Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis

RENGA, DANIELA; APILETTI, DANIELE; GIORDANO, DANILO; Matteo, Nisi; HUANG, TAO; ZHANG, YANG; MELLIA, MARCO; BARALIS, ELENA MARIA

doi:10.1007/s00607-019-00781-w

PORTO @ Archivio Istituzionale della Ricerca

IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.

Data-driven models are becoming of fundamental importance in electric distribution networks to enable predictive maintenance, to perform effective diagnosis and to reduce related expenditures, with the final goal of improving the electric service efficiency and reliability to the benefit of both the citizens and the grid operators themselves. This paper considers a dataset collected over 6 years in a real-world medium-voltage distribution network by the Supervisory Control And Data Acquisition (SCADA) system. A transparent, exploratory, and exhaustive data-mining workflow, based on data characterisation, time-windowing, association rule mining, and associative classification is proposed and experimentally evaluated to automatically identify correlations and build a prognostic–diagnostic model from the SCADA events occurring before and after specific service interruptions, i.e., network faults. Our results, evaluated by both data-driven quality metrics and domain expert interpretations, highlight the capability to assess the limited predictive capability of the SCADA events for medium-voltage distribution networks, while their effective exploitation for diagnostic purposes is promising.

Titolo:	Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis
Autori:	RENGA, DANIELA APILETTI, DANIELE GIORDANO, DANILO Matteo Nisi HUANG, TAO ZHANG, YANG MELLIA, MARCO BARALIS, ELENA MARIA mostra contributor esterni nascondi contributor esterni
Data di pubblicazione:	2020
Rivista:	COMPUTING
Digital Object Identifier (DOI):	http://dx.doi.org/10.1007/s00607-019-00781-w
Appare nelle tipologie:	1.1 Articolo in rivista

File in questo prodotto:

File	Descrizione	Tipologia	Licenza
Enel___Extension (1).pdf	Versione finale	2. Post-print / Author's Accepted Manuscript	PUBBLICO - Tutti i diritti riservati	Embargo: 11/01/2021 Richiedi una copia
Renga2020_Article_Data-drivenExploratoryModelsOf.pdf		2a Post-print versione editoriale / Version of Record	Non Pubblico - Accesso privato/ristretto	Administrator Richiedi una copia

Utilizza questo identificativo per citare o creare un link a questo documento:
http://hdl.handle.net/11583/2779892

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.