Coupling of K-NN with decision trees for power system transient stability assessment

Decision trees are a rather unique automatic learning approach to power system security assessment, in particular due to their interpretability, their capability to identify the main driving parameters, and their computational efficiency. Yet, other automatic learning methods may have complementary potentials. This paper proposes hybrid techniques attempting to combine the advantages of decision trees with nearest neighbor methods, by coupling them while using genetic algorithms to further enhance their performances. The derived approaches are then applied to a real world study case. It is shown that the hybrid approaches are indeed superior to the corresponding “pure” ones. In particular, the proposed genetic algorithm-based nearest neighbor-decision tree technique shows to be very accurate and efficient for real-time applications