Power System Stabilizers (PSS) have been applied as the most common solution to damp small magnitude and low frequency oscillations in modern electric power systems. Conventional Stabilizers, with fixed structure and parameters, have been used with this objective for several decades, but there are some system operation conditions where the performance of these linear stabilizers may deteriorate, especially when compared with that of stabilizers designed using modern control techniques. A Neural PSS, trained with a set of local linear controllers, is applied to establish the regions where a Conventional PSS shows low performance. Using non-linear digital simulations of a synchronous machine connected to an infinite-bus system and a multi-machine power system the Neural PSS is assessed showing superiority in those regions.
Power System Stabilizers; Neural Networks; Power System Control; Dynamic Stability; Excitation Control
