Self-organizing maps for contingency analysis: visual classification and temporal evolution

In this paper an analysis of the applicability of Kohonen´s self-organizing maps (SOMs) to contingency analysis in power systems is presented. We show the applicability of this artificial neural paradigm for both visualization and graphic monitoring of contingency severity, and the prediction of the system evolution to a future possible dangerous state. Both bidimensional and linear SOMs have been studied using as reference standard IEEE-14 and IEEE-118 electrical networks. Among the advantages of linear SOMs with respect to bidimensional SOMs and other classical methods we highlight the following ones: (1) a greater number of contingencies may be represented in one only screen and they may be more easily analyzed by a human operator; (2) the architecture and training process complexity of the SOM does not significantly increase with the power system size; and (3) the operation model is carried out in real time.