Adaptive detection of generator out-of-step conditions in power systems using an artificial neural network

Application of artificial neural networks (ANN) to power systems has resulted in an overall improvement of solutions in many implementations. This paper presents a new approach for adaptive out-of-step detection of synchronous generators based on neural networks. The paper describes the ANN architecture adopted as well as the selection of the input features for training the ANN. A feedforward model of the neural network based on the stochastic backpropagation training algorithm has been used to predict the out-of-step condition. Due to power network configuration changes, the performance of the protective relays can vary. Consequently, an adaptive out-of-step prediction strategy is suggested in this paper. The capabilities of the proposed strategy have been tested through computer simulation for a typical case study. The results reveal an acceptable classification performance.