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This paper presents a method for estimating the parameters of a harmonic equivalent model based on the RANSAC(random sample consensus) and RLS(recursive least square) algorithms. The harmonic contribution assessment basically requires the equivalent models of harmonic sources. This model consists of an equivalent impedance and an equivalent voltage source. Because the equivalent model parameters can not be measured directly, it is necessary to estimate the parameters of harmonic sources by measuring PCC voltage and current. In previous studies, several RLS-based methods have been proposed to obtain the equivalent parameters. However, if the measured data contains outliers, the methods lead to large errors in parameter estimation. Outlier is a data point that differs significantly from other measured data due to various causes such as measurement errors. In this paper, we propose a RANSAC based method to remove outlier from measured data set to improve parameter estimation performance. To verify the performance of the proposed method, a comparative analysis for the case of including data outlier, with the previous methods was performed
