A neural network architecture for static security mapping in power systems

In this paper a neural network based architecture, which combines supervised and unsupervised learning for the static security assessment of power systems, is presented. The proposed method allows the on-line security evaluation of a possible outage simply by considering the position of the neuron activated by the pre-fault state vector in an output map, allowing an easy and immediate view of the contingency risks. The mapping capabilities of two unsupervised neural networks, SOM (self-organising map) and CCA (curvilinear component analysis), are compared. Numerical tests, carried out on a study system, are presented and discussed.