Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder

The ever increasing power demand and stress on reducing carbon footprint have paved the way for widespread use of photovoltaic (PV) integrated microgrid. However, the development of a reliable protection scheme for PV integrated microgrid is challenging because of the similar voltage-current profile of PV array faults and symmetrical line faults. Conventional protection schemes based on pre-defined threshold setting are not able to distinguish between PV array and symmetrical faults, and hence fail to provide separate controlling actions for the two cases. In this regard, a protection scheme based on sparse autoencoder (SAE) and deep neural network has been proposed to discriminate between array faults and symmetrical line faults in addition to perform mode detection, fault detection, classification and section identification. The voltage-current signals retrieved from relaying buses are converted into grey-scale images and further fed as input to the SAE to perform unsupervised feature learning. The performance of the proposed scheme has been evaluated through reliability analysis and compared with artificial neural network, support vector machine and decision tree based techniques under both islanding and grid-connected mode of the microgrid. The scheme has been also validated for field applications by performing real-time simulations on OPAL-RT digital simulator.