## 2 Related Work  
This section contains a review of recent studies that employed CWT and CNNs to analyze nonstationary signals in power systems, including GICs.  
A machine learning approach for detecting GICs in power grids was proposed in [14]. The authors used a CNN-based architecture to classify scalogram and spectrogram images obtained via the CWT and short-time Fourier transform (STFT) of currents of single-phase current transformers, respectively. CWT and STFT are spectral analysis methods. The main advantage of CWT is that the wavelet function is scaled during signal analysis, while the sinusoid function has a fixed size during STFT. The proposed framework, consisting of a hybrid feature extractor using Gaussian CWT and STFT and a CNN, successfully achieved good performance, even under low-intensity GICs. The framework works very quickly (detection is achieved within 30 ms). Thus, it can be applied for online monitoring applications in power systems.  
Study [15] proposed a deep learning method for fault diagnosis in dry-type transformers using vibration signals. To convert the transformer vibration signals to scalogram images, a CWT method was used, which allows for the extraction of fault features from different conditions. The proposed diagnosis method achieved the mean accuracy of 99.95%.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multicolumn{2}{c}{**Substation**} & \multicolumn{2}{c}{**Geographical Coordinates**} & \multicolumn{2}{c}{**The Period of Registration**} & \multicolumn{2}{c}{**Volume of**} \\ \hline
**Code** & **Name** & **Latitude (\({}^{\circ}\)N)** & **Longitude (\({}^{\circ}\)E)** & **Recorded Data (GB)** \\ \hline VKH & Vykhodnoy & 68.83 & 33.08 & October 2011 until now & 6.49 \\ RVD & Revda & 67.90 & 34.61 & May 2011 until now & 5.65 \\ TTN & Titan & 67.53 & 33.44 & June 2010–December 2014 & 2.28 \\ LKH & Loukhi & 66.08 & 33.12 & September 2011 until now & 5.67 \\ KND & Kondopoga & 62.22 & 34.36 & September 2011 until now & 6.67 \\ \hline \end{tabular}
\end{table}
Table 1: Details about the GIC registration system in power lines in northwestern Russia.  
The feature extraction and classification process took less than 7 s, which potentially provides a fast and accurate online diagnosis of transformer faults.  
In article [16], deep learning algorithms were used to classify power quality disturbances. This study examined the application of several CNN models for voltage disturbance classification. The results obtained showed high accuracy for a CNN created from scratch and ResNet-50 (transfer learning). This study confirmed the suitability of using the CNN for voltage disturbance classification and the advantage of using scalograms to characterize these disturbances.  
The studies listed above use CNN-based methods. This is due to the fact that this type of artificial neural network architecture has several advantages over other machine learning algorithms for image classification. Traditional image feature extraction algorithms focus more on manually setting specific image features, so these methods have a poor generalization ability and portability. In contrast, CNNs automatically detect the relevant features without any human supervision [17]. CNNs use a convolution technique that allows CNNs to reduce the amount of information that needs to be processed, making them faster and more efficient than other types of algorithms. CNNs can exploit the spatial and hierarchical structure of images by using filters that build more abstract and high-level representations as they go deeper into the network. Therefore, CNNs can capture the variability and diversity of the images, and also generalize well to new and unseen data. All this makes CNNs suitable for image classification.