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An Interpretable Deep Learning Method for Power System Transient Stability Assessment via Tree Regularization.
- Source :
- IEEE Transactions on Power Systems; Sep2022, Vol. 37 Issue 5, p3359-3369, 11p
- Publication Year :
- 2022
-
Abstract
- Deep learning (DL) techniques have shown promising performance for designing data-driven power system transient stability assessment (TSA) models. However, due to the deep structure of the DL, the resulting model is always a black-box and hard to explain, which hinders its practical adoption by the industry. This paper proposes an interpretable DL-based TSA model to balance the TSA accuracy and transparency. The proposed method combines the strong nonlinear modelling capability of a deep neural network and the interpretability of a decision tree (DT). Through regularizing DL-based model with the average DT path length in the training process, the proposed interpretable DL-based TSA method can visually explain the TSA decision-making process. Simulation results have shown that the proposed method can deliver highly accurate TSA results and interpretable TSA decision-making rules, which can be used for designing preventive control actions. [ABSTRACT FROM AUTHOR]
- Subjects :
- ELECTRIC transients
ARTIFICIAL neural networks
DECISION trees
DEEP learning
Subjects
Details
- Language :
- English
- ISSN :
- 08858950
- Volume :
- 37
- Issue :
- 5
- Database :
- Complementary Index
- Journal :
- IEEE Transactions on Power Systems
- Publication Type :
- Academic Journal
- Accession number :
- 158649778
- Full Text :
- https://doi.org/10.1109/TPWRS.2021.3133611