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

• Published in: Energies
• Main Authors: Odin Foldvik Eikeland, Filippo Maria Bianchi, Inga Setså Holmstrand, Sigurd Bakkejord, Sergio Santos, Matteo Chiesa
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2022
• Subjects: energy analytics; machine learning; anomaly detection; power interruptions; unbalanced classification; Technology; T; Arctic; Norway; Northern Norway
• Online Access: https://doi.org/10.3390/en15010305; https://doaj.org/article/1a422542aaa84b4393b6d9055fc86b22

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.