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Hybrid CNN-decision tree framework for efficient transmission line fault detection and classification: an XAI-based approach
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  • Published: 10 April 2026

Hybrid CNN-decision tree framework for efficient transmission line fault detection and classification: an XAI-based approach

  • Anish Kumar Biswas1,
  • Md. Faysal Ahamed1,
  • Fariya Bintay Shafi1,
  • M. Murugappan2,3,
  • Muhammad E. H. Chowdhury4,
  • Rajeswaran Nagalingam5,7 &
  • …
  • T. Samraj Lawrence6 

Scientific Reports (2026) Cite this article

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Subjects

  • Energy science and technology
  • Engineering
  • Mathematics and computing

Abstract

Accurate, fast, and interpretable fault identification on electrical transmission lines is essential for maintaining power system stability and reducing outage durations. In this study, we propose a hybrid 1D convolutional neural network–Decision Tree (1D-CNN–DT) for transmission line fault detection and classification, in which the 1D-CNN acts solely as a feature extractor. During this process, the Decision Tree performs the final, interpretable classification. By preserving decision transparency and achieving high diagnostic accuracy, the proposed architecture differs from conventional end-to-end deep learning models. In MATLAB/Simulink, four distinct transmission line scenarios were simulated to evaluate the framework under realistic operating conditions. These scenarios included short lines, long distributed lines, source-end faults, and load-end faults. We developed a large, balanced dataset of three-phase voltage and current measurements per unit, covering standard operation and ten types of faults. According to the proposed model, fault detection accuracies were 99.89%, 99.94%, 99.94%, and 99.97%, and fault classification accuracies were 99.93%, 99.58%, 99.44%, and 99.86% across the four transmission line configurations. In addition to its high accuracy, the hybrid framework demonstrated significantly lower computational complexity and shorter training and inference times than conventional ANN- and LSTM-based approaches, without requiring manual signal transformations. The SHapley Additive Explanations (SHAP) are integrated to enhance trust and practical usability, providing both global and instance-level interpretability that reveals how voltages and currents contribute to individual faults. According to the results, a hybrid architecture that combines deep learning and explainable AI offers reliable, efficient, and transparent real-time transmission line monitoring and protection.

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Data availability

Data is publicly available at: https://doi.org/10.6084/m9.figshare.30615710.v1

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Acknowledgement

The manuscript was refined with the support of AI-based tools, including ChatGPT, Gemini, and Claude. The authors reviewedand edited the content as needed and take full responsibility for the content of the publication.

Funding

This research received no specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

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Authors and Affiliations

  1. Department of Electrical & Computer Engineering, Rajshahi University of Engineering & Technology, Rajshahi, 6204, Bangladesh

    Anish Kumar Biswas, Md. Faysal Ahamed & Fariya Bintay Shafi

  2. Intelligent Signal Processing (ISP) Research Lab, Department of Electronics and Communication Engineering, Kuwait College of Science and Technology, Block 4, 13133, Doha, Kuwait

    M. Murugappan

  3. Department of Electronics and Communication Engineering, Vels Institute of Sciences, Technology, and Advanced Studies, Chennai, Tamilnadu, India

    M. Murugappan

  4. Department of Electrical Engineering, Qatar University, Doha, Qatar

    Muhammad E. H. Chowdhury

  5. School of Computer Applications, IMS Unison University, Dehradun, Uttarakhand, India

    Rajeswaran Nagalingam

  6. Department of IT, College of Engineering and Technology, Dambi Dollo University, Dambi Dollo, Oromia Region, Ethiopia

    T. Samraj Lawrence

  7. School of Engineering Technology & AI, MNR University, Sangareddy, Telangana, India

    Rajeswaran Nagalingam

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Contributions

Anish Kumar Biswas: Conceptualization, Methodology, Investigation, Software, Writing—Original manuscript Md. Faysal Ahamed: Methodology, Formal analysis, Write- Original manuscript Fariya Binte Shafi: Software, Data curation, Methodology M Murugappan: Conceptualization, Methodology, Investigation, Formal analysis, Validation, Review & Write—Manuscript Muhammad E. H. Chowdhury: Methodology, Supervision, Resources, Writing—Original manuscript Rajeswaran Nagalingam: Visualization and Validation T.Samraj Lawrence: Validation, Visualization, Investigation, Review & Write—Manuscript.

Corresponding authors

Correspondence to M. Murugappan, Muhammad E. H. Chowdhury or T. Samraj Lawrence.

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Biswas, A.K., Ahamed, M.F., Shafi, F.B. et al. Hybrid CNN-decision tree framework for efficient transmission line fault detection and classification: an XAI-based approach. Sci Rep (2026). https://doi.org/10.1038/s41598-026-45514-0

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  • Received: 08 November 2025

  • Accepted: 19 March 2026

  • Published: 10 April 2026

  • DOI: https://doi.org/10.1038/s41598-026-45514-0

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Keywords

  • Transmission line
  • Convolutional neural network
  • Fault detection
  • MATLAB simulink
  • Explainable AI(XAI)
  • SHapley additive exPlanations (SHAP)
  • Fault classification
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