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.

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

Ardito C.;Deldjoo Y.;Di Sciascio E.;Nazary F.;
2021

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.
978-3-030-85606-9
978-3-030-85607-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/243900
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