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Volume 13 | Issue 7 | Year 2026 | Article Id. IJECE-V13I7P118 | DOI : https://doi.org/10.14445/23488549/IJECE-V13I7P118

Robust Ensemble Framework for Fault Detection in Transmission Lines Using Hybrid Classifiers and Deep Q-Networks

Sandeep Godhade, Jayendra Kumar

Received Revised Accepted Published
16 May 2026 11 Jun 2026 30 Jun 2026 29 Jul 2026

Citation :

Sandeep Godhade, Jayendra Kumar, "Robust Ensemble Framework for Fault Detection in Transmission Lines Using Hybrid Classifiers and Deep Q-Networks," International Journal of Electronics and Communication Engineering, vol. 13, no. 7, pp. 257-274, 2026. Crossref, https://doi.org/10.14445/23488549/IJECE-V13I7P118

Abstract

The continuous complexities and the requirement of reliable power transmission have led to the necessity for advanced fault detection systems in order to ensure stability and safety in electrical grids. In this study, an ensemble learning-based technique has been proposed for fault detection in power transmission lines by using Deep Q-Networks (DQN) in conjunction with standard classifiers such as Naive Bayes, Multilayer Perceptron (MLP), Logistic Regression, and Deep Forest. The proposed algorithm uses reinforcement learning for optimizing classifier weights. Voltage and current waveforms are used as input data for extracting features and performing classification. The performance of the proposed technique is highly improved, providing 99.10% accuracy, 99.40% precision, 99.70% sensitivity, and 99.50% F1-score, which is far better than the existing techniques. Moreover, the proposed framework provides reduced processing time (12-13 milliseconds) and low memory consumption (150-152 megabytes).

Keywords

Deep Q-Networks, Ensemble Learning, Fault Detection, Reinforcement Learning, Transmission Lines.

References

  1. Nikhil Kumar Sharma et al., “VMD-Enabled Current-Based Fast Fault Detection Scheme for DC Microgrid,” IEEE Systems Journal, vol. 16, no. 1, pp. 933-944, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. G. K. Rao, and P. Jena, “A Novel Fault Identification and Localization Scheme for Bipolar DC Microgrid,” IEEE Transactions on Industrial Informatics, vol. 19, no. 12, pp. 11752-11764, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  3. Mina Mola et al., “Distributed Fast Fault Detection in DC Microgrids,” IEEE Systems Journal, vol. 16, no. 1, pp. 440-451, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  4. Navid Vafamand et al., “Dual-EKF-Based Fault-Tolerant Predictive Control of Nonlinear DC Microgrids with Actuator and Sensor Faults,” IEEE Transactions on Industry Applications, vol. 58, no. 4, pp. 5438-5446, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  5. Satyavarta Kumar Prince, Shaik Affijulla, and Gayadhar Panda, “Protection of DC Microgrids Based on Complex Power during Faults in On/Off-Grid Scenarios,” IEEE Transactions on Industry Applications, vol. 59, no. 1, pp. 244-254, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  6. Saad Alzahrani, Khalil Sinjari, and Joydeep Mitra, “Multi-Agent and State Observer-Based Technique for Microgrid Protection,” IEEE Transactions on Industry Applications, vol. 60, no. 2, pp. 2697-2705, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  7. Meysam Yadegar et al., “A Distributed High-Impedance Fault Detection and Protection Scheme in DC Microgrids,” IEEE Transactions on Power Delivery, vol. 39, no. 1, pp. 141-154, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  8. Bappa Roy et al., “Deep Learning Based Relay for Online Fault Detection, Classification, and Fault Location in a Grid-Connected Microgrid,” IEEE Access, vol. 11, pp. 62674-62696, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  9. Neelesh Yadav, and Narsa Reddy Tummuru, “Application of Filter Capacitor Dynamics Based Short-Circuit Fault Detection Method in Ring-Type DC Microgrid,” IEEE Transactions on Industrial Electronics, vol. 70, no. 5, pp. 5286-5295, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  10. Hua Han et al., “A Fast Fault Diagnosis Scheme for Ring Bus DC Microgrids with Fewer Sensors,” IEEE Transactions on Power Delivery, vol. 39, no. 1, pp. 283-295, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  11. Sajeeb Saha et al., “Sensor Fault-Resilient Control of Electronically Coupled Distributed Energy Resources in Islanded Microgrids,” IEEE Transactions on Industry Applications, vol. 58, no. 1, pp. 914-929, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  12. Kuppili Anirudh, Himanshu J. Bahirat, and Kishore Chatterjee, “Current-Limiting-Type Fault Ride-Through Control Scheme for Converters in DC Microgrid,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 12, no. 2, pp. 1523-1534, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  13. Leony Ortiz-Matos, Luís B. Gutiérrez Zea, and Jorge W. González-Sanchez, “A Methodology of Sensor Fault-Tolerant Control on a Hierarchical Control for Hybrid Microgrids,” IEEE Access, vol. 11, pp. 58078-58098, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  14. Saurabh Sharma, and Manoj Tripathy, “Differential Reactor Voltage Based Fault Detection and Classification for Smart DC Microgrid,” IEEE Transactions on Industrial Informatics, vol. 19, no. 12, pp. 11730-11741, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  15. Sunil Kumar Maurya et al., “ΔV−ΔI Plane-Based Line Faults Detection and Classification in DC Microgrid,” IEEE Transactions on Power Delivery, vol. 38, no. 3, pp. 1755-1767, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  16. Ting Wang et al., “A Comprehensive Fault Detection and Isolation Method for DC Microgrids Using Reduced-Order Unknown Input Observers,” IEEE Transactions on Power Delivery, vol. 39, no. 1, pp. 479-495, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  17. Gade Kesava Rao, and Premalata Jena, “Unit Protection of Tapped Line DC Microgrid,” IEEE Journal of Emerging and Selected Topics in Power Electronics, vol. 10, no. 4, pp. 4680-4689, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  18. Marija Čuljak, Hrvoje Pandžić, and Juraj Havelka, “Mathematical Morphology-Based Fault Detection in Radial DC Microgrids Considering Fault Current From VSC,” IEEE Transactions on Smart Grid, vol. 14, no. 4, pp. 2545-2557, 2023.
    [CrossRef] [Google Scholar] [Publisher Link]
  19. Bhatraj Anudeep, and Paresh Kumar Nayak, “Differential Power Based Selective Phase Tripping for Fault-resilient Microgrid,” Journal of Modern Power Systems and Clean Energy, vol. 10, no. 2, pp. 459-470, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  20. Neelesh Yadav, and Narsa Reddy Tummuru, “Short-Circuit Fault Detection and Isolation Using Filter Capacitor Current Signature in Low-Voltage DC Microgrid Applications,” IEEE Transactions on Industrial Electronics, vol. 69, no. 8, pp. 8491-8500, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  21. Ting Wang et al., “Synthetic Data in DC Microgrids: Label Creation for Ensemble Learning for Fault Isolation,” IEEE Transactions on Power Delivery, vol. 37, no. 3, pp. 2301-2313, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  22. Xiu Yao, Vu Le, and Inhwan Lee, “Unknown Input Observer-Based Series DC Arc Fault Detection in DC Microgrids,” IEEE Transactions on Power Electronics, vol. 37, no. 4, pp. 4708-4718, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  23. Mohammad Raeispour et al., “Fault-Tolerant, Distributed Control for Emerging, VSC-Based, Islanded Microgrids—An Approach Based on Simultaneous Passive Fault Detection,” IEEE Access, vol. 10, pp. 10995-11010, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  24. Jorge de la Cruz et al., “Review of Networked Microgrid Protection: Architectures, Challenges, Solutions, and Future Trends,” CSEE Journal of Power and Energy Systems, vol. 10, no. 2, pp. 448-467, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  25. Muiz M. Zaben et al., “Machine Learning Methods for Fault Diagnosis in AC Microgrids: A Systematic Review,” IEEE Access, vol. 12, pp. 20260-20298, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  26. Shahriar Rahman Fahim et al., “Uncertainty Awareness in Transmission Line Fault Analysis: A Deep Learning based Approach,” Applied Soft Computing, vol. 128, 2022.
    [CrossRef] [Google Scholar] [Publisher Link]
  27. Nguyen Quoc Minh, Nguyen Trong Khiem, and Vu Hoai Giang, “Fault Classification and Localization in Power Transmission Line based on Machine Learning and Combined CNN-LSTM Models,” Energy Reports, vol. 12, pp. 5610-5622, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  28. Pravati Nayak et al., “2D-Convolutional Neural Network based Fault Detection and Classification of Transmission Lines using Scalogram Images,” Heliyon, vol. 10, no. 19, pp. 1-18, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  29. Dong-Hee Yoon, and Jonghee Yoon, “Development of a Real-Time Fault Detection Method for Electric Power System Via Transformer-based Deep Learning Model,” International Journal of Electrical Power and Energy Systems, vol. 159, pp. 1-9, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  30. Simon Bin Akter et al., “Ensemble Learning based Transmission Line Fault Classification using Phasor Measurement Unit (PMU) Data with Explainable AI (XAI),” Plos one, vol. 19, no. 2, pp. 1-31, 2024.
    [CrossRef] [Google Scholar] [Publisher Link]
  31. Kasem Khalil et al., “Machine Learning-Based Approach for Hardware Faults Prediction,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 67, no. 11, pp. 3880-3892, 2020.
    [CrossRef] [Google Scholar] [Publisher Link]
  32. Ying He, Nan Zhao, and Hongxi Yin, “Integrated Networking, Caching, and Computing for Connected Vehicles: A Deep Reinforcement Learning Approach,” IEEE Transactions on Vehicular Technology, vol. 67, no. 1, pp. 44-55, 2018.
    [CrossRef] [Google Scholar] [Publisher Link]
  33. Mevludin Glavic, “(Deep) Reinforcement Learning for Electric Power System Control and Related Problems: A Short Review and Perspectives,” Annual Reviews in Control, vol. 48, pp. 22-35, 2019.
    [CrossRef] [Google Scholar] [Publisher Link]