Synchronous generator parameters estimation

Abstract

In this work, genetic algorithm and hybrid genetic algorithm are used to estimates synchronous generator parameters using stand-still frequency response (SSFR) data. The case study for this work is a salient pole synchronous generator in a gas power plant. The transfer function model parameters are obtained through curve fitting over the recorded frequency-response data. Finally, the estimated parameters values were compared to nominal values and, the result of this comparison show that, these algorithms have acceptable accuracy for estimation of synchronous generators parameters using SSFR data.