Abstract
The digitization and IoT advancement is evolving the energy sector. 5G is playing an important role in connecting various smart grid modules and stockholders. In this framework, this paper suggests a new adaptive-rate method for time-domain power quality (PQ) signals features extraction and identification. The incoming PQ signal is digitized with an event-driven A/D converter (EDADC). A novel selection mechanism is employed to efficiently segment the non-uniformly sampled signal. In next step, features of these segments are explored by performing only the time-domain analysis. The identification is performed with the knearest neighbor (KNN) classifier. Results demonstrate a 12.3 times reduction in collected samples count as compared to the traditional counterparts. It confirms a significant processing and power consumption effectiveness of the designed solution compared to the conventional equals. The proposed system secures an average recognition accuracy of 93.5% Thanks to the 5G network, fmdings are effectively logged on the cloud for further analysis and decision making (C) 2021 The Authors. Published by Elsevier