An Approach to Evaluate Switching Overvoltages during Power System Restoration

Abstract

Transformer switching is one of the important stages during power system restoration. This switching can cause harmonic overvoltages that might damage some equipment and delay power system restoration. Core saturation on the energisation of a transformer with residual flux is a noticeable factor in harmonic overvoltages. This work uses artificial neural networks (ANN) in order to estimate the temporary overvoltages (TOVs) due to transformer energisation. In the proposed methodology, the Levenberg–Marquardt method is used to train the multilayer perceptron. The developed ANN is trained with the worst case of switching condition, and tested for typical cases. Simulated results for a partial 39-bus New England test system, show the proposed technique can accurately estimate the peak values and durations of switching overvoltages.