Utility Harmonic Impedance Estimation Based on Isolation Forest and Bernaola Galvan Method

Traditional non-invasive harmonic impedance estimation methods often suffer from significant accuracy degradation or even failure under conditions of large background harmonic voltage fluctuations and abrupt changes in harmonic impedance. To address these challenges, a novel estimation method for utility harmonic impedance is proposed. First, a sliding-window-based isolation forest (IF) method is proposed to extract data segments exhibiting a strong correlation between harmonic voltage and current, thereby reducing the adverse impact of background harmonic voltage fluctuations on the impedance estimation results. Then, the Bernaola Galvan (BG) method is proposed to detect abrupt change points in harmonic impedance, and data are grouped accordingly based on the detected change points. Finally, a binary linear regression method is employed to estimate the utility harmonic impedance. The proposed method is validated using both simulation and field data, achieving relative errors below 4% and outperforming conventional methods under complex conditions.