Research Article
Explainable Artificial Intelligence for Real-Time Stability Assessment in Smart Power Systems
@ARTICLE{10.4108/ew.13678, author={Zhanjie Liu and Xiaonan Gao and Jiaqi Yuan and Yixin Sun}, title={Explainable Artificial Intelligence for Real-Time Stability Assessment in Smart Power Systems}, journal={EAI Endorsed Transactions on Energy Web}, volume={13}, number={1}, publisher={EAI}, journal_a={EW}, year={2026}, month={10}, keywords={Smart Grid Stability, Graph Transformer, Temporal Fusion Transformer, Explainable AI, SHapley Additive exPLanations, Real-Time Prediction}, doi={10.4108/ew.13678} }- Zhanjie Liu
Xiaonan Gao
Jiaqi Yuan
Yixin Sun
Year: 2026
Explainable Artificial Intelligence for Real-Time Stability Assessment in Smart Power Systems
EW
EAI
DOI: 10.4108/ew.13678
Abstract
INTRODUCTION: Smart grid stability analysis is necessary for reliable power system operations in the presence of increased renewable energy penetration and distributed energy resources. OBJECTIVES: This study proposes a novel Graph Transformer–Temporal Fusion Transformer (GT-TFT) with an Explainable Artificial Intelligence (XAI) framework for real-time smart grid stability prediction. METHODS: A feature-correlation graph is constructed and processed using a Graph Transformer (GT) to capture spatial dependencies, while a Temporal Fusion Transformer (TFT) learns temporal dynamics from smart grid measurements. The extracted features are fused through a feature fusion layer and classified using a neural network classifier. SHapley Additive exPlanations (SHAP) is employed to improve model interpretability. RESULTS: Experimental evaluation on the Smart Grid Stability dataset demonstrates superior performance, achieving an accuracy of 0.9879, precision of 0.9886, recall of 0.9926, F1-score of 0.9905, and AUC of 0.9999. Comparative analysis confirms that the proposed framework outperforms existing baseline models. CONCLUSION: The suggested GT-TF-XAI framework offers a solution to real-time smart grid stability assessment that is accurate, reliable, and explainable to enable effective decision-making in current smart power systems.
Copyright © 2026 Zhanije Liu et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.