Dual State - Parameter Estimation for Series Arc Fault Detection on a DC Microgrid

Abstract

In this paper, a detection and localization technique based on dual State and Parameter Estimation (SE and PE respectively) for series dc arc faults is presented. Detection of series arc faults in dc microgrids is challenging due to its low fault current. By using the available set of sensor measurement data over a period of time, a Least Squares (LS) based SE algorithm estimates the dc microgrid’s bus voltages and injection currents. Kalman Filter (KF) is then used to estimate the line conductances in the network, which are used to detect and localize (with respect to the faulted line) the series arc fault. Simulation results are presented with different case studies to demonstrate the robustness of the algorithm to normal operating conditions and different number and placement of sensors. Finally, Control Hardware in the Loop (CHIL) results are shown.