|Reference : Decision Trees and Transient Stability of Electric Power Systems|
|Scientific journals : Article|
|Engineering, computing & technology : Electrical & electronics engineering|
|Decision Trees and Transient Stability of Electric Power Systems|
|Wehenkel, Louis [Université de Liège - ULg > Dép. d'électric., électron. et informat. (Inst.Montefiore) > Systèmes et modélisation >]|
|Pavella, Mania [Université de Liège - ULg > Services généraux (Faculté des sciences appliquées) > Relations académiques et scientifiques (Sciences appliquées) >]|
|Pergamon Press - An Imprint of Elsevier Science|
|[en] Power Systems ; Stability ; Machine Learning|
|[en] An inductive inference method for the automatic building of decision trees is investigated. Among its various tasks, the splitting and the stop splitting criteria successively applied to the nodes of a grown tree, are found to play a crucial role on its overall shape and performances. The application of this general method to transient stability is
systematically explored. Parameters related to the stop splitting criterion, to the learning set and to the tree classes are thus considered, and their influence on the tree features is scrutinized. Evaluation criteria appropriate to assess
accuracy are also compared. Various tradeoffs are further examined, such as complexity vs number of classes, or
misclassification rate vs type of misclassification errors. Possible uses of the trees are also envisaged. Computational
issues relating to the building and the use of trees are finally discussed.
|Researchers ; Professionals ; Students ; General public|
|File(s) associated to this reference|
All documents in ORBi are protected by a user license.