Damodhar Reddy

Abstract In this paper a novel predictive technique is proposed for power quality (PQ) event classification in smart grid. Here Ensemble Kalman filter (EnKF) based approach and Entropy based trust modelling are utilized to design hybrid learning model that can classify PQ events during non-stationary environment. Here both PQ real and synthetic data had been used to mitigate the scarcity of real time data.

Abstract This study introduces a robust hybrid predictive framework for early detection and classification of power quality (PQ) events in carbon-aware smart distribution systems. The proposed approach synergistically combines the Ensemble Kalman Filter (EnKF) with a reliability-aware synthetic data integration strategy, where the contribution of each synthetic sample is adaptively weighted using entropy-derived confidence metrics.

M. Ravinder and Vikram Kulkarni* Department of Information Technology, SVKM’s NMIMS Mukesh Patel School of Technology Management and Engineering, SVKM’s NMIMS University, Mumbai, India The intrusion detection in network traffic for crucial smart metering applications based on radio sensor networks is becoming very important in the Smart Grid area. The network’s structure for smart meters under investigation should consider important security factors.