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

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Abstract

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.

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Acknowledgements

This work has been partially funded by Enel Italia, e-distribuzione, and the SmartData@PoliTO center for Data Science technologies and applications. We are grateful to Paolo Garza and Giuseppe Attanasio for their help in exploiting the L3 associative classifier.

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Authors and Affiliations

  1. The Politenico di Torino, Turin, Italy

    Daniela Renga, Daniele Apiletti, Danilo Giordano, Matteo Nisi, Tao Huang, Yang Zhang, Marco Mellia & Elena Baralis

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  1. Daniela Renga
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  2. Daniele Apiletti
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  3. Danilo Giordano
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  4. Matteo Nisi
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  5. Tao Huang
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  6. Yang Zhang
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  7. Marco Mellia
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  8. Elena Baralis
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Correspondence to Daniela Renga.

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Renga, D., Apiletti, D., Giordano, D. et al. Data-driven exploratory models of an electric distribution network for fault prediction and diagnosis. Computing 102, 1199–1211 (2020). https://doi.org/10.1007/s00607-019-00781-w

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