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

arXiv: https://arxiv.org/abs/2207.11803 · HTML: https://mlsanity.com/paper/2207.11803/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 26 references that are in the map.
- First posted on arXiv 24 Jul 2022.

## Builds on (most cited first, 2 of 2)

- [A Survey on State Estimation Techniques and Challenges in Smart Distribution Systems](https://mlsanity.com/paper/1809.00057/) (2018)
- [Linear Single- and Three-Phase Voltage Forecasting and Bayesian State Estimation with Limited Sensing](https://mlsanity.com/paper/1806.06024/) (2018)

## Abstract (arXiv)

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.