Convolutional neural network-based intelligent protection strategy for microgrids

Microgrids experience significantly different fault currents in different operating scenarios, which make microgridprotection challenging. Existing intelligent protection schemes rely on the extraction of appropriate fault features using statisticalparameters. The selection of these features is difficult in a microgrid because of its various operating scenarios. This studydevelops a convolutional neural network-based intelligent fault protection strategy (CNNBIPS) for microgrids that inherentlyintegrates the feature extraction and classification process. The proposed strategy is directly applicable to three-phase (TP)current signals; thus, it does not require any separate feature extractor. In the proposed CNNBIPS, TP current signals sampledby the protective relays are used as an input to three different CNNs. The CNNs apply convolution and pooling operations toextract the features from the input signals. Then, fully connected layers of the CNNs employ the features to develop fault-type,phase, and location information. To analyse the efficacy of the proposed design, we execute exhaustive simulations on astandard microgrid test system. The results confirm the effectiveness of the proposed strategy in terms of detection accuracy,security, and dependability. Moreover, comparisons with previous methods show that the proposed approach outperforms theexisting microgrid protection schemes.