ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids - Human-Computer-Interaction – INTERACT 2021 Access content directly
Conference Papers Year : 2021

ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids

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.
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hal-04291274 , version 1 (17-11-2023)

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Carmelo Ardito, Yashar Deldjoo, Eugenio Di Sciascio, Fatemeh Nazary, Gianluca Sapienza. ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids. 18th IFIP Conference on Human-Computer Interaction (INTERACT), Aug 2021, Bari, Italy. pp.270-274, ⟨10.1007/978-3-030-85607-6_20⟩. ⟨hal-04291274⟩
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