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Enhancing optimal transmission or subtransmission planning by using decision trees.

J. Peco, E.F. Sánchez-Úbeda, T. Gómez

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.

DOI: DOI icon 10.1109/PTC.1999.826607    

Published: August 1999.


    Research topics:
  • *Forecasting and data mining
  • *Medium-term tactical planning

IIT-99-059A

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