A Method for Estimating the Equivalent Model of Harmonic Source Based on the Extended Kalman Filter and Prediction Deviation

This paper presents a method for estimating the equivalent model of harmonic sources using the Extended Kalman Filter(EKF) and the prediction deviation. The parameters of the equivalent model are difficult to measure directly, so they are generally estimated using data measured in the power system and numerical analysis. However, existing methods based on numerical analysis have a fundamental problem that including outliers in measured data significantly degrades the estimation performance. Additionally, the measured data before changing the equivalent model parameters can slow down the convergence speed of the estimation. To solve this problem, this paper proposed an equivalent model estimation method using the EKF and the prediction deviation. The EKF algorithm reduces outliers included in the measured voltage and current and the RLS algorithm is initialized by calculating the prediction deviation. To verify the performance of the proposed method, a comparative analysis with the previous method was conducted for the case where random data outliers were included.