Dynamic brake switching strategies for stabilization of power systems using artificial neural networks

When a large disturbance appears on a power system, it may render the system unstable. One way to stabilize the post-disturbance system is to connect resistors or brakes at the generator terminals, and switch them dynamically. In this study, artificial neural networks have been trained to predict the switching times of these dynamic braking resistors for stability improvement. Training data for the nets were generated from a minimum time stabilizing strategy. Comparison of the back-propagation and radial-basis-function networks demonstrate that while both are suitable in estimating the switch times, the radial-basis-function networks are superior in terms of convergence characteristics as well as accuracy of prediction. The nets were also trained with different input features from the various generators.