Enhancing the Reliability of Protection Scheme for PV Integrated Microgrid by Discriminating between Array Faults and Symmetrical Line Faults using Sparse Auto Encoder

摘要： The ever increasing power demand and the stress on reducing carbon footprint have paved the way for widespread use of 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 auto-encoder and deep neural network (SAE-DNN) approach has been proposed to discriminate between array faults and symmetrical line faults in addition to performing the tasks of mode detection, fault detection, classification and section identification. The voltage and current signals retrieved from relaying buses are converted into grayscale image dataset, which is fed as input to the SAE to perform the unsupervised feature learning. The performance of proposed scheme has been evaluated through reliability analysis and compared with ANN, SVM and DT 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.