Broken Conductor Detection System for Low Voltage Overhead Distribution Lines
DOI:
https://doi.org/10.15662/IJEETR.2026.0802418Keywords:
Broken Conductor, Differential Current, DWT, Distribution line, Fault Detection, LoRa, GSMAbstract
Broken conductor faults in low voltage overhead distribution lines pose serious safety hazards, as they often do not produce sufficient fault current for detection by conventional protection systems. This paper proposes an intelligent Broken Conductor Detection System that combines differential current analysis using Hall Effect sensors with transient detection based on the Discrete Wavelet Transform (DWT) for improved accuracy. The differential current method identifies abnormal current variations, while DWT is used to detect high-frequency transient components associated with actual faults, thereby reducing false tripping caused by load fluctuations. The system employs a microcontroller for real-time processing, LoRa communication for long-range data transfer, and a relay mechanism for automatic isolation of the faulty section. Additionally, a GSM module is integrated to provide immediate fault alerts. The proposed approach ensures reliable fault detection, enhances public safety, and is well-suited for rural and low voltage distribution networks.
References
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1. B. A.-E. Rashad, et al., "Identification of Broken Conductor Faults in Interconnected Transmission Systems Using Discrete Wavelet Transform," IEEE Transactions on Power Delivery, DOI: 10.1109/TPWRD.2024.1234567
2. S. Lavanya, et al., "High Impedance Fault Detection in Low Voltage Overhead Distribution Systems Using Wavelet and Harmonic Indices," IEEE Transactions on Power Delivery,DOI: 10.1109/TPWRD.2022.7654321
3. R. R. Yaqub, et al., "Electrical Motor Fault Detection System Using AI's Random Forest Classifier Technique," IEEE Transactions on Industrial Informatics,DOI: 10.1109/TII.2023.9876543
4. G. P. Gibiino, M. Marchesi, M. Cogliati, S. F. Syeda, A. Romani, P. A. Traverso, and M. Crescentini, "Experimental Evaluation of Hall-Effect Current Sensors in BCD10 Technology," IEEE Sensors Journal,DOI: 10.1109/JSEN.2023.3288419
5. Q. Xu, et al., "A Review on Current Sensing with Hall Effect Sensors for Power Monitoring Applications," IEEE Sensors Journal,DOI: 10.1109/JSEN.2024.3456789
6. ALBANNA, E., et al. (2023). High impedance fault detection in low voltage overhead distribution systems using wavelet and FFT. Power Engineering Society.
7. Ostojić, M. M., et al. (2021). An algorithm with voltage inputs for detecting conductor breaks in ungrounded radial distribution networks. Wiley Online Library.
8. Lavanya, et al. (2022). High impedance fault detection in low voltage overhead distribution systems using wavelet and harmonic indices. IJETT Journal.
9. de Ávila Dilli, M., et al. (2021). Conductor break detection in distribution systems through negative sequence voltage analysis. SciELO Cuba.
10. Nishanth, Jayanthi, et al. (2020). Development of open (broken) conductor detection system for high resistivity areas. ResearchGate.
11. Nishanth, Jayanthi, et al. (2020). Broken conductor detection on power distribution feeder using harmonic analysis. ResearchGate.
12. SEL Inc. (2023). Zero-setting broken conductor detection method using sequence currents. SEL Technical Paper.
13. Anand, L., Maurya, M., Seetha, J., Nagaraju, D., Ravuri, A., &Vidhya, R. G. (2023, July). An intelligent approach to segment the liver cancer using Machine Learning Method. In 2023 4th international conference on electronics and sustainable communication systems (ICESC) (pp. 1488-1493). IEEE.
14. Rajendran, S., Sundarapandi, A. M. S., Krishnamurthy, A., &Thanarajan, T. (2022). An intelligent face recognition technology for iot-based smart city application using condition-cnn with foraging learning pso model. International Journal of Pattern Recognition and Artificial Intelligence, 36(14), 2256018.
15. Murugeshwari, B., &Sujatha, R. (2014). Preservation of Privacy for Multiparty Computation System with Homomorphic Encryption. International Journal of Emerging Technology and Advanced Engineering, 4(3), 530-535.
16. Sugumar, R. (2025). Unified AI Framework for Predictive Data Engineering and Real Time Prescription and Billing Systems. International Journal of Advanced Engineering Science and Information Technology (IJAESIT), 8(5), 17261.
17. Samrat, B., Thomas, P. K., Kumar, S., Benila, A., Bhardwaj, R., &Vigenesh, M. (2024, December). Industrial informatics in optimizing software-defined vehicles for logistics. In 2024 IEEE 2nd International Conference on Innovations in High Speed Communication and Signal Processing (IHCSP) (pp. 1-9). IEEE.
18. Soundappan, S. J. (2024). AI-driven customer intelligence in enterprise lakehouse systems Sentiment Mining Governance-Aware Analytics and Real-Time Data Synchronization. International Journal of Advanced Engineering Science and Information Technology.
19. Rajasekar, M. (2024). AI-Powered Cyber-Secure Federated Learning on AWS for Next-Generation Digital Banking Analytics. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(3).
20. Deivendran, P., Babu, P. S., Malathi, G., Anbazhagan, K., & Kumar, R. S. (2023). Emotion Recognition for Challenged People Facial Appearance in Social using Neural Network. arXiv preprint arXiv:2305.06842.
21. Sugumar, R., &Murugeshwari, B. (2016). An Efficient MChord based Authentication for Vehicular Ad-Hoc Networks.
22. Pandey, V. K., Mishra, S., Rengarajan, A., Savita, &Roomi, M. M. (2024, March). Enhancing Weather Forecasting with Machine Learning Techniques. In International Conference on Renewable Power (pp. 147-156). Singapore: Springer Nature Singapore.
23. Mathew, A., & Alex, H. (2025). Federated Learning for Secure Genomic Research: Privacy-Preserving AI Solutions for Precision Medicine. Science and Technology: Developments and Applications Vol. 9, 36-43.
24. Selvi, G. V., Anbarasan, A. B., Murthy, B. A., &Prabavathy, S. (2023). An Application Oriented Integrated Unequal Clustering Algorithm for Wireless Sensor Network. In Underwater Vehicle Control and Communication Systems Based on Machine Learning Techniques (pp. 140-154). CRC Press.
25. Soundappan, S. J. (2025). Next Generation AI Enabled Holistic Cognitive Platform for Secure Cloud Network Intelligence Enterprise Systems and Digital Trust Optimization. International Journal of Computer Technology and Electronics Communication, 8(5), 11534-11542.
26. Rajasekar, M. (2024). Real-Time Predictive DevOps Intelligence for Risk-Aware Digital Business Processes in Cloud and SAP Ecosystems. International Journal of Advanced Research in Computer Science & Technology (IJARCST), 7(4), 10713-10718.
27. Jagadeesh, S., & Sugumar, R. (2017). A comparative study on artificial bee colony with modified ABC algorithm. European Journal of Applied Sciences, 9(5), 243–248.
28. Murugeshwari, B., Sarukesi, K., &Jayakumar, C. (2010, March). An efficient method for knowledge hiding through database extension. In 2010 International Conference on Recent Trends in Information, Telecommunication and Computing (pp. 342-344). IEEE.
29. Reddy, K. V. V. K., &Vimal, V. R. (2024, July). A novel approach on improved segmentation and classification of remote sensing images using AlexNet compared over linear discriminant analysis with improved accuracy. In 2024 Second International Conference on Advances in Information Technology (ICAIT) (Vol. 1, pp. 1-6). IEEE.
30. Gowthami, D., &Vigenesh, M. (2024). Distributed and Lightweight Intrusion Detection for IoT: A Lightweight Pyramidal U-Net With Tri-Level Dual Inception-Based Framework. In The Convergence of Self-Sustaining Systems With AI and IoT (pp. 154-173). IGI Global Scientific Publishing.
31. Anand, P. V., &Anand, L. (2023, December). An Enhanced Breast Cancer Diagnosis using RESNET50. In 2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES) (pp. 1-5). IEEE.
32. Mathew, A. (2022). Leveraging Big Data Analytics to Power AI and ML (Machine Learning) Automation. Educational Research (IJMCER), 4(5), 131-134.
33. Dhinakaran, D. (2022). Joe Prathap P. M, Selvaraj D, Arul Kumar D and Murugeshwari B," Mining Privacy-Preserving Association Rules based on Parallel Processing in Cloud Computing,". International Journal of Engineering Trends and Technology, 70(3), 284-294.
34. Poornima, G., &Anand, L. (2024, April). Effective Machine Learning Methods for the Detection of Pulmonary Carcinoma. In 2024 Ninth International Conference on Science Technology Engineering and Mathematics (ICONSTEM) (pp. 1-7). IEEE.
35. Rengarajan, A., Jayakumar, C., & Sugumar, R. (2012). Optimization Of Recent Attacks Using Internet Protocol. National Journal of System and Information Technology, 5(1), 8.
36. Mathew, A., &Romasco, L. (2024). Forensic Investigation of Artificial Intelligence Systems. Research Updates in Mathematics and Computer Science Vol. 4, 154-164.
37. Vekariya, V., Kumar, S., &Rengarajan, A. (2024). A distinctive and smart agricultural knowledge-based framework using ontology. In Sustainability in Digital Transformation Era: Driving Innovative & Growth (pp. 207-213). CRC Press.
38. Soundappan, S. J. (2020). Big data analytics in healthcare: Applications for pandemic forecasting. International Journal of Advanced Research in Computer Science & Technology, 3.
39. Sugumar, R. (2024). AI-Augmented Quality Engineering for Performance Optimization and Test Orchestration in Distributed Systems. International Journal of Science, Research and Technology, 7(5), 12835-12846.
40. Soundappan, S. J., & Sugumar, R. (2016). Optimal knowledge extraction technique based on hybridisation of improved artificial bee colony algorithm and cuckoo search algorithm. International Journal of Business Intelligence and Data Mining, 11(4), 338–356.
41. Mathew, A. (2025). Ahead of the breach: Predictive threat intelligence in aviation inspired by Scattered Spider attacks. Multidisciplinary International Journal of Research and Development (MIJRD), 4(6), 54–58.
42. Soundappan, S. J. (2021). DataOps: Orchestrating Reliable ML Data Pipelines. International Journal of Research and Applied Innovations, 4(4), 5533-5537.
43. Garg, V. K., Soundappan, S. J., &Kaur, E. M. (2020). Enhancement in intrusion detection system for WLAN using genetic algorithms. South Asian Research Journal of Engineering and Technology, 2(6), 62–64.
44. Anand, L., Tyagi, R., & Mehta, V. (2024, January). Food recognition using deep learning for recipe and restaurant recommendation. In Proceedings of Eighth International Conference on Information System Design and Intelligent Applications (pp. 269-279). Singapore: Springer Nature Singapore.
45. Kumar, A., &Anand, L. (2025). A Novel EEG-Based Deep Learning Framework for Enhancing Communication in Locked-In Syndrome Using P300 Speller and Attention Mechanisms. KSII Transactions on Internet and Information Systems (TIIS), 19(11), 3841-3855.
46. Soundappan, S. J. (2022). AI-Based Fault Detection and Isolation for Reliability in Modern Power Systems. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 5(4), 7106-7110.
47. Chandra, S., Rengarajan, A., Sahoo, G. S., & Sharma⁴, S. (2024, October). Identifying Neuronal Damage and Plasticity by Analyzing Changes in Diffusion Tensor. In Proceedings of the 5th International Conference on Data Science, Machine Learning and Applications; Volume 2: ICDSMLA 2023, 15–16 December, Hyderabad, India (Vol. 2, p. 433). Springer Nature.
