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Automatic learning methods have proved to be quite attractive in the context of power system security assessment. The complementarity of various methods proposed so far, lead us to combine them in a toolbox in order to exploit their advantages and discard their limitations. In this paper, we show how the nearest neighbor approach could be used to face the problem of detecting outliers, i.e. cases not well enough represented in the data base used to learn the models. More precisely, such detection can be based on distance rejection which implies the choice of an appropriate distance. On a particular real life problem, we show how the simple nearest neighbor in the candidate attributes space allows to reject such cases.

Distance rejection in the context of electric power system security assessment based on automatic learning