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 methodology.

通过从不同电网的音频和电能记录生成频谱图，利用特征明显的电网频率（ENF）模式辅助电网分类，本文提出了一种新的电网分类方法。该方法采用四种传统机器学习分类器和一个经由神经网络结构搜索优化的卷积神经网络（CNN）进行逐一分类，然后将这些分类结果综合到一个浅层多标签神经网络中进行训练，最终得出每个样本的确定性分类预测。实验结果表明，所提出的方法在验证和测试的准确率上超过了现有最先进的分类器，突显了其方法的有效性和鲁棒性。