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.

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Unscheduled power disturbances cause severe consequences for customers and grid operators. To avoid such events, it is important to identify the causes and localize the sources of the disturbances in the power distribution network. In this work, we focus on a specific power grid in the Arctic region of Northern Norway that experiences an increased frequency of failures of unspecified origin. First, we built a data set by collecting relevant meteorological data and power consumption measurements logged by power-quality meters. Then, we exploited machine-learning techniques to detect disturbances in the power supply and to identify the most significant variables that should be monitored. Specifically, we framed the problem of detecting faults as a supervised classification and used both linear and non-linear classifiers. Linear models achieved the highest classification performances and were able to predict the failures reported with a weighted F1-score of 0.79. The linear models identified the amount of flicker and wind speed of gust as the most significant variables in explaining the power disturbances. Our results could provide valuable information to the distribution system operator for implementing strategies to prevent and mitigate incoming failures.

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