Power file extraction process from bangladesh grid and exploring ENF based classification accuracy using machine learning

Abstract

The Electric Network Frequency (ENF) is the supply frequency of power distribution networks, which can be captured by multimedia signals recorded near electrical activities. It normally fluctuates slightly over time from its nominal value of 50 Hz/60 Hz. The ENF remain consistent across the entire power grid. This has led to the emergence of multiple forensic application like estimating the recording location and validating the time of recording. Recently an ENF based Machine Learning system was proposed which infers that the region of recording can be identified using ENF signal extracted from the recorded multimedia signal, with the help of relevant features. As supervised learning process requires ground truth to train classifier for identifying future unknown data, in this work-we report Power Recording data extraction process from the National Grid of Bangladesh. Furthermore, we used ENF data — derived from Power Recordings, to compare grids around the world and found out classification accuracy of Bangladesh National Grid. ENF derivation process from Power Recording data and set of features, which serve as identifying characteristics for detecting the region of origin of the multimedia recording are followed from published work. We used those characteristics in a multiclass Machine Learning implementation based on MATLAB which is able to identify the grid of the recorded signal.