Small-signal stability assessment based on advanced neural network methods

This paper deals with a new method for eigenvalue prediction of critical stability modes of power systems based on neural networks. Special interest is focused on inter-area oscillations of large-scale interconnected power systems. The existing methods for eigenvalue computations are time-consuming and require the entire system model that includes an extensive number of states. After reduction of the neural network input space and proper training of the neural network, the stability condition of the power system can be predicted with high accuracy. Hereby, the neural network outputs are assigned to regions where the critical eigenvalues can be found.