Data-driven Models to Anticipate Critical Voltage Events in Power Systems
This addresses voltage stability issues for power grid operators, but it is incremental as it applies existing classification methods to a specific domain.
The paper tackles the problem of predicting voltage excursion events in power systems by using data-driven models as categorical classification, achieving a low computational and data burden. A case study on a real Italian 150 kV network with wind power demonstrates the general validity of the approach and compares several prediction models.
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.