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Article

Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case

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Department of Physics and Technology, UiT the Arctic University of Norway, 9037 Tromso, Norway
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Department of Mathematics and Statistics, UiT the Arctic University of Norway, 9037 Tromso, Norway
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NORCE, The Norwegian Research Centre AS, 5008 Bergen, Norway
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Distribution System Operator Arva, 9024 Tromso, Norway
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Laboratory for Energy and NanoScience (LENS), Masdar Institute Campus, Khalifa University of Science and Technology, Abu Dhabi 127788, United Arab Emirates
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Author to whom correspondence should be addressed.
Academic Editor: Dino Musmarra
Energies 2022, 15(1), 305; https://doi.org/10.3390/en15010305
Received: 25 November 2021 / Revised: 20 December 2021 / Accepted: 29 December 2021 / Published: 3 January 2022
(This article belongs to the Section F: Electrical Engineering)
Electric failures are a problem for customers and grid operators. Identifying causes and localizing the source of failures in the grid is critical. Here, we focus on a specific power grid in the Arctic region of Northern Norway. First, we collected data pertaining to the grid topology, the topography of the area, the historical meteorological data, and the historical energy consumption/production data. Then, we exploited statistical and machine-learning techniques to predict the occurrence of failures. The classification models achieve good performance, meaning that there is a significant relationship between the collected variables and fault occurrence. Thus, we interpreted the variables that mostly explain the classification results to be the main driving factors of power interruption. Wind speed of gust and local industry activity are found to be the main controlling parameters in explaining the power failure occurrences. The result could provide important information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures. View Full-Text
Keywords: energy analytics; machine learning; anomaly detection; power interruptions; unbalanced classification energy analytics; machine learning; anomaly detection; power interruptions; unbalanced classification
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MDPI and ACS Style

Eikeland, O.F.; Maria Bianchi, F.; Holmstrand, I.S.; Bakkejord, S.; Santos, S.; Chiesa, M. Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case. Energies 2022, 15, 305. https://doi.org/10.3390/en15010305

AMA Style

Eikeland OF, Maria Bianchi F, Holmstrand IS, Bakkejord S, Santos S, Chiesa M. Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case. Energies. 2022; 15(1):305. https://doi.org/10.3390/en15010305

Chicago/Turabian Style

Eikeland, Odin Foldvik, Filippo Maria Bianchi, Inga Setså Holmstrand, Sigurd Bakkejord, Sergio Santos, and Matteo Chiesa. 2022. "Uncovering Contributing Factors to Interruptions in the Power Grid: An Arctic Case" Energies 15, no. 1: 305. https://doi.org/10.3390/en15010305

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