Kullback-Leibler Divergence-Based Fault Detection Scheme for 100% Inverter Interfaced Autonomous Microgrids

The development of a dependable and secure protection system is critical for further integration of renewable energy sources into electrical energy systems. To address the bidirectional power flow and limited fault current contribution of 100% inverter-based microgrids, this article presents a differential fault detection scheme (DFDI) based on monitoring the line-end current signals using an intelligent electronic device. The difference in current waveforms of a faulty line is quantified using the Kullback-Leibler divergence similarity measure. The efficacy of the proposed scheme in detecting severe and non-severe fault conditions with various fault inception angles in the presence of measurement noise and nonlinear load for both radial and loop configurations is assessed on two benchmark microgrids. As verified by various simulation scenarios on the benchmark microgrids, the proposed DFDI detects various types of faults in different locations with fault resistance up to 100 ohm. Moreover, it demonstrates high immunity to harmonics and measurement noise up to 25 dB. The results confirm that the proposed method offers a reliable, efficient, and adaptive solution for fault detection in inverter-based microgrids.