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.; Sapienza, G.. - 12936 LNCS:(2021), pp. 270-274. [10.1007/978-3-030-85607-6_20]
ISCADA: Towards a Framework for Interpretable Fault Prediction in Smart Electrical Grids
Ardito C.;Deldjoo Y.;Di Sciascio E.;Nazary F.;
2021-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
