Artificial Neural Network Based Algorithm for Fault Detection in a Ring DC Microgrid Under Diverse Fault Conditions

Abstract

The DC microgrid has become a greater power system in modern power technology due to its wider acceptance as compared to the AC-based traditional power distribution network. Nevertheless, protection of the DC microgrid is a difficult and complicated task due to numerous types of fault scenarios such as pole-to-ground and pole-to-pole faults, variation in fault current magnitude during grid connected and islanded mode, as well as bidirectional behaviour of the converters. In addition to the abovementioned challenges, fault detection during varying fault resistance and intermittency is also a crucial and tricky task because the level of the fault current can vary due to the distinct value of the fault resistance. Therefore, in this manuscript, an ANN-based protection scheme is proposed to detect the fault under varying fault conditions. Furthermore, to investigate the appropriateness of the protection scheme, DT and kNN-based techniques have also been considered for analysis purpose. In the proposed protection scheme, the tasks of mode identification, fault detection/classification, as well as section identification, have been proposed. The results in Section 5 indicate that the protection scheme is capable and accurate for fault detection in any type of faulty condition.