Machine Parameter Estimation of Superconducting Transformerusing a Genetic Algorithm Only for Inrush Current Waveform

Abstract

This paper presents the estimation of machine parameters for a superconducting power transformer. The estimation technique was developed using simplified φ- L magnetizing characteristics, the Ralston's Runge-Kutta method on spreadsheet software and a genetic algorithm. Using this estimation technique only for inrush current waveform, machine parameters and switching conditions of the superconducting transformer were estimated. EMTP-ATP simulation of the inrush current was carried out using these machine parameters. Consequently, the EMTP-ATP simulated waveforms reproduced the waveforms measured.