Waveform Parameter Evaluation of Lightning Impulse Voltage Based on Neural Network Method

Abstract

We propose a new approach based on applying neural networks for the base curve fitting of full lightning impulse voltage waveforms. In the standard IEC 61083-2, the Levenberg-Marquardt algorithm is employed in the calculation of the base curve to evaluate the waveform parameters. In this study, multilayer neural networks were constructed to correct the base curve calculated by simple two-exponential fittings. The approach to constructing the networks divides the data into two groups: training data and testing data. The training data were collected from the standard waveforms generated by the test data generator of the standard, where there were 29 cases of full lightning impulse voltage waveforms. The testing data were collected from real experimental data with various overshoot rates. The waveform parameters obtained by the proposed method were compared with those obtained by the standard approach, and very good agreement was always observed. It was found that the proposed method provides very accurate waveform parameters within the acceptable tolerances of the standard. Also, the method has a shorter execution time than the standard method. Therefore, the proposed method is an attractive choice for evaluating lightning impulse voltage waveform parameters.