Voltage collapse prediction with locally recurrent neural networks

Voltage stability studies aim to evaluate the ability of a power system to keep acceptable value of voltages at all nodes either under normal or contingency conditions. Voltage instability involves generation, transmission, and distribution and includes a wide range of phenomena. When a power system is working close to its stability limit, perturbations can easily lead it to a voltage collapse. Among all the stability indicators available in literature, the one based on the minimum singular value of the Jacobian matrix is very common, but it requires a tedious and time consuming iterative solution of the dynamic load flow equations, especially in real size power systems, and therefore it cannot be used for on-line applications. In this paper a new methodology based on the use of artificial neural networks, which are characterized by fast computation and high ability to generalize, is proposed. The adoption of locally recurrent neural networks has permitted predicting the value of minimum singular value with high accuracy.