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

• URL: http://arxiv.org/abs/2207.11803v1
• Date: Sun, 24 Jul 2022 20:24:27 GMT
• Title: Data-driven Models to Anticipate Critical Voltage Events in Power Systems
• Authors: Fabrizio De Caro, Adam J. Collin, Alfredo Vaccaro (University of Sannio)
• Abstract summary: This paper explores the effectiveness of data-driven models to predict voltage excursion events in power systems using simple categorical labels. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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