期刊
EXPERT SYSTEMS WITH APPLICATIONS
卷 210, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2022.118368
关键词
Fault diagnosis; Visual explanation; Smart grids; Interpretability 2010 MSC
类别
资金
- e-distribuzione S.p.A company, Italy
- Italian MISE FSC
- Italian P.O. Puglia FESR [2014/20]
In the field of electrical grids, fault diagnosis is critical but often lacks interpretability. This paper proposes a spectrogram-convolutional neural network approach for representing electrical signals, using pre-trained models for multiple fault classification tasks and providing visual interpretation of the results to enhance transparency.
In electrical grids, fault diagnosis (fault type and fault location classifica-tions) 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 sig-nals, we propose a spectrogram-convolutional neural network based represen-tation of the electrical signals where pre-trained models such as GoogleNet and SqueezeNet are trivially used. We then perform multiple fault classifica-tion tasks and offer a visual interpretation of the collected findings. The sug-gested 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 ma-chine learning system, which is a critical requirement for designing modernized smart grids.
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