Sammendrag
Power supply disruptions, including short-time disturbances,
can lead to large direct and indirect financial losses.
The ability to predict the risk of these disturbances allows for
preventive actions and increases the reliability of the supply. This
paper investigates the impact of using seasonal data of combined
common weather conditions on the power quality prediction in
distribution grids. Our main contribution consists of weatherbased
predictive models for three types of events that frequently
occur in these grids, as well as an analysis of the influence
of two training approaches: with either seasonal or all-year
data, on their performance. All developed models score higher
than arbitrary guessing; in several instances the improvement
is considerable. It is demonstrated that in some cases the
models improve when the training data is limited to a subset
corresponding to a particular meteorological season. Examining
variable importance values and distributions of the models’ data,
it is shown that this situation takes place particularly when
weather conditions correlated with the occurrence of power grid
events vary across seasons
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