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

6 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

Bibliographical note

KAUST Repository Item: Exported on 2022-07-01
Acknowledgements: This work was funded in part by the New York State Energy Research and Development Authority (NYSERDA) through the Electric Power Transmission and Distribution (EPTD) High Performing Grid Program under agreement number 137951, in part by the Engineering Research Center Program of the National Science Foundation and the Department of Energy under Award EEC-1041877, in part by the CURENT Industry Partnership Program, and in part by the Center of Excellence for NEOM Research at King Abdullah University of Science and Technology.
This publication acknowledges KAUST support, but has no KAUST affiliated authors.


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