99 Conference, Paper BPT99-304-16, ISBN: 0780358376, Budapest (Hungary). 28 August - 02 September 1999
Publication date: August 1999.
J. Peco, E.F. Sánchez-Úbeda, T. Gómez, Enhancing optimal transmission or subtransmission planning by using decision trees, IEEE Power Tech '99 Conference, Paper BPT99-304-16, ISBN: 0780358376. ISBN: 0-7803-5836-8, Budapest, Hungary, 28 August - 02 September 1999