Synthetic Training Data Generation for ML-based Small-Signal Stability Assessment

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

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@inproceedings{dorado-rojas2020b,
  title = {Synthetic {{Training Data Generation}} for {{ML-based Small-Signal Stability Assessment}}},
  booktitle = {2020 {{IEEE International Conference}} on {{Communications}}, {{Control}}, and {{Computing Technologies}} for {{Smart Grids}} ({{SmartGridComm}})},
  author = {{Dorado-Rojas}, Sergio A. and {de Castro Fernandes}, Marcelo and Vanfretti, Luigi},
  year = 2020,
  month = nov,
  doi = {10.1109/SmartGridComm47815.2020.9302991},
}

Abstract

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.

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