Data-driven Models to Anticipate Critical Voltage Events in Power Systems
Artificial Intelligence
2023-08-25 v1 Signal Processing
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.
Keywords
Cite
@article{arxiv.2207.11803,
title = {Data-driven Models to Anticipate Critical Voltage Events in Power Systems},
author = {Fabrizio De Caro and Adam J. Collin and Alfredo Vaccaro},
journal= {arXiv preprint arXiv:2207.11803},
year = {2023}
}
Comments
In proceedings of the 11th Bulk Power Systems Dynamics and Control Symposium (IREP 2022), July 25-30, 2022, Banff, Canada