Network switching and voltage evaluation during power system restoration

In this work, voltage evaluation after power components energization such as transmission line, transformer and shunt reactor is analyzed using artificial neural network (ANN)-based approach. Throughout the initial phase of system restoration, unexpected overvoltage may happen due to nonlinear interaction between the unloaded transformer and the transmission system. Such an overvoltage might damage some equipment and delay power system restoration. In the cases of transformer and shunt reactor energization, ANN is trained with the worst case scenario of switching angle and remanent flux which reduce the number of required simulations for training ANN. Moreover, for achieving good generalization capability for developed ANN, equivalent parameters of the network are used as ANN inputs. The simulated results for a partial of 39-bus New England test system show that the proposed technique can estimate the peak values and duration of overvoltages during network switching with good accuracy.