## 4 GIC Classification by CNNs  
The main goal of this study is to create a binary classifier of scalograms of currents in an autotransformer neutral for analyzing data from the GIC monitoring system in  
Figure 2: Sample scalogram images taken from different days at different substations from the created dataset. SD means the standard deviation of wavelet coefficient values calculated for each scalogram. northwestern Russia. For this purpose, a comparative analysis of four CNN models with different configurations was carried out. All models were built from scratch. The first model has the following architecture: four convolutional layers, four max pooling layers, a flatten layer, and two fully connected layers. The architectures of the second and third models differ from the first one based on the presence of a dropout layer after the fourth max pooling layer and pre-processing layers before the first convolutional layer, respectively. The fourth model contains all of the above layers in its architecture. The models' training and testing were conducted using an Intel(R) Core(TM) i5-11400 CPU (central processing unit) at 2.6 GHz. Other computer specifications used in the experiment were 16 GB RAM (random access memory) and the Microsoft Windows 10 Pro 64-bit OS (operating system). A well-known Keras library (version 2.12.0) was chosen as the deep learning framework [38]. Keras is the open-source high-level API (application programming interface), running on top of the TensorFlow platform for machine learning. The CNN models were trained by employing 80% (640) of the scalogram images and validated using the other 20% (160). Accuracy and the binary cross-entropy loss function were used as the evaluation metrics. Training all models was carried out over 15 epochs. Table 2 shows the results of the models' performance validation. In general, it can be stated that all CNN models showed good results. An accuracy of 99.37% with a 0.0098 binary cross-entropy loss was obtained for the first CNN model. The second binary classifier model successfully classified the scalogram images with 99.37% accuracy and the lowest binary cross-entropy loss of 0.0092. For the third CNN model, the lowest accuracy of 98.75% with the highest value of binary cross-entropy loss of 0.0344 was obtained. The best accuracy of 100.00% with a binary cross-entropy loss of 0.0115 was estimated for the fourth CNN model. The difference in the total training time (15 epochs) between the fastest and slowest CNN model was only 16 s. As a result of comparing the performance of CNN models with different structures, the last neural network model containing image augmentation layers and a dropout layer was selected, since this one achieved maximum accuracy. Also, the higher loss value of this model compared to the first model (it achieved the second highest accuracy among the considered CNN architectures) suggests that the CNN learned fewer wrong features associated with noise or random fluctuations in the data. Thus, this model will potentially be better at classifying previously unseen data. The selected CNN model also outperformed a framework for detecting GICs in power systems using a hybrid time-frequency analysis (STFT and CWT) combined with a CNN in terms of accuracy. This framework achieved 90.95% accuracy [14]. Figure 3 shows the learning curves for this model on the training and validation sets. The use of dropout along with an artificial increase in the size of the training dataset allows the model to avoid overfitting and achieve high performance. As seen in Figure 3, the validation accuracy of the selected model is higher than the training accuracy and the validation loss is lower than the training one. This is explained by the fact that regularization (dropout) and data augmentation are applied only during training, making classification of the training set more difficult. Since the dropout layer is not active during validation, the model uses all features, which makes it more robust, increases validation accuracy, and decreases validation loss.