Abstract
This paper reports ongoing research for the definition of a data-driven self-healing system using machine learning (ML) techniques that can perform automatic and timely detection of fault types and locations. Specifically, the proposed method makes use of spectrogram-based CNN modeling of the 3-phase voltage signals. Furthermore, to keep human operators informed about why certain decisions were made, i.e., to facilitate the interpretability of the black-box ML model, we propose a novel explanation approach that highlight regions in the input spectrogram that contributed the most for the prediction task at hand (e.g., fault type or location) - or visual explanation.
F. Nazary—Authors are listed in alphabetical order.
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References
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Acknowledgments
This work has been partially funded by e-distribuzione S.p.A company, Italy, through a PhD scholarship granted to Fatemeh Nazary.
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Ardito, C., Deldjoo, Y., Di Sciascio, E., Nazary, F., Sapienza, G. (2021). ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids. In: Ardito, C., et al. Human-Computer Interaction – INTERACT 2021. INTERACT 2021. Lecture Notes in Computer Science(), vol 12936. Springer, Cham. https://doi.org/10.1007/978-3-030-85607-6_20
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DOI: https://doi.org/10.1007/978-3-030-85607-6_20
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