Deep Learning Based Frequency Stability Assessment in Power Grid with High Renewables

Yinfeng Zhao, Shutang You, Mirka Mandich, Hongyu Li, Summer Fabus, Yi Cui, Huangqing Xiao, Lin Zhu, Chujie Zeng, Yilu Liu, Huaiguang Jiang, Haoyu Yuan, Yingchen Zhang, Jin Tan

Research output: Contribution to conferencePaperpeer-review

1 Scopus Citations


Frequency stability assessment is one critical aspect of power system security assessment. Traditional N-1 screening method is based on the simulations of a few typical daily and seasonal operation scenarios. However, the increasing integration of inverter-based renewables and the retirement of conventional synchronous generators result in decreasing system inertia and growing complexity of system operating conditions. Selecting a few typical operation scenarios cannot cover all operating conditions, and the time-domain simulation of all operation conditions requires tremendous time. This paper proposes a more efficient frequency stability assessment method based on deep learning. The affinity propagation clustering algorithm is used to divide the dataset into different clusters, so the selected dataset for training can cover the diversified operating conditions as much as possible. Also, feature normalization is applied to both the training dataset and testing dataset in order to remove any unnecessary bias. Especially, trained model based on full dataset normalization has bounded error in the prediction. The case study on the reduced 240-bus WECC system demonstrates that the proposed method can predict accurate frequency nadir with limited training dataset. The deep learning model using the revised feature normalization can predict more accurate frequency nadir than that using the traditional feature normalization and has very small maximum prediction error.

Original languageAmerican English
Number of pages5
StatePublished - 2022
Event2022 IEEE Power and Energy Society General Meeting, PESGM 2022 - Denver, United States
Duration: 17 Jul 202221 Jul 2022


Conference2022 IEEE Power and Energy Society General Meeting, PESGM 2022
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2022 IEEE.

NREL Publication Number

  • NREL/CP-5D00-80040


  • Affinity propagation
  • deep learning
  • feature normalization
  • frequency nadir
  • frequency stability
  • neural network


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