Novel Robust Classifier for Power Quality Disturbances in AC Microgrid

Modern day power system, is radically different from the conventional power system. The integration of renew-able energy resources, FACTS devices and enhanced power elec-tronics have added to the transients and non-linearity. These are collectively referred as power quality disturbance (PQD). Further, the Inclusion of IoT, communication channels in power system to enable real-time monitoring, have led to the distortion of data. The presented article presents a novel methodology to classify the power quality disturbances in an AC microgrid with high efficacy. The proposed methodology have been implied on simulated noise less and noisy data of voltage signals in microgrid. ML based classifiers are then applied on distinct time domain and frequency based classifiers, and the performance of method is then compared on basis of various metrics. The present work intends to open a new pathway for the PQD classification techniques in the era of communication, often distorting the data signals.