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 inspection of fault type and zone prediction in electrical grids using interpretable spectrogram-based CNN modeling

Ardito Carmelo;Deldjoo Yashar;Di Noia Tommaso;Di Sciascio Eugenio;Nazary Fatemeh
2022-01-01

Abstract

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.
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/243823
Citazioni
  • Scopus 11
  • ???jsp.display-item.citation.isi??? 9
social impact