A Prediction Model For Lightning-Induced Overvoltages Over Lossy Ground Using Gaussian Process Regression

Abstract

The accurate assessment of lightning-induced over-voltages is essential for proper insulation coordination studies. In this paper, a novel machine learning based Gaussian process regression model is developed for the prediction of lightning-induced over-voltages considering wide range of ground resistivity and permittivity. The lightning-induced over-voltages have been computed using two-dimensional finite-difference time-domain technique associated with Agrawal field to line coupling model. To account for the input space variability, seven predictor variables namely: resistivity, permittivity, return stroke velocity, distance from flashpoint, return stroke peak current, front-time, and height of an overhead line are considered to determine the response variable, that is, lightning-induced over-voltages. Accordingly, the training of the Gaussian process regression model is carried out using lightning-induced over-voltages obtained from two-dimensional finite-difference time-domain technique for both first and the subsequent strokes. The estimation accuracy of model is appraised using root mean squared error indicating that the exponential kernel function has the least error than other kernel types for describing the covariance. The predictive performance is evaluated by corroborating the predictions from the model against the random test cases generated from two-dimensional finite-difference time-domain technique. The predicted results showed a good agreement with those obtained from finite-difference time-domain technique.