  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 North Norway. First, we
collect data pertaining to the grid topology, the topography of the area, the
historical meteorological data, and the historical energy
consumption/production data. Then, we exploit statistical and machine-learning
techniques to predict the occurrence of failures.
  We interpret the variables that mostly explain the classification results to
be the main driving factors of power interruption. We are able to predict 57%
(F1-score 0.53) of all failures reported over a period of 1 year with a
weighted support-vector machine model. Wind speed and local industry activity
are found to be the main controlling parameters where the location of exposed
power lines is a likely trigger. In summary, we discuss causing factors for
failures in the power grid and enable the distribution system operators to
implement strategies to prevent and mitigate incoming failures.
