Estimation of Switching Overvoltages during Energization of Transmission Lines using Recurrent Neural Networks

Abstract

Overvoltages frequently pose significant challenges during the energization of transmission lines. During restoration, transmission lines have to be energized from zero voltage to the nominal voltage and the switching of transmission lines is a primary source of these overvoltages. The magnitude and waveform of switching overvoltages are influenced by system parameters, network configuration, and the specific point in the wave cycle at which switching occurs. The ability to estimate peak overvoltages in real-time is crucial for operators, during power system restoration. Traditional methods rely on extensive simulations or empirical formulas, which may not provide the necessary speed or accuracy for operational decisions. It is crucial for operators to ensure that peak overvoltages from switching actions remain within safe limits. This paper introduces a compact long short term memory recurrent neural network (LSTM RNN) based methodology to estimate the peak overvoltages induced during line energization. The developed RNN is trained and tested using extensive simulated data in PSCAD. The results demonstrate that the proposed RNN technique accurately estimates the peak values of switching overvoltages, offering a reliable tool for operators during power system restoration.