Recursive Least Squares and Adaptive Kalman Filter-Based State and Parameter Estimation for Series Arc Fault Detection on DC Microgrids

In this article, we present a recursive least squares (RLS) and adaptive Kalman filter (AKF)-based state and parameter estimation (SE and PE) for series arc fault (SAF) detection and identification on dc microgrids. It is evident from the state-of-the-art research on dc SAFs that due to the lack of zero crossings and low current of the fault, the detection/identification of a SAF is difficult. Furthermore, due to the unplanned placement of sensors and the effect of SAF's noise signatures on the adjacent sensors, we present a RLS-based SE for voltages and injection currents. The injection currents and nodal voltages from the states are then used by the AKF for a quick SAF detection, by estimating line admittances on the microgrid. The simulation results, control hardware in loop (CHIL), and experimental results are presented to manifest the SE-PE technique's potential.