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In electrical grids, fault diagnosis (fault type and fault location classifications) are critical due to their economic and important implications. Numerous smart grid applications have embraced data-driven methodologies. While the majority of the work in this topic has been on increasing the predicted accuracy of machine-learning model for fault diagnosis, one important aspect that has received less attention is the interpretability of these systems. We advocate for a complementary perspective. To represent faulty signals, we propose a spectrogram–convolutional neural network based representation of the electrical signals where pre-trained models such as GoogleNet and SqueezeNet are trivially used. We then perform multiple fault classification tasks and offer a visual interpretation of the collected findings. The suggested approach makes the model more transparent through the use of Gradient-weighted Class Activation Mapping (Grad-CAM), which visualizes regions in the input spectrogram that are more relevant for predictions, assisting the end-user in the understanding and interpreting the results. We explore the merits of the suggested technique in terms of increasing the transparency of the black-box machine learning system, which is a critical requirement for designing modernized smart grids. • Visual representation of electrical signal by using spectrogram-based CNN modeling. • Building a visually interpretable fault type and zone classification system. • A highly accurate system able to provide individual or joint FTC or FZC. • Comparison with a large number of baselines using DFT or DWT. [ABSTRACT FROM AUTHOR]