Hybrid adaptive nearest neighbor approaches to dynamic security assessment

We develop a general hybrid k Nearest Neighbors (kNN) approach, where kNNs take advantage of problem-specific information provided by decision trees and of general-purpose optimization provided by genetic algorithms. This general methodology is then adapted to two concerns of power system dynamic security that kNNs are conceptually well appropriate to handle. One such question of paramount importance is how to detect outliers; these are cases "too far away" from the preanalyzed cases of the data base used to train kNNs. The other question is how to avoid dangerous diagnostics which could arise from an erroneous identification of the relevant majority class of neighbors. In this paper, these two questions are tackled in the context of transient stability and illustrated on the Hydro-Quebec power system. Copyright (C) 1998 IFAC.