A Comparison of Machine Learning Methods for Frequency Nadir Estimation in Power Systems

Xuebo Liu, Jian Xie, Xin Fang, Haoyu Yuan, Bin Wang, Hongyu Wu, Jin Tan

Research output: Contribution to conferencePaperpeer-review

7 Scopus Citations


An increasing penetration level of inverter-based renewable energy resources changes the inertia of power systems, posing challenges for maintaining the desired system frequency stability. An accurate frequency nadir estimation is crucial for power system operators to prepare preventive actions against large frequency excursions. In this paper, five machine learning methods - linear regression, gradient boosting, support vector regression, an artificial neural network, and XGBoost - are applied to two different datasets, i.e., 1) the unit generation dataset and 2) the system total inertia and headroom dataset, for the prediction of the frequency nadir. The training and testing datasets are generated through extensive generation scheduling simulations using Multi-timescale Integrated Dynamic and Scheduling (MI-DAS) toolbox on the Western Electricity Coordinating Council 240-bus system with high renewable penetration levels. Numerical results show that all five machine learning methods perform well in predicting the nadir frequency of the system. Among them, the gradient boosting and the XGBoost are clear winners yielding the best prediction accuracy in terms of four evaluation metrics.

Original languageAmerican English
Number of pages5
StatePublished - 2022
Event3rd IEEE Kansas Power and Energy Conference, KPEC 2022 - Manhattan, United States
Duration: 25 Apr 202226 Apr 2022


Conference3rd IEEE Kansas Power and Energy Conference, KPEC 2022
Country/TerritoryUnited States

Bibliographical note

See NREL/CP-6A40-82116 for preprint

NREL Publication Number

  • NREL/CP-6A40-83783


  • data driven
  • frequency nadir estimation
  • machine learning
  • power system stability


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