  Unscheduled power disturbances cause severe consequences both for customers
and grid operators. To defend against such events, it is necessary to identify
the causes of interruptions in the power distribution network. In this work, we
focus on the power grid of a Norwegian community in the Arctic that experiences
several faults whose sources are unknown. First, we construct a data set
consisting of relevant meteorological data and information about the current
power quality logged by power-quality meters. Then, we adopt machine-learning
techniques to predict the occurrence of faults. Experimental results show that
both linear and non-linear classifiers achieve good classification performance.
This indicates that the considered power-quality and weather variables explain
well the power disturbances. Interpreting the decision process of the
classifiers provides valuable insights to understand the main causes of
disturbances. Traditional features selection methods can only indicate which
are the variables that, on average, mostly explain the fault occurrences in the
dataset. Besides providing such a global interpretation, it is also important
to identify the specific set of variables that explain each individual fault.
To address this challenge, we adopt a recent technique to interpret the
decision process of a deep learning model, called Integrated Gradients. The
proposed approach allows to gain detailed insights on the occurrence of a
specific fault, which are valuable for the distribution system operators to
implement strategies to prevent and mitigate power disturbances.

Machine Learning