Transient stability assessment in longitudinal power systems using artificial neural networks

Results of the application of artificial neural networks to the problem of transient stability assessment are presented. This technique is applied to a real longitudinal power system that includes discrete supplementary controls. Different representations of the training space patterns and neural networks architectures are investigated. Input variables include topological changes, load and generation levels and contingencies. A special organization of training patterns with a separation by type of contingency is proposed to reduce classification errors. A graphical presentation of results is power system suggested as an aid to help system operators to select preventive control actions.