Explainable Bayesian Neural Network for Probabilistic Transient Stability Analysis Considering Wind Energy

Bendong Tan, Junbo Zhao, Tong Su, Qiuhua Huang, Yingchen Zhang, Hongming Zhang

Research output: Contribution to conferencePaper


While several data-driven models have been developed for transient stability assessment, how to consider the uncertainties from load and renewable generations and provide interpretation of data-driven assessment results are still open. This paper proposes an explainable Bayesian Neural Network (BNN) for probabilistic transient stability assessment (TSA). By extracting the uncertainties from loads and wind farms, the BNN model can make a reliable prediction and quantify the prediction uncertainties. We also develop the Gradient Shap algorithm to make the global and local explanations for the probabilistic TSA model, a significant advantage over existing black-box data-driven methods. Numerical results on the modified IEEE 39-bus system show that the proposed method outperforms the existing methods in terms of prediction accuracy and uncertainty quantification capabilities. The explainability of the proposed method allows system operators to design preventive controls for enhancing system stability.
Original languageAmerican English
Number of pages5
StatePublished - 2022
Event2022 IEEE PES General Meeting - Denver, CO
Duration: 17 Jul 202222 Jul 2022


Conference2022 IEEE PES General Meeting
CityDenver, CO

NREL Publication Number

  • NREL/CP-5D00-84991


  • Bayesian Neural Network
  • Gradient Shap
  • model interpretability
  • probabilistic transient stability assessment


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