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

Autor:	Daniela Renga, Daniele Apiletti, Marco Mellia, Danilo Giordano, Matteo Nisi, Elena Baralis, Yang Zhang, Tao Huang
Rok vydání:	2020
Předmět:	Service (systems architecture) Smart grid Predictive maintenance Fault diagnosis Medium Voltage distribution networks Data mining Associative classification Association rule learning Computer science Associative classification Smart grid 02 engineering and technology computer.software_genre Predictive maintenance Theoretical Computer Science SCADA 0202 electrical engineering electronic engineering information engineering Data mining Fault diagnosis Medium Voltage distribution networks Numerical Analysis 020206 networking & telecommunications Electric distribution network Computer Science Applications Computational Mathematics Subject-matter expert Workflow Computational Theory and Mathematics 020201 artificial intelligence & image processing computer Software
Zdroj:	Computing. 102:1199-1211
ISSN:	1436-5057 0010-485X
DOI:	10.1007/s00607-019-00781-w
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::39d08e99f02a292b6c8784b2ea3681c3 https://doi.org/10.1007/s00607-019-00781-w Zobrazit plný text záznamu Plný text ve formátu PDF Plný text ve formátu HTML
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