Out-of-step prediction based on artificial neural networks

The application of artificial intelligence to power systems has resulted in an overall improvement of solutions in many implementations. This paper presents a new approach to the prediction (detection) of out-of-step synchronous generators based on artificial neural networks (ANNs). The paper describes the ANN architecture adopted as well as the selection of input features for training the ANN. A feedforward model of the neural network based on the stochastic back-propagation training algorithm has been used. The capabilities of the developed algorithm for the prediction of the out-of-step condition have been tested through computer simulation for a typical case study. The results of using the proposed algorithm reveal a high classification performance.