Identification of parameters of an AC machine from standstill time domain data

Abstract

This paper presents an evaluation of the performance of the maximum likelihood (ML) method when used to estimate the linear parameters of a synchronous machine model from the standstill time-domain flux decay test data. It is shown that a unique set of parameters can be obtained and the noise effects can be dealt with effectively when the ML estimation technique is used. The results of study also show that accurate machine parameters can be identified even when signal-to-noise ratio is as low as 200:l.