## 2 Related Work  
Conventional approaches for frequency domain classification of the power grid primarily encompass signal processing and machine learning methodologies. Signal processing techniques entail the extraction of frequency domain features followed by manual comparison and identification, for instance, employing band-pass filters to extract fundamental ENF and harmonic information, as well as utilizing spectral analysis to scrutinize the ENF signal [10]. Furthermore, investigations have employed high-resolution spectral estimation methods based on eigenvalue decomposition, such as Multiple Signal Classification (MUSIC), as well as parameter estimation methods based on the signal subspace rotational invariance principle, such as Estimation of Signal Parameters via Rotational Invariance Techniques (ESPRIT), to analyze the characteristics of the ENF signal for grid classification and localization [11; 12]. While these approaches can partially extract features from the ENF \begin{table}
\begin{tabular}{c c c} \hline \hline
**Region** & **Voltage** & **Frequency** \\ \hline Brazil & 110/220 V & 60 Hz \\ China & 220 V & 50 Hz \\ Germany & 230 V & 50 Hz \\ Japan & 100 V & 50/60 Hz \\ USA & 120 V & 60 Hz \\ Myanmar & 230 V & 50 Hz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Schematic table of nominal voltages and ENFs in selected countries. signal and conduct regional classification, their efficiency and accuracy are heavily reliant on operator expertise. Machine learning-based approaches have been utilized for ENF classification. These methods leverage empirical knowledge to extract features from data and subsequently employ classification algorithms. While support vector machines (SVMs), random forests (RFs), and K-nearest neighbors (KNNs) are common machine learning techniques [12; 13], their reliance on manually designed features and combination strategies poses challenges when fully capturing the long-term and cyclical nature of ENFs as well as achieving efficient and accurate classifications, despite some improvements in automation and accuracy. The advancement of deep learning technology has led to the integration of neural network-based approaches in ENF classification. These methods excel at automatically extracting intricate features from data and demonstrating a strong performance in classifying tasks. Notably, [PERSON] et al. [14] achieved outstanding results through a multi-classifier fusion framework for regional classification. This approach leverages a convolutional neural network (CNN) to extract spatial features via convolutional layers; long short-term memory network (LSTM) to capture long-term dependencies within time -series data; and SVM for handling high-dimensional data. The collective performance is commendable with a final accuracy rate of 96%. In February 2024, [PERSON] et al. introduced the UniTS model [15]. This model is a unified, multi-task, time-series model suitable for prediction, classification, anomaly detection, and interpolation tasks. It can handle homogeneous time series with different variables and lengths. Illustrated in Figure 1, the UniTS model consists of multiple, stacked, repetitive modules known as UNITS blocks and a lightweight tower designed for both mask generation and classification. Each UNITS block integrates components such as multi-head self-attention (MHSA), variable MHSA, a dynamic multi-layer perceptron (MLP), and a gating module. The mask tower is responsible for generating task-specific masks, while the classification tower (CLS) is dedicated to handling classification tasks. The cross-attention mechanism processes tokens and performs classification predictions. Tested on 38 datasets, the UniTS model outperformed other models and demonstrated zero-shot learning as well as prompt-based learning capabilities.