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arXiv · 2207.11803

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

Fabrizio De Caro·Adam J. Collin·Alfredo Vaccaro
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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.

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Fabrizio De Caro, Adam J. Collin, Alfredo Vaccaro. 2022-07-24. Data-driven Models to Anticipate Critical Voltage Events in Power Systems. https://doi.org/10.1007/s40866-022-00159-6

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