Prediction of critical clearing time using artificial neural network

This paper is concerned with the application of feed forward artificial neural networks for the prediction of the critical clearing time of a fault in power systems. The training of ANNs is done using selected features as inputs and the critical clearing time (CCT) as desire target. A single contingency was applied and the target CCT was found using time domain simulations. Multi layer feed forward neural network trained with Levenberg-Marquardt (LM) back propagation algorithm is used to provide the estimated CCT. The simulation results show that ANNs is capable to provide fast and accurate mapping. This makes it attractive for real-time stability assessment.