Authors:
Georgios Tzolopoulos
;
Christos Korgialas
and
Constantine Kotropoulos
Affiliation:
Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki 54124, Greece
Keyword(s):
Electric Network Frequency (ENF), Power Grid Classification, Spectrogram Analysis, Data Augmentation, Neural Architecture Search (NAS), Fusion Framework.
Abstract:
The Electric Network Frequency (ENF) serves as a unique signature inherent to power distribution systems. Here, a novel approach for power grid classification is developed, leveraging ENF. Spectrograms are generated from audio and power recordings across different grids, revealing distinctive ENF patterns that aid in grid classification through a fusion of classifiers. Four traditional machine learning classifiers plus a Convolutional Neural Network (CNN), optimized using Neural Architecture Search, are developed for One-vs-All classification. This process generates numerous predictions per sample, which are then compiled and used to train a shallow multi-label neural network specifically designed to model the fusion process, ultimately leading to the conclusive class prediction for each sample. Experimental findings reveal that both validation and testing accuracy outperform those of current state-of-the-art classifiers, underlining the effectiveness and robustness of the proposed m
ethodology.
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