Data-Driven Security Assessment of Power Grids Based on Machine Learning Approach: Preprint

Jin Tan, Haoyu Yuan, Yingchen Zhang, H. Xiao, S. Fabus, Y. Su, S. You, Y. Zhao, H. Li, C. Zhang, Y. Liu

Research output: Contribution to conferencePaper


Data-driven security assessment provides key indicators on power system stability using simulations on scheduling models, as opposed to dynamic simulations that are more time-consuming. This paper investigates data-driven security assessment of power grids based on machine learning. Multivariate random forest regression is used as the machine learning algorithm due to its high robustness to the input data. Three stability issues are analyzed using the proposed machine learning tool, including transient stability, frequency stability and small signal stability. The estimation values from machine learning tool are compared with those from dynamic simulations. Results show that the proposed machine learning tool can effectively predict the stability margins for the three stability metrics.
Original languageAmerican English
Number of pages11
StatePublished - 2020
Event2019 CIGRE Grid of the Future Symposium - Atlanta, Georgia
Duration: 3 Nov 20196 Nov 2019


Conference2019 CIGRE Grid of the Future Symposium
CityAtlanta, Georgia

NREL Publication Number

  • NREL/CP-5D00-74256


  • data-driven security assessment
  • frequency stability
  • machine learning
  • multivariate random forest
  • small-signal stability
  • transient stability


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