![]() ![]() The nets were also trained with different input features from the various generators. Comparison of the back-propagation and radial-basis-function networks demonstrate that whileīoth are suitable in estimating the switch times, the radial-basis-function networks are superior in terms of convergence characteristics as Training data for the nets were generatedįrom a minimum time stabilizing strategy. Trained to predict the switching times of these dynamic braking resistors for stability improvement. In this study, artificial neural networks have been Is to connect resistors or brakes at the generator terminals, and switch them dynamically. One way to stabilize the post-disturbance system When a large disturbance appears on a power system, it may render the system unstable. University of Petroleum and Minerals, Dhahran, Saudi Arabia Expert Systems with Applications 18 (2000) 101–109ĭynamic brake switching strategies for stabilization of power systemsĭepartment of Electrical Engineering, K.F.
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