An Inverter-Based Data-Driven Method for Line Impedance Estimation Using Genetic Algorithm in Non-PMU LVDN

To estimate the line impedance, most of the related research relies on the installment of PMU and the information of voltage phase angle. However, the PMUs may not be available due to the high cost, which poses a challenge to estimate line impedance in low voltage distribution networks (LVDNs) using traditional methods. Moreover, conventional methods for estimating line impedance use the voltage drop model and least square regression algorithm, which are time-consuming and require large computational resources due to the nonlinear calculation process of the model. Therefore, this paper first derives the secondary model based on the center-tapped transformer and proposes the secondary line impedance estimation method based on Genetic Algorithm (GA) without considering the voltage phase angle. The Kalman filter is introduced to preprocess voltage data and improve the accuracy of estimation. Compared to conventional least square regression, the proposed method improves the accuracy from 79% to 97.5%. The results demonstrate that the proposed method can provide an accurate line impedance estimation based on limited samples with noise.