Iteratively reweighted least squares for maximum likelihood identification of synchronous machine parameters from on-line tests

This paper presents a new approach for the statistical identification of synchronous-machine parameters from on-line test data that were recorded on a 202-MVA hydro-generator at Hydro-Quebec's La Grande 3 generating station. Data processing is performed to remove harmonics in noise-corrupted measurements. The time-domain parameter identification is carried out by means of our proposed maximum-likelihood estimation method, also called the iteratively reweighted least-squares algorithm. A comparison of the results with the ordinary weighted least-squares estimation, which is equivalent to the maximum-likelihood estimation only when the noise is white, shows the superiority of the proposed method. This procedure appears more convenient than previous schemes for parameter identification of the synchronous machine linear equivalent-circuits, especially when the noise statistics are poorly known.