## 3 Data and Methods
### The GIC Scalogram Dataset  
The quality of a dataset on which a CNN is trained has a strong impact on its classification performance. In order to be considered high-quality, a labeled dataset must satisfy two basic conditions. Firstly, the images must be directly related to the task. Secondly, the images in each class must be uniform.  
To train the CNN, a dataset containing two-dimensional images of wavelet spectra of the current in autotransformer neutrals was created. The produced dataset consists of 800 RGB scalogram images with a size of 224 \(\times\) 224 pixels. Since the goal of this study is to detect the presence or absence of GICs in the neutral current (binary classification), the dataset includes images of two classes: GICs and geomagnetically quiet hours (Figure 2). The high GIC values that are most dangerous for transformers have frequencies in the range from 1.5 mHz to 3.6 mHz [34], so it was decided to select the frequency range between 1.36 mHz and 5.42 mHz for the scalograms in this study. Also, in this frequency range, only neutral currents caused by unbalanced load distribution among three phases are reflected. Thus, a geomagnetically quiet hour means the absence of GICs and the presence of insignificant currents resulting from phase imbalance. This explains the consideration of the binary classification problem in this study. The number of examples in the \"GIC\" class (400) equals the number of examples in the \"geomagnetically quiet hour\" class (400), which makes the dataset balanced. This characteristic of the dataset makes training a classifier easier by helping to prevent the model from becoming biased towards the class with more examples, and potentially improves the accuracy of the future model.  
Each image in the dataset is a scalogram of the neutral current, with a duration of 60 min and a frequency range from 1.36 mHz to 5.42 mHz. The choice of such a time interval for this study is based on several reasons. Firstly, files that contain neutral current data are transferred from GIC monitoring devices to the server every hour. In the future, the proposed CNN model will be able to analyze new measurements in real time. Secondly, GIC events have a duration of up to several hours. The CWT is applied to the current data with a sampling interval of 1 s. As a previous study has shown [34], a sampling rate of 1 Hz is sufficient for a reliable estimate of GIC values, including peak values, as well as for determining their time localization. A non-orthogonal Morlet wavelet is used as the mother wavelet. The benefits of using this wavelet function as the most suitable function for the analysis of GICs in autotransformer neutrals were also considered and proven in practice in the previous study [34]. The colors in the scalograms denote the values of wavelet coefficients (Figure 2) and characterize the degree of matching between the researched signal and the wavelet. The red color corresponds to the maximum value of the energy and the blue color corresponds to the minimum one.  
We calculated the standard deviation (SD) of each scalogram image (Figure 2) to show that the standard deviation classification method is not applicable to our problem. SD characterizes the degree of deviation of data from the mean value and is defined as the square root of the variance [37]. To classify GIC and \"geomagnetically quiet hour\" scalograms using the SD, it is necessary to find an empirical threshold. But, as can be seen from the values in Figure 2, GIC scalograms can have both high and relatively low SD values, comparable to the SD values for \"geomagnetically quiet hour\" scalograms. This makes it difficult to find an accurate empirical threshold for classifying events, and may lead to incorrect class definition. This, in turn, potentially reduces the accuracy of this method.