Machine Learning-based Voltage Dip Measurement of Smart Energy Meter

Abstract

In recent times, a huge amount of data is generated often termed as big data. Specifically, from the viewpoint of the smart grid paradigm, which contains information about various features in the grid. Motivated from the aforementioned points, in this paper, we introduce a novel concepts of big data and extend it to highlight its influence on the smart grid system. This study is focused on implementing existing approaches to analyse the data available, using the deep learning algorithms. An implementation is undertaken with its results analysed and thoroughly discussed to convey the effectiveness of the approaches. The space phasor model displays substantial information about the voltage dips and allows us to create a smart energy meter. The meter allows to have a minimalistic turn around time for analysis.