Enhancing the accuracy of rational function-based models using optimization

•This paper proposes to enhance the accuracy of rational function-based models.•Damped Gauss–Newton (DGN) method is used to improve the accuracy.•Three cases were taken as examples to show the effects of the DGN method.•Using DGN can substantially improve the accuracy of a model.

Rational function approximations are widely used for the modelling of frequency-dependent effects in power system components. This paper proposes to use optimization by the damped Gauss–Newton method to improve the accuracy of rational approximations calculated by three mainstream fitting techniques: Bode's asymptotic approximation, Sanathanan–Koerner iteration, and vector fitting. The optimization method is described in detail along with an outline of the numerical implementation. The proposed methodology is demonstrated for a synthetic example for the modelling of a measured reactor frequency response and for the rational fitting of the frequency-dependent parameters corresponding to a single-phase transmission line. In this example, we also present the impact of the optimization enhancement on the final time domain responses.