Synthetic training data generation for ML-based small-signal stability assessment

Sergio A. Dorado-Rojas, Marcelo De Castro Fernandes, Luigi Vanfretti

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations


This article presents a simulation-based massive data generation procedure with applications in training machine learning (ML) solutions to automatically assess the small-signal stability condition of a power system subjected to contingencies. This method of scenario generation for employs a Monte Carlo two-stage sampling procedure to set up a contingency condition while considering the likelihood of a given combination of line outages. The generated data is pre-processed and then used to train several ML models (logistic and softmax regression, support vector machines, k-nearest Neighbors, Naïve Bayes and decision trees), and a deep learning neural network. The performance of the ML algorithms shows the potential to be deployed in efficient real-time solutions to assist power system operators.
Original languageEnglish (US)
Title of host publication2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm)
ISBN (Print)9781728161273
StatePublished - Dec 30 2020
Externally publishedYes


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