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PreprintCC BY 4.0Zenodo

Artificial Intelligence for Fault Detection and Diagnosis in Power Systems: Methods, Evaluation Challenges, and Deployment Considerations

  • Shraddha Mane
  • Prathamesh Patil
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Abstract

This review examines artificial intelligence methods for fault detection and diagnosis in electrical power systems. It discusses conventional machine learning, deep learning, signal-processing and hybrid approaches, evaluation metrics, robustness, interpretability, cybersecurity, and deployment challenges. The manuscript highlights the importance of transparent datasets, realistic validation, and safe integration with conventional protection systems. This work is a literature-based review and does not report new experimental results.

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Cite

BibTeXRIS
BibTeX
@misc{mane2026artificial,
  title = {{Artificial Intelligence for Fault Detection and Diagnosis in Power Systems: Methods, Evaluation Challenges, and Deployment Considerations}},
  author = {Shraddha Mane and Prathamesh Patil},
  howpublished = {Zenodo},
  year = {2026},
  month = oct,
  doi = {10.5281/zenodo.23537833},
  url = {https://zenodo.org/records/23537833},
  note = {CC BY 4.0},
}
RIS
TY  - UNPB
TI  - Artificial Intelligence for Fault Detection and Diagnosis in Power Systems: Methods, Evaluation Challenges, and Deployment Considerations
AU  - Shraddha Mane
AU  - Prathamesh Patil
PY  - 2026
DA  - 2026/10/21
PB  - Zenodo
DO  - 10.5281/zenodo.23537833
UR  - https://zenodo.org/records/23537833
AB  - This review examines artificial intelligence methods for fault detection and diagnosis in electrical power systems. It discusses conventional machine learning, deep learning, signal-processing and hybrid approaches, evaluation metrics, robustness, interpretability, cybersecurity, and deployment challenges. The manuscript highlights the importance of transparent datasets, realistic validation, and safe integration with conventional protection systems. This work is a literature-based review and does not report new experimental results.
ER  -