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Peer-reviewedCC BYScientific Reports

Hybrid deep learning–driven explainable AI framework for fault detection and classification in smart power grids

  • Udit Mamodiya1
  • Divyanshu Sinha2
  • Indra Kishor3
  • Malik Jawarneh4
  • Mais Haj Qasem5
  • Afnan Almegren6
6 affiliations
  1. 1Faculty of Engineering and Technology, Poornima University
  2. 2Amrita School of Artificial Intelligence, Amrita Vishwavidyapeetham
  3. 3Department of CSE, Poornima Institute of Engineering and Technology
  4. 4Department of Computer Science, Amman Arab University
  5. 5Department of Data Science and Artificial Intelligence, Amman Arab University
  6. 6Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University
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Abstract

The stable detection of faults in smart power grids is essential when focusing on the stable operation and the reduction of the downtime. In this paper, the author suggests a hybrid deep learning-based model that combines convolutional neural networks (CNN) and long short-term memory (LSTM) with explainable artificial intelligence (XAI) to detect and classify faults accurately and interpretably. The model aims at capturing the spatial and time-varying attributes of multivariate electrical signatures such as voltage, current, and frequency changes. A combination of real time sensor measurements and simulated fault conditions are used which includes several fault classes including LG, LL, LLG, and three phase faults. Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 97.84%, with an F1-score of 97.08%, outperforming conventional CNN and LSTM models by more than 3%. The framework is also able to work in a noisy environment with a stable performance of less than 2% performance decrease, and inference latency of 18 ms, which can be deployed in real-time. Besides, the combination of SHAP and attention processes improves the interpretability through the detection of the crucial features that lead to fault prediction. The findings suggest that the suggested solution is a powerful, scalable, and clear solution when it comes to intelligent fault management in a contemporary smart grid.

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Cite

BibTeXRIS
BibTeX
@article{mamodiya2026hybrid,
  title = {{Hybrid deep learning–driven explainable AI framework for fault detection and classification in smart power grids}},
  author = {Udit Mamodiya and Divyanshu Sinha and Indra Kishor and Malik Jawarneh and Mais Haj Qasem and Afnan Almegren},
  journal = {Scientific Reports},
  year = {2026},
  month = jul,
  doi = {10.1038/s41598-026-62771-1},
  url = {https://doi.org/10.1038/s41598-026-62771-1},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - Hybrid deep learning–driven explainable AI framework for fault detection and classification in smart power grids
AU  - Udit Mamodiya
AU  - Divyanshu Sinha
AU  - Indra Kishor
AU  - Malik Jawarneh
AU  - Mais Haj Qasem
AU  - Afnan Almegren
PY  - 2026
DA  - 2026/07/01
JO  - Scientific Reports
DO  - 10.1038/s41598-026-62771-1
UR  - https://doi.org/10.1038/s41598-026-62771-1
AB  - The stable detection of faults in smart power grids is essential when focusing on the stable operation and the reduction of the downtime. In this paper, the author suggests a hybrid deep learning-based model that combines convolutional neural networks (CNN) and long short-term memory (LSTM) with explainable artificial intelligence (XAI) to detect and classify faults accurately and interpretably. The model aims at capturing the spatial and time-varying attributes of multivariate electrical signatures such as voltage, current, and frequency changes. A combination of real time sensor measurements and simulated fault conditions are used which includes several fault classes including LG, LL, LLG, and three phase faults. Experimental evaluation demonstrates that the proposed model achieves a classification accuracy of 97.84%, with an F1-score of 97.08%, outperforming conventional CNN and LSTM models by more than 3%. The framework is also able to work in a noisy environment with a stable performance of less than 2% performance decrease, and inference latency of 18 ms, which can be deployed in real-time. Besides, the combination of SHAP and attention processes improves the interpretability through the detection of the crucial features that lead to fault prediction. The findings suggest that the suggested solution is a powerful, scalable, and clear solution when it comes to intelligent fault management in a contemporary smart grid.
SN  - 2045-2322
ER  -