  This paper explores the effectiveness of data-driven models to predict
voltage excursion events in power systems using simple categorical labels. By
treating the prediction as a categorical classification task, the workflow is
characterized by a low computational and data burden. A proof-of-concept case
study on a real portion of the Italian 150 kV sub-transmission network, which
hosts a significant amount of wind power generation, demonstrates the general
validity of the proposal and offers insight into the strengths and weaknesses
of several widely utilized prediction models for this application.
