Fault Detection and Location in DC Microgrids by Recurrent Neural Networks and Decision Tree Classifier

Abstract

Microgrids have played an important role in distribution networks during recent years. DC microgrids are very popular among researchers because of their benefits. Protection is one of significant challenges in the way of microgrids progress. As a result, in this paper, a fault detection and location scheme for DC microgrids is proposed. Due to advances in Artificial Intelligence (AI) and suitable performance of smart protection methods in AC microgrids, Recurrent Neural Networks (RNNs) are used in the proposed method for fault location in DC micro grids. In this method, the fault detection and location are done by measuring feeders current and main bus voltage. Further, the performance of the proposed method is assessed in grid-connected, and islanded operation modes of microgrid. The result will confirm the efficiency of the proposed scheme. In this paper, MATLAB and DIgSILENT are used to design RNNs and DC microgrid simulation respectivly.