Data-driven Models to Anticipate Critical Voltage Events in Power Systems

TL;DR

This paper evaluates data-driven classification models for predicting voltage excursion events in power systems, demonstrating their effectiveness with a case study on an Italian sub-transmission network with wind power.

Contribution

It introduces a low-complexity, data-driven classification approach for voltage event prediction and validates it on real power system data, highlighting model strengths and weaknesses.

Findings

Effective prediction of voltage events using simple categorical labels

Low computational and data requirements for the proposed models

Validation on real-world Italian power network data

Abstract

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.