Enhancing the reliability of protection scheme for PV integrated microgrid by discriminating between array faults and symmetrical line faults using sparse auto encoder. Issue 2 (26th November 2018) - Manohar, Murli; Koley, Ebha; Ghosh, Subhojit
Abstract : The ever increasing power demand and stress on reducing carbon footprint have paved the way forwidespread use of photovoltaic (PV) integrated microgrid. However, thedevelopment of a reliable protection scheme for PV integrated microgrid ischallenging because of the similar voltage‐current profile of PV array faultsand symmetrical line faults. Conventional protection schemes based onpre‐defined threshold setting are not able to distinguish between PV array andsymmetrical faults, and hence fail to provide separate controlling actions forthe two cases. In this regard, a protection scheme based on sparse autoencoder(SAE) and deep neural network has been proposed to discriminate between arrayfaults and symmetrical line faults in addition to perform mode detection, faultdetection, classification and section identification. The voltage‐currentsignals retrieved from relaying buses are converted into grey‐scale images andfurther fed as input to the SAE to perform unsupervised feature learning. Theperformance of the proposed scheme has been evaluated through reliabilityanalysis and compared with artificial neural network, support vector machine anddecision tree based techniques under both islanding and grid‐connected mode ofthe microgrid. The scheme has been also validated for field applications byperforming real‐time simulations on OPAL‐RT digital simulator.